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Autonomy in the interactive music system VIVO | Interactive Music Systems (IMS) have introduced a new world of music-making
modalities. But can we really say that they create music, as in true autonomous
creation? Here we discuss Video Interactive VST Orchestra (VIVO), an IMS that
considers extra-musical information by adopting a simple salience based model
of user-system interaction when simulating intentionality in automatic music
generation. Key features of the theoretical framework, a brief overview of
pilot research, and a case study providing validation of the model are
presented. This research demonstrates that a meaningful user/system interplay
is established in what we define as reflexive multidominance.
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Information and estimation in Fokker-Planck channels | We study the relationship between information- and estimation-theoretic
quantities in time-evolving systems. We focus on the Fokker-Planck channel
defined by a general stochastic differential equation, and show that the time
derivatives of entropy, KL divergence, and mutual information are characterized
by estimation-theoretic quantities involving an appropriate generalization of
the Fisher information. Our results vastly extend De Bruijn's identity and the
classical I-MMSE relation.
| 1 | 0 | 1 | 1 | 0 | 0 |
The role of industry, occupation, and location specific knowledge in the survival of new firms | How do regions acquire the knowledge they need to diversify their economic
activities? How does the migration of workers among firms and industries
contribute to the diffusion of that knowledge? Here we measure the industry,
occupation, and location-specific knowledge carried by workers from one
establishment to the next using a dataset summarizing the individual work
history for an entire country. We study pioneer firms--firms operating in an
industry that was not present in a region--because the success of pioneers is
the basic unit of regional economic diversification. We find that the growth
and survival of pioneers increase significantly when their first hires are
workers with experience in a related industry, and with work experience in the
same location, but not with past experience in a related occupation. We compare
these results with new firms that are not pioneers and find that
industry-specific knowledge is significantly more important for pioneer than
non-pioneer firms. To address endogeneity we use Bartik instruments, which
leverage national fluctuations in the demand for an activity as shocks for
local labor supply. The instrumental variable estimates support the finding
that industry-related knowledge is a predictor of the survival and growth of
pioneer firms. These findings expand our understanding of the micro-mechanisms
underlying regional economic diversification events.
| 0 | 0 | 0 | 0 | 0 | 1 |
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning | We analyze the problem of learning a single user's preferences in an active
learning setting, sequentially and adaptively querying the user over a finite
time horizon. Learning is conducted via choice-based queries, where the user
selects her preferred option among a small subset of offered alternatives.
These queries have been shown to be a robust and efficient way to learn an
individual's preferences. We take a parametric approach and model the user's
preferences through a linear classifier, using a Bayesian prior to encode our
current knowledge of this classifier. The rate at which we learn depends on the
alternatives offered at every time epoch. Under certain noise assumptions, we
show that the Bayes-optimal policy for maximally reducing entropy of the
posterior distribution of this linear classifier is a greedy policy, and that
this policy achieves a linear lower bound when alternatives can be constructed
from the continuum. Further, we analyze a different metric called
misclassification error, proving that the performance of the optimal policy
that minimizes misclassification error is bounded below by a linear function of
differential entropy. Lastly, we numerically compare the greedy entropy
reduction policy with a knowledge gradient policy under a number of scenarios,
examining their performance under both differential entropy and
misclassification error.
| 1 | 0 | 0 | 1 | 0 | 0 |
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes | It is widely observed that deep learning models with learned parameters
generalize well, even with much more model parameters than the number of
training samples. We systematically investigate the underlying reasons why deep
neural networks often generalize well, and reveal the difference between the
minima (with the same training error) that generalize well and those they
don't. We show that it is the characteristics the landscape of the loss
function that explains the good generalization capability. For the landscape of
loss function for deep networks, the volume of basin of attraction of good
minima dominates over that of poor minima, which guarantees optimization
methods with random initialization to converge to good minima. We theoretically
justify our findings through analyzing 2-layer neural networks; and show that
the low-complexity solutions have a small norm of Hessian matrix with respect
to model parameters. For deeper networks, extensive numerical evidence helps to
support our arguments.
| 1 | 0 | 0 | 1 | 0 | 0 |
Benchmarking Decoupled Neural Interfaces with Synthetic Gradients | Artifical Neural Networks are a particular class of learning systems modeled
after biological neural functions with an interesting penchant for Hebbian
learning, that is "neurons that wire together, fire together". However, unlike
their natural counterparts, artificial neural networks have a close and
stringent coupling between the modules of neurons in the network. This coupling
or locking imposes upon the network a strict and inflexible structure that
prevent layers in the network from updating their weights until a full
feed-forward and backward pass has occurred. Such a constraint though may have
sufficed for a while, is now no longer feasible in the era of very-large-scale
machine learning, coupled with the increased desire for parallelization of the
learning process across multiple computing infrastructures. To solve this
problem, synthetic gradients (SG) with decoupled neural interfaces (DNI) are
introduced as a viable alternative to the backpropagation algorithm. This paper
performs a speed benchmark to compare the speed and accuracy capabilities of
SG-DNI as opposed to a standard neural interface using multilayer perceptron
MLP. SG-DNI shows good promise, in that it not only captures the learning
problem, it is also over 3-fold faster due to it asynchronous learning
capabilities.
| 1 | 0 | 0 | 1 | 0 | 0 |
Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers | Macquarie University's contribution to the BioASQ challenge (Task 5b Phase B)
focused on the use of query-based extractive summarisation techniques for the
generation of the ideal answers. Four runs were submitted, with approaches
ranging from a trivial system that selected the first $n$ snippets, to the use
of deep learning approaches under a regression framework. Our experiments and
the ROUGE results of the five test batches of BioASQ indicate surprisingly good
results for the trivial approach. Overall, most of our runs on the first three
test batches achieved the best ROUGE-SU4 results in the challenge.
| 1 | 0 | 0 | 0 | 0 | 0 |
Network Topology Modulation for Energy and Data Transmission in Internet of Magneto-Inductive Things | Internet-of-things (IoT) architectures connecting a massive number of
heterogeneous devices need energy efficient, low hardware complexity, low cost,
simple and secure mechanisms to realize communication among devices. One of the
emerging schemes is to realize simultaneous wireless information and power
transfer (SWIPT) in an energy harvesting network. Radio frequency (RF)
solutions require special hardware and modulation methods for RF to direct
current (DC) conversion and optimized operation to achieve SWIPT which are
currently in an immature phase. On the other hand, magneto-inductive (MI)
communication transceivers are intrinsically energy harvesting with potential
for SWIPT in an efficient manner. In this article, novel modulation and
demodulation mechanisms are presented in a combined framework with
multiple-access channel (MAC) communication and wireless power transmission.
The network topology of power transmitting active coils in a transceiver
composed of a grid of coils is changed as a novel method to transmit
information. Practical demodulation schemes are formulated and numerically
simulated for two-user MAC topology of small size coils. The transceivers are
suitable to attach to everyday objects to realize reliable local area network
(LAN) communication performances with tens of meters communication ranges. The
designed scheme is promising for future IoT applications requiring SWIPT with
energy efficient, low cost, low power and low hardware complexity solutions.
| 1 | 0 | 1 | 0 | 0 | 0 |
Core structure of two-dimensional Fermi gas vortices in the BEC-BCS crossover region | We report $T=0$ diffusion Monte Carlo results for the ground-state and vortex
excitation of unpolarized spin-1/2 fermions in a two-dimensional disk. We
investigate how vortex core structure properties behave over the BEC-BCS
crossover. We calculate the vortex excitation energy, density profiles, and
vortex core properties related to the current. We find a density suppression at
the vortex core on the BCS side of the crossover, and a depleted core on the
BEC limit. Size-effect dependencies in the disk geometry were carefully
studied.
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One can hear the Euler characteristic of a simplicial complex | We prove that that the number p of positive eigenvalues of the connection
Laplacian L of a finite abstract simplicial complex G matches the number b of
even dimensional simplices in G and that the number n of negative eigenvalues
matches the number f of odd-dimensional simplices in G. The Euler
characteristic X(G) of G therefore can be spectrally described as X(G)=p-n.
This is in contrast to the more classical Hodge Laplacian H which acts on the
same Hilbert space, where X(G) is not yet known to be accessible from the
spectrum of H. Given an ordering of G coming from a build-up as a CW complex,
every simplex x in G is now associated to a unique eigenvector of L and the
correspondence is computable. The Euler characteristic is now not only the
potential energy summing over all g(x,y) with g=L^{-1} but also agrees with a
logarithmic energy tr(log(i L)) 2/(i pi) of the spectrum of L. We also give
here examples of L-isospectral but non-isomorphic abstract finite simplicial
complexes. One example shows that we can not hear the cohomology of the
complex.
| 1 | 0 | 1 | 0 | 0 | 0 |
A new complete Calabi-Yau metric on $\mathbb{C}^3$ | Motivated by the study of collapsing Calabi-Yau threefolds with a Lefschetz
K3 fibration, we construct a complete Calabi-Yau metric on $\mathbb{C}^3$ with
maximal volume growth, which in the appropriate scale is expected to model the
collapsing metric near the nodal point. This new Calabi-Yau metric has singular
tangent cone at infinity, and its Riemannian geometry has certain non-standard
features near the singularity of the tangent cone $\mathbb{C}^2/\mathbb{Z}_2
\times \mathbb{C}$, which are more typical of adiabatic limit problems. The
proof uses an existence result in H-J. Hein's PhD thesis to perturb an
asymptotic approximate solution into an actual solution, and the main
difficulty lies in correcting the slowly decaying error terms.
| 0 | 0 | 1 | 0 | 0 | 0 |
Weighted density fields as improved probes of modified gravity models | When it comes to searches for extensions to general relativity, large efforts
are being dedicated to accurate predictions for the power spectrum of density
perturbations. While this observable is known to be sensitive to the
gravitational theory, its efficiency as a diagnostic for gravity is
significantly reduced when Solar System constraints are strictly adhered to. We
show that this problem can be overcome by studying weigthed density fields. We
propose a transformation of the density field for which the impact of modified
gravity on the power spectrum can be increased by more than a factor of three.
The signal is not only amplified, but the modified gravity features are shifted
to larger scales which are less affected by baryonic physics. Furthermore, the
overall signal-to-noise increases, which in principle makes identifying
signatures of modified gravity with future galaxy surveys more feasible. While
our analysis is focused on modified gravity, the technique can be applied to
other problems in cosmology, such as the detection of neutrinos, the effects of
baryons or baryon acoustic oscillations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Nonlinear control for an uncertain electromagnetic actuator | This paper presents the design of a nonlinear control law for a typical
electromagnetic actuator system. Electromagnetic actuators are widely
implemented in industrial applications, and especially as linear positioning
system. In this work, we aim at taking into account a magnetic phenomenon that
is usually neglected: flux fringing. This issue is addressed with an uncertain
modeling approach. The proposed control law consists of two steps, a
backstepping control regulates the mechanical part and a sliding mode approach
controls the coil current and the magnetic force implicitly. An illustrative
example shows the effectiveness of the presented approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
Adaptation and Robust Learning of Probabilistic Movement Primitives | Probabilistic representations of movement primitives open important new
possibilities for machine learning in robotics. These representations are able
to capture the variability of the demonstrations from a teacher as a
probability distribution over trajectories, providing a sensible region of
exploration and the ability to adapt to changes in the robot environment.
However, to be able to capture variability and correlations between different
joints, a probabilistic movement primitive requires the estimation of a larger
number of parameters compared to their deterministic counterparts, that focus
on modeling only the mean behavior. In this paper, we make use of prior
distributions over the parameters of a probabilistic movement primitive to make
robust estimates of the parameters with few training instances. In addition, we
introduce general purpose operators to adapt movement primitives in joint and
task space. The proposed training method and adaptation operators are tested in
a coffee preparation and in robot table tennis task. In the coffee preparation
task we evaluate the generalization performance to changes in the location of
the coffee grinder and brewing chamber in a target area, achieving the desired
behavior after only two demonstrations. In the table tennis task we evaluate
the hit and return rates, outperforming previous approaches while using fewer
task specific heuristics.
| 1 | 0 | 0 | 1 | 0 | 0 |
Transverse spinning of light with globally unique handedness | Access to the transverse spin of light has unlocked new regimes in
topological photonics and optomechanics. To achieve the transverse spin of
nonzero longitudinal fields, various platforms that derive transversely
confined waves based on focusing, interference, or evanescent waves have been
suggested. Nonetheless, because of the transverse confinement inherently
accompanying sign reversal of the field derivative, the resulting transverse
spin handedness experiences spatial inversion, which leads to a mismatch
between the densities of the wavefunction and its spin component and hinders
the global observation of the transverse spin. Here, we reveal a globally pure
transverse spin in which the wavefunction density signifies the spin
distribution, by employing inverse molding of the eigenmode in the spin basis.
Starting from the target spin profile, we analytically obtain the potential
landscape and then show that the elliptic-hyperbolic transition around the
epsilon-near-zero permittivity allows for the global conservation of transverse
spin handedness across the topological interface between anisotropic
metamaterials. Extending to the non-Hermitian regime, we also develop
annihilated transverse spin modes to cover the entire Poincare sphere of the
meridional plane. Our results enable the complete transfer of optical energy to
transverse spinning motions and realize the classical analogy of 3-dimensional
quantum spin states.
| 0 | 1 | 0 | 0 | 0 | 0 |
A general class of quasi-independence tests for left-truncated right-censored data | In survival studies, classical inferences for left-truncated data require
quasi-independence, a property that the joint density of truncation time and
failure time is factorizable into their marginal densities in the observable
region. The quasi-independence hypothesis is testable; many authors have
developed tests for left-truncated data with or without right-censoring. In
this paper, we propose a class of test statistics for testing the
quasi-independence which unifies the existing methods and generates new useful
statistics such as conditional Spearman's rank correlation coefficient.
Asymptotic normality of the proposed class of statistics is given. We show that
a new set of tests can be powerful under certain alternatives by theoretical
and empirical power comparison.
| 0 | 0 | 0 | 1 | 0 | 0 |
EPIC 220204960: A Quadruple Star System Containing Two Strongly Interacting Eclipsing Binaries | We present a strongly interacting quadruple system associated with the K2
target EPIC 220204960. The K2 target itself is a Kp = 12.7 magnitude star at
Teff ~ 6100 K which we designate as "B-N" (blue northerly image). The host of
the quadruple system, however, is a Kp = 17 magnitude star with a composite
M-star spectrum, which we designate as "R-S" (red southerly image). With a 3.2"
separation and similar radial velocities and photometric distances, 'B-N' is
likely physically associated with 'R-S', making this a quintuple system, but
that is incidental to our main claim of a strongly interacting quadruple system
in 'R-S'. The two binaries in 'R-S' have orbital periods of 13.27 d and 14.41
d, respectively, and each has an inclination angle of >89 degrees. From our
analysis of radial velocity measurements, and of the photometric lightcurve, we
conclude that all four stars are very similar with masses close to 0.4 Msun.
Both of the binaries exhibit significant ETVs where those of the primary and
secondary eclipses 'diverge' by 0.05 days over the course of the 80-day
observations. Via a systematic set of numerical simulations of quadruple
systems consisting of two interacting binaries, we conclude that the outer
orbital period is very likely to be between 300 and 500 days. If sufficient
time is devoted to RV studies of this faint target, the outer orbit should be
measurable within a year.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the higher Cheeger problem | We develop the notion of higher Cheeger constants for a measurable set
$\Omega \subset \mathbb{R}^N$. By the $k$-th Cheeger constant we mean the value
\[h_k(\Omega) = \inf \max \{h_1(E_1), \dots, h_1(E_k)\},\] where the infimum is
taken over all $k$-tuples of mutually disjoint subsets of $\Omega$, and
$h_1(E_i)$ is the classical Cheeger constant of $E_i$. We prove the existence
of minimizers satisfying additional "adjustment" conditions and study their
properties. A relation between $h_k(\Omega)$ and spectral minimal
$k$-partitions of $\Omega$ associated with the first eigenvalues of the
$p$-Laplacian under homogeneous Dirichlet boundary conditions is stated. The
results are applied to determine the second Cheeger constant of some planar
domains.
| 0 | 0 | 1 | 0 | 0 | 0 |
Petri Nets and Machines of Things That Flow | Petri nets are an established graphical formalism for modeling and analyzing
the behavior of systems. An important consideration of the value of Petri nets
is their use in describing both the syntax and semantics of modeling
formalisms. Describing a modeling notation in terms of a formal technique such
as Petri nets provides a way to minimize ambiguity. Accordingly, it is
imperative to develop a deep and diverse understanding of Petri nets. This
paper is directed toward a new, but preliminary, exploration of the semantics
of such an important tool. Specifically, the concern in this paper is with the
semantics of Petri nets interpreted in a modeling language based on the notion
of machines of things that flow. The semantics of several Petri net diagrams
are analyzed in terms of flow of things. The results point to the viability of
the approach for exploring the underlying assumptions of Petri nets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fundamental Conditions for Low-CP-Rank Tensor Completion | We consider the problem of low canonical polyadic (CP) rank tensor
completion. A completion is a tensor whose entries agree with the observed
entries and its rank matches the given CP rank. We analyze the manifold
structure corresponding to the tensors with the given rank and define a set of
polynomials based on the sampling pattern and CP decomposition. Then, we show
that finite completability of the sampled tensor is equivalent to having a
certain number of algebraically independent polynomials among the defined
polynomials. Our proposed approach results in characterizing the maximum number
of algebraically independent polynomials in terms of a simple geometric
structure of the sampling pattern, and therefore we obtain the deterministic
necessary and sufficient condition on the sampling pattern for finite
completability of the sampled tensor. Moreover, assuming that the entries of
the tensor are sampled independently with probability $p$ and using the
mentioned deterministic analysis, we propose a combinatorial method to derive a
lower bound on the sampling probability $p$, or equivalently, the number of
sampled entries that guarantees finite completability with high probability. We
also show that the existing result for the matrix completion problem can be
used to obtain a loose lower bound on the sampling probability $p$. In
addition, we obtain deterministic and probabilistic conditions for unique
completability. It is seen that the number of samples required for finite or
unique completability obtained by the proposed analysis on the CP manifold is
orders-of-magnitude lower than that is obtained by the existing analysis on the
Grassmannian manifold.
| 1 | 0 | 1 | 1 | 0 | 0 |
Multiplex Network Regression: How do relations drive interactions? | We introduce a statistical method to investigate the impact of dyadic
relations on complex networks generated from repeated interactions. It is based
on generalised hypergeometric ensembles, a class of statistical network
ensembles developed recently. We represent different types of known relations
between system elements by weighted graphs, separated in the different layers
of a multiplex network. With our method we can regress the influence of each
relational layer, the independent variables, on the interaction counts, the
dependent variables. Moreover, we can test the statistical significance of the
relations as explanatory variables for the observed interactions. To
demonstrate the power of our approach and its broad applicability, we will
present examples based on synthetic and empirical data.
| 1 | 1 | 0 | 1 | 0 | 0 |
Hall-Littlewood-PushTASEP and its KPZ limit | We study a new model of interactive particle systems which we call the
randomly activated cascading exclusion process (RACEP). Particles wake up
according to exponential clocks and then take a geometric number of steps. If
another particle is encountered during these steps, the first particle goes to
sleep at that location and the second is activated and proceeds accordingly. We
consider a totally asymmetric version of this model which we refer as
Hall-Littlewood-PushTASEP (HL-PushTASEP) on $\mathbb{Z}_{\geq 0}$ lattice where
particles only move right and where initially particles are distributed
according to Bernoulli product measure on $\mathbb{Z}_{\geq 0}$. We prove
KPZ-class limit theorems for the height function fluctuations. Under a
particular weak scaling, we also prove convergence to the solution of the KPZ
equation.
| 0 | 0 | 1 | 1 | 0 | 0 |
Pricing for Online Resource Allocation: Intervals and Paths | We present pricing mechanisms for several online resource allocation problems
which obtain tight or nearly tight approximations to social welfare. In our
settings, buyers arrive online and purchase bundles of items; buyers' values
for the bundles are drawn from known distributions. This problem is closely
related to the so-called prophet-inequality of Krengel and Sucheston and its
extensions in recent literature. Motivated by applications to cloud economics,
we consider two kinds of buyer preferences. In the first, items correspond to
different units of time at which a resource is available; the items are
arranged in a total order and buyers desire intervals of items. The second
corresponds to bandwidth allocation over a tree network; the items are edges in
the network and buyers desire paths.
Because buyers' preferences have complementarities in the settings we
consider, recent constant-factor approximations via item prices do not apply,
and indeed strong negative results are known. We develop static, anonymous
bundle pricing mechanisms.
For the interval preferences setting, we show that static, anonymous bundle
pricings achieve a sublogarithmic competitive ratio, which is optimal (within
constant factors) over the class of all online allocation algorithms, truthful
or not. For the path preferences setting, we obtain a nearly-tight logarithmic
competitive ratio. Both of these results exhibit an exponential improvement
over item pricings for these settings. Our results extend to settings where the
seller has multiple copies of each item, with the competitive ratio decreasing
linearly with supply. Such a gradual tradeoff between supply and the
competitive ratio for welfare was previously known only for the single item
prophet inequality.
| 1 | 0 | 0 | 0 | 0 | 0 |
Learning best K analogies from data distribution for case-based software effort estimation | Case-Based Reasoning (CBR) has been widely used to generate good software
effort estimates. The predictive performance of CBR is a dataset dependent and
subject to extremely large space of configuration possibilities. Regardless of
the type of adaptation technique, deciding on the optimal number of similar
cases to be used before applying CBR is a key challenge. In this paper we
propose a new technique based on Bisecting k-medoids clustering algorithm to
better understanding the structure of a dataset and discovering the the optimal
cases for each individual project by excluding irrelevant cases. Results
obtained showed that understanding of the data characteristic prior prediction
stage can help in automatically finding the best number of cases for each test
project. Performance figures of the proposed estimation method are better than
those of other regular K-based CBR methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Optimal designs for enzyme inhibition kinetic models | In this paper we present a new method for determining optimal designs for
enzyme inhibition kinetic models, which are used to model the influence of the
concentration of a substrate and an inhibition on the velocity of a reaction.
The approach uses a nonlinear transformation of the vector of predictors such
that the model in the new coordinates is given by an incomplete response
surface model. Although there exist no explicit solutions of the optimal design
problem for incomplete response surface models so far, the corresponding design
problem in the new coordinates is substantially more transparent, such that
explicit or numerical solutions can be determined more easily. The designs for
the original problem can finally be found by an inverse transformation of the
optimal designs determined for the response surface model. We illustrate the
method determining explicit solutions for the $D$-optimal design and for the
optimal design problem for estimating the individual coefficients in a
non-competitive enzyme inhibition kinetic model.
| 0 | 0 | 1 | 1 | 0 | 0 |
Highly Granular Calorimeters: Technologies and Results | The CALICE collaboration is developing highly granular calorimeters for
experiments at a future lepton collider primarily to establish technologies for
particle flow event reconstruction. These technologies also find applications
elsewhere, such as detector upgrades for the LHC. Meanwhile, the large data
sets collected in an extensive series of beam tests have enabled detailed
studies of the properties of hadronic showers in calorimeter systems, resulting
in improved simulation models and development of sophisticated reconstruction
techniques. In this proceeding, highlights are included from studies of the
structure of hadronic showers and results on reconstruction techniques for
imaging calorimetry. In addition, current R&D activities within CALICE are
summarized, focusing on technological prototypes that address challenges from
full detector system integration and production techniques amenable to mass
production for electromagnetic and hadronic calorimeters based on silicon,
scintillator, and gas techniques.
| 0 | 1 | 0 | 0 | 0 | 0 |
Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods | Hamiltonian Monte Carlo has emerged as a standard tool for posterior
computation. In this article, we present an extension that can efficiently
explore target distributions with discontinuous densities, which in turn
enables efficient sampling from ordinal parameters though embedding of
probability mass functions into continuous spaces. We motivate our approach
through a theory of discontinuous Hamiltonian dynamics and develop a numerical
solver of discontinuous dynamics. The proposed numerical solver is the first of
its kind, with a remarkable ability to exactly preserve the Hamiltonian and
thus yield a type of rejection-free proposals. We apply our algorithm to
challenging posterior inference problems to demonstrate its wide applicability
and competitive performance.
| 0 | 0 | 0 | 1 | 0 | 0 |
Optimal boundary gradient estimates for Lamé systems with partially infinite coefficients | In this paper, we derive the pointwise upper bounds and lower bounds on the
gradients of solutions to the Lamé systems with partially infinite
coefficients as the surface of discontinuity of the coefficients of the system
is located very close to the boundary. When the distance tends to zero, the
optimal blow-up rates of the gradients are established for inclusions with
arbitrary shapes and in all dimensions.
| 0 | 0 | 1 | 0 | 0 | 0 |
On a variable step size modification of Hines' method in computational neuroscience | For simulating large networks of neurons Hines proposed a method which uses
extensively the structure of the arising systems of ordinary differential
equations in order to obtain an efficient implementation. The original method
requires constant step sizes and produces the solution on a staggered grid. In
the present paper a one-step modification of this method is introduced and
analyzed with respect to their stability properties. The new method allows for
step size control. Local error estimators are constructed. The method has been
implemented in matlab and tested using simple Hodgkin-Huxley type models.
Comparisons with standard state-of-the-art solvers are provided.
| 0 | 0 | 1 | 0 | 0 | 0 |
Neural Question Answering at BioASQ 5B | This paper describes our submission to the 2017 BioASQ challenge. We
participated in Task B, Phase B which is concerned with biomedical question
answering (QA). We focus on factoid and list question, using an extractive QA
model, that is, we restrict our system to output substrings of the provided
text snippets. At the core of our system, we use FastQA, a state-of-the-art
neural QA system. We extended it with biomedical word embeddings and changed
its answer layer to be able to answer list questions in addition to factoid
questions. We pre-trained the model on a large-scale open-domain QA dataset,
SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our
approach, we achieve state-of-the-art results on factoid questions and
competitive results on list questions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Implementation of Smart Contracts Using Hybrid Architectures with On- and Off-Blockchain Components | Recently, decentralised (on-blockchain) platforms have emerged to complement
centralised (off-blockchain) platforms for the implementation of automated,
digital (smart) contracts. However, neither alternative can individually
satisfy the requirements of a large class of applications. On-blockchain
platforms suffer from scalability, performance, transaction costs and other
limitations. Off-blockchain platforms are afflicted by drawbacks due to their
dependence on single trusted third parties. We argue that in several
application areas, hybrid platforms composed from the integration of on- and
off-blockchain platforms are more able to support smart contracts that deliver
the desired quality of service (QoS). Hybrid architectures are largely
unexplored. To help cover the gap, in this paper we discuss the implementation
of smart contracts on hybrid architectures. As a proof of concept, we show how
a smart contract can be split and executed partially on an off-blockchain
contract compliance checker and partially on the Rinkeby Ethereum network. To
test the solution, we expose it to sequences of contractual operations
generated mechanically by a contract validator tool.
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Electro-mechanical control of an on-chip optical beam splitter containing an embedded quantum emitter | We demonstrate electro-mechanical control of an on-chip GaAs optical beam
splitter containing a quantum dot single-photon source. The beam splitter
consists of two nanobeam waveguides, which form a directional coupler (DC). The
splitting ratio of the DC is controlled by varying the out-of-plane separation
of the two waveguides using electro-mechanical actuation. We reversibly tune
the beam splitter between an initial state, with emission into both output
arms, and a final state with photons emitted into a single output arm. The
device represents a compact and scalable tuning approach for use in III-V
semiconductor integrated quantum optical circuits.
| 0 | 1 | 0 | 0 | 0 | 0 |
Data Distillation for Controlling Specificity in Dialogue Generation | People speak at different levels of specificity in different situations.
Depending on their knowledge, interlocutors, mood, etc.} A conversational agent
should have this ability and know when to be specific and when to be general.
We propose an approach that gives a neural network--based conversational agent
this ability. Our approach involves alternating between \emph{data
distillation} and model training : removing training examples that are closest
to the responses most commonly produced by the model trained from the last
round and then retrain the model on the remaining dataset. Dialogue generation
models trained with different degrees of data distillation manifest different
levels of specificity.
We then train a reinforcement learning system for selecting among this pool
of generation models, to choose the best level of specificity for a given
input. Compared to the original generative model trained without distillation,
the proposed system is capable of generating more interesting and
higher-quality responses, in addition to appropriately adjusting specificity
depending on the context.
Our research constitutes a specific case of a broader approach involving
training multiple subsystems from a single dataset distinguished by differences
in a specific property one wishes to model. We show that from such a set of
subsystems, one can use reinforcement learning to build a system that tailors
its output to different input contexts at test time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Non-locality of the meet levels of the Trotter-Weil Hierarchy | We prove that the meet level $m$ of the Trotter-Weil, $\mathsf{V}_m$ is not
local for all $m \geq 1$, as conjectured in a paper by Kufleitner and Lauser.
In order to show this, we explicitly provide a language whose syntactic
semigroup is in $L \mathsf{V}_m$ and not in $\mathsf{V}_m*\mathsf{D}$.
| 1 | 0 | 1 | 0 | 0 | 0 |
Particle-based and Meshless Methods with Aboria | Aboria is a powerful and flexible C++ library for the implementation of
particle-based numerical methods. The particles in such methods can represent
actual particles (e.g. Molecular Dynamics) or abstract particles used to
discretise a continuous function over a domain (e.g. Radial Basis Functions).
Aboria provides a particle container, compatible with the Standard Template
Library, spatial search data structures, and a Domain Specific Language to
specify non-linear operators on the particle set. This paper gives an overview
of Aboria's design, an example of use, and a performance benchmark.
| 1 | 0 | 0 | 0 | 0 | 0 |
Backlund transformations and divisor doubling | In classical mechanics well-known cryptographic algorithms and protocols can
be very useful for construction canonical transformations preserving form of
Hamiltonians. We consider application of a standard generic divisor doubling
for construction of new auto Bäcklund transformations for the Lagrange top
and Hénon-Heiles system separable in parabolic coordinates.
| 0 | 1 | 1 | 0 | 0 | 0 |
KATE: K-Competitive Autoencoder for Text | Autoencoders have been successful in learning meaningful representations from
image datasets. However, their performance on text datasets has not been widely
studied. Traditional autoencoders tend to learn possibly trivial
representations of text documents due to their confounding properties such as
high-dimensionality, sparsity and power-law word distributions. In this paper,
we propose a novel k-competitive autoencoder, called KATE, for text documents.
Due to the competition between the neurons in the hidden layer, each neuron
becomes specialized in recognizing specific data patterns, and overall the
model can learn meaningful representations of textual data. A comprehensive set
of experiments show that KATE can learn better representations than traditional
autoencoders including denoising, contractive, variational, and k-sparse
autoencoders. Our model also outperforms deep generative models, probabilistic
topic models, and even word representation models (e.g., Word2Vec) in terms of
several downstream tasks such as document classification, regression, and
retrieval.
| 1 | 0 | 0 | 1 | 0 | 0 |
Stochastic evolution equations for large portfolios of stochastic volatility models | We consider a large market model of defaultable assets in which the asset
price processes are modelled as Heston-type stochastic volatility models with
default upon hitting a lower boundary. We assume that both the asset prices and
their volatilities are correlated through systemic Brownian motions. We are
interested in the loss process that arises in this setting and we prove the
existence of a large portfolio limit for the empirical measure process of this
system. This limit evolves as a measure valued process and we show that it will
have a density given in terms of a solution to a stochastic partial
differential equation of filtering type in the two-dimensional half-space, with
a Dirichlet boundary condition. We employ Malliavin calculus to establish the
existence of a regular density for the volatility component, and an
approximation by models of piecewise constant volatilities combined with a
kernel smoothing technique to obtain existence and regularity for the full
two-dimensional filtering problem. We are able to establish good regularity
properties for solutions, however uniqueness remains an open problem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Learning to Address Health Inequality in the United States with a Bayesian Decision Network | Life-expectancy is a complex outcome driven by genetic, socio-demographic,
environmental and geographic factors. Increasing socio-economic and health
disparities in the United States are propagating the longevity-gap, making it a
cause for concern. Earlier studies have probed individual factors but an
integrated picture to reveal quantifiable actions has been missing. There is a
growing concern about a further widening of healthcare inequality caused by
Artificial Intelligence (AI) due to differential access to AI-driven services.
Hence, it is imperative to explore and exploit the potential of AI for
illuminating biases and enabling transparent policy decisions for positive
social and health impact. In this work, we reveal actionable interventions for
decreasing the longevity-gap in the United States by analyzing a County-level
data resource containing healthcare, socio-economic, behavioral, education and
demographic features. We learn an ensemble-averaged structure, draw inferences
using the joint probability distribution and extend it to a Bayesian Decision
Network for identifying policy actions. We draw quantitative estimates for the
impact of diversity, preventive-care quality and stable-families within the
unified framework of our decision network. Finally, we make this analysis and
dashboard available as an interactive web-application for enabling users and
policy-makers to validate our reported findings and to explore the impact of
ones beyond reported in this work.
| 0 | 0 | 0 | 1 | 0 | 0 |
Asynchronous parallel primal-dual block update methods | Recent several years have witnessed the surge of asynchronous (async-)
parallel computing methods due to the extremely big data involved in many
modern applications and also the advancement of multi-core machines and
computer clusters. In optimization, most works about async-parallel methods are
on unconstrained problems or those with block separable constraints.
In this paper, we propose an async-parallel method based on block coordinate
update (BCU) for solving convex problems with nonseparable linear constraint.
Running on a single node, the method becomes a novel randomized primal-dual BCU
with adaptive stepsize for multi-block affinely constrained problems. For these
problems, Gauss-Seidel cyclic primal-dual BCU needs strong convexity to have
convergence. On the contrary, merely assuming convexity, we show that the
objective value sequence generated by the proposed algorithm converges in
probability to the optimal value and also the constraint residual to zero. In
addition, we establish an ergodic $O(1/k)$ convergence result, where $k$ is the
number of iterations. Numerical experiments are performed to demonstrate the
efficiency of the proposed method and significantly better speed-up performance
than its sync-parallel counterpart.
| 0 | 0 | 1 | 1 | 0 | 0 |
Towards Noncommutative Topological Quantum Field Theory: New invariants for 3-manifolds | We define some new invariants for 3-manifolds using the space of taut codim-1
foliations along with various techniques from noncommutative geometry. These
invariants originate from our attempt to generalise Topological Quantum Field
Theories in the Noncommutative geometry / topology realm.
| 0 | 0 | 1 | 0 | 0 | 0 |
Chaotic Dynamic S Boxes Based Substitution Approach for Digital Images | In this paper, we propose an image encryption algorithm based on the chaos,
substitution boxes, nonlinear transformation in Galois field and Latin square.
Initially, the dynamic S boxes are generated using Fisher Yates shuffle method
and piece wise linear chaotic map. The algorithm utilizes advantages of keyed
Latin square and transformation to substitute highly correlated digital images
and yield encrypted image with valued performance. The chaotic behavior is
achieved using Logistic map which is used to select one of thousand S boxes and
also decides the row and column of selected S box. The selected S box value is
transformed using nonlinear transformation. Along with the keyed Latin square
generated using a 256 bit external key, used to substitute secretly plain image
pixels in cipher block chaining mode. To further strengthen the security of
algorithm, round operation are applied to obtain final ciphered image. The
experimental results are performed to evaluate algorithm and the anticipated
algorithm is compared with a recent encryption scheme. The analyses demonstrate
algorithms effectiveness in providing high security to digital media.
| 1 | 0 | 0 | 0 | 0 | 0 |
Randomized Optimal Transport on a Graph: Framework and New Distance Measures | The recently developed bag-of-paths framework consists in setting a
Gibbs-Boltzmann distribution on all feasible paths of a graph. This probability
distribution favors short paths over long ones, with a free parameter (the
temperature $T > 0$) controlling the entropic level of the distribution. This
formalism enables the computation of new distances or dissimilarities,
interpolating between the shortest-path and the resistance distance, which have
been shown to perform well in clustering and classification tasks. In this
work, the bag-of-paths formalism is extended by adding two independent equality
constraints fixing starting and ending nodes distributions of paths. When the
temperature is low, this formalism is shown to be equivalent to a relaxation of
the optimal transport problem on a network where paths carry a flow between two
discrete distributions on nodes. The randomization is achieved by considering
free energy minimization instead of traditional cost minimization. Algorithms
computing the optimal free energy solution are developed for two types of
paths: hitting (or absorbing) paths and non-hitting, regular paths, and require
the inversion of an $n \times n$ matrix with $n$ being the number of nodes.
Interestingly, for regular paths, the resulting optimal policy interpolates
between the deterministic optimal transport policy ($T \rightarrow 0^{+}$) and
the solution to the corresponding electrical circuit ($T \rightarrow \infty$).
Two distance measures between nodes and a dissimilarity between groups of
nodes, both integrating weights on nodes, are derived from this framework.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Generative Model for Natural Sounds Based on Latent Force Modelling | Recent advances in analysis of subband amplitude envelopes of natural sounds
have resulted in convincing synthesis, showing subband amplitudes to be a
crucial component of perception. Probabilistic latent variable analysis is
particularly revealing, but existing approaches don't incorporate prior
knowledge about the physical behaviour of amplitude envelopes, such as
exponential decay and feedback. We use latent force modelling, a probabilistic
learning paradigm that incorporates physical knowledge into Gaussian process
regression, to model correlation across spectral subband envelopes. We augment
the standard latent force model approach by explicitly modelling correlations
over multiple time steps. Incorporating this prior knowledge strengthens the
interpretation of the latent functions as the source that generated the signal.
We examine this interpretation via an experiment which shows that sounds
generated by sampling from our probabilistic model are perceived to be more
realistic than those generated by similar models based on nonnegative matrix
factorisation, even in cases where our model is outperformed from a
reconstruction error perspective.
| 0 | 0 | 0 | 1 | 0 | 0 |
Linear and Nonlinear Heat Equations on a p-Adic Ball | We study the Vladimirov fractional differentiation operator $D^\alpha_N$,
$\alpha >0, N\in \mathbb Z$, on a $p$-adic ball $B_N=\{ x\in \mathbb Q_p:\
|x|_p\le p^N\}$. To its known interpretations via restriction from a similar
operator on $\mathbb Q_p$ and via a certain stochastic process on $B_N$, we add
an interpretation as a pseudo-differential operator in terms of the Pontryagin
duality on the additive group of $B_N$. We investigate the Green function of
$D^\alpha_N$ and a nonlinear equation on $B_N$, an analog the classical porous
medium equation.
| 0 | 0 | 1 | 0 | 0 | 0 |
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations | We propose position-velocity encoders (PVEs) which learn---without
supervision---to encode images to positions and velocities of task-relevant
objects. PVEs encode a single image into a low-dimensional position state and
compute the velocity state from finite differences in position. In contrast to
autoencoders, position-velocity encoders are not trained by image
reconstruction, but by making the position-velocity representation consistent
with priors about interacting with the physical world. We applied PVEs to
several simulated control tasks from pixels and achieved promising preliminary
results.
| 1 | 0 | 0 | 0 | 0 | 0 |
Few-shot Learning by Exploiting Visual Concepts within CNNs | Convolutional neural networks (CNNs) are one of the driving forces for the
advancement of computer vision. Despite their promising performances on many
tasks, CNNs still face major obstacles on the road to achieving ideal machine
intelligence. One is that CNNs are complex and hard to interpret. Another is
that standard CNNs require large amounts of annotated data, which is sometimes
hard to obtain, and it is desirable to learn to recognize objects from few
examples. In this work, we address these limitations of CNNs by developing
novel, flexible, and interpretable models for few-shot learning. Our models are
based on the idea of encoding objects in terms of visual concepts (VCs), which
are interpretable visual cues represented by the feature vectors within CNNs.
We first adapt the learning of VCs to the few-shot setting, and then uncover
two key properties of feature encoding using VCs, which we call category
sensitivity and spatial pattern. Motivated by these properties, we present two
intuitive models for the problem of few-shot learning. Experiments show that
our models achieve competitive performances, while being more flexible and
interpretable than alternative state-of-the-art few-shot learning methods. We
conclude that using VCs helps expose the natural capability of CNNs for
few-shot learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
Noise Statistics Oblivious GARD For Robust Regression With Sparse Outliers | Linear regression models contaminated by Gaussian noise (inlier) and possibly
unbounded sparse outliers are common in many signal processing applications.
Sparse recovery inspired robust regression (SRIRR) techniques are shown to
deliver high quality estimation performance in such regression models.
Unfortunately, most SRIRR techniques assume \textit{a priori} knowledge of
noise statistics like inlier noise variance or outlier statistics like number
of outliers. Both inlier and outlier noise statistics are rarely known
\textit{a priori} and this limits the efficient operation of many SRIRR
algorithms. This article proposes a novel noise statistics oblivious algorithm
called residual ratio thresholding GARD (RRT-GARD) for robust regression in the
presence of sparse outliers. RRT-GARD is developed by modifying the recently
proposed noise statistics dependent greedy algorithm for robust de-noising
(GARD). Both finite sample and asymptotic analytical results indicate that
RRT-GARD performs nearly similar to GARD with \textit{a priori} knowledge of
noise statistics. Numerical simulations in real and synthetic data sets also
point to the highly competitive performance of RRT-GARD.
| 0 | 0 | 0 | 1 | 0 | 0 |
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation | This paper addresses the problem of depth estimation from a single still
image. Inspired by recent works on multi- scale convolutional neural networks
(CNN), we propose a deep model which fuses complementary information derived
from multiple CNN side outputs. Different from previous methods, the
integration is obtained by means of continuous Conditional Random Fields
(CRFs). In particular, we propose two different variations, one based on a
cascade of multiple CRFs, the other on a unified graphical model. By designing
a novel CNN implementation of mean-field updates for continuous CRFs, we show
that both proposed models can be regarded as sequential deep networks and that
training can be performed end-to-end. Through extensive experimental evaluation
we demonstrate the effective- ness of the proposed approach and establish new
state of the art results on publicly available datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the relation between representations and computability | One of the fundamental results in computability is the existence of
well-defined functions that cannot be computed. In this paper we study the
effects of data representation on computability; we show that, while for each
possible way of representing data there exist incomputable functions, the
computability of a specific abstract function is never an absolute property,
but depends on the representation used for the function domain. We examine the
scope of this dependency and provide mathematical criteria to favour some
representations over others. As we shall show, there are strong reasons to
suggest that computational enumerability should be an additional axiom for
computation models. We analyze the link between the techniques and effects of
representation changes and those of oracle machines, showing an important
connection between their hierarchies. Finally, these notions enable us to gain
a new insight on the Church-Turing thesis: its interpretation as the underlying
algebraic structure to which computation is invariant.
| 1 | 0 | 0 | 0 | 0 | 0 |
Towards Proxemic Mobile Collocated Interactions | Research on mobile collocated interactions has been exploring situations
where collocated users engage in collaborative activities using their personal
mobile devices (e.g., smartphones and tablets), thus going from
personal/individual toward shared/multiuser experiences and interactions. The
proliferation of ever-smaller computers that can be worn on our wrists (e.g.,
Apple Watch) and other parts of the body (e.g., Google Glass), have expanded
the possibilities and increased the complexity of interaction in what we term
mobile collocated situations. Research on F-formations (or facing formations)
has been conducted in traditional settings (e.g., home, office, parties) where
the context and the presence of physical elements (e.g., furniture) can
strongly influence the way people socially interact with each other. While we
may be aware of how people arrange themselves spatially and interact with each
other at a dinner table, in a classroom, or at a waiting room in a hospital,
there are other less-structured, dynamic, and larger-scale spaces that present
different types of challenges and opportunities for technology to enrich how
people experience these (semi-) public spaces. In this article, the authors
explore proxemic mobile collocated interactions by looking at F-formations in
the wild. They discuss recent efforts to observe how people socially interact
in dynamic, unstructured, non-traditional settings. The authors also report the
results of exploratory F-formation observations conducted in the wild (i.e.,
tourist attraction).
| 1 | 0 | 0 | 0 | 0 | 0 |
Adjusting for bias introduced by instrumental variable estimation in the Cox Proportional Hazards Model | Instrumental variable (IV) methods are widely used for estimating average
treatment effects in the presence of unmeasured confounders. However, the
capability of existing IV procedures, and most notably the two-stage residual
inclusion (2SRI) procedure recommended for use in nonlinear contexts, to
account for unmeasured confounders in the Cox proportional hazard model is
unclear. We show that instrumenting an endogenous treatment induces an
unmeasured covariate, referred to as an individual frailty in survival analysis
parlance, which if not accounted for leads to bias. We propose a new procedure
that augments 2SRI with an individual frailty and prove that it is consistent
under certain conditions. The finite sample-size behavior is studied across a
broad set of conditions via Monte Carlo simulations. Finally, the proposed
methodology is used to estimate the average effect of carotid endarterectomy
versus carotid artery stenting on the mortality of patients suffering from
carotid artery disease. Results suggest that the 2SRI-frailty estimator
generally reduces the bias of both point and interval estimators compared to
traditional 2SRI.
| 0 | 0 | 0 | 1 | 0 | 0 |
On the length of perverse sheaves and D-modules | We prove that the length function for perverse sheaves and algebraic regular
holonomic D-modules on a smooth complex algebraic variety Y is an absolute
Q-constructible function. One consequence is: for "any" fixed natural (derived)
functor F between constructible complexes or perverse sheaves on two smooth
varieties X and Y, the loci of rank one local systems L on X whose image F(L)
has prescribed length are Zariski constructible subsets defined over Q,
obtained from finitely many torsion-translated complex affine algebraic subtori
of the moduli of rank one local systems via a finite sequence of taking union,
intersection, and complement.
| 0 | 0 | 1 | 0 | 0 | 0 |
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks | Deep neural networks are commonly developed and trained in 32-bit floating
point format. Significant gains in performance and energy efficiency could be
realized by training and inference in numerical formats optimized for deep
learning. Despite advances in limited precision inference in recent years,
training of neural networks in low bit-width remains a challenging problem.
Here we present the Flexpoint data format, aiming at a complete replacement of
32-bit floating point format training and inference, designed to support modern
deep network topologies without modifications. Flexpoint tensors have a shared
exponent that is dynamically adjusted to minimize overflows and maximize
available dynamic range. We validate Flexpoint by training AlexNet, a deep
residual network and a generative adversarial network, using a simulator
implemented with the neon deep learning framework. We demonstrate that 16-bit
Flexpoint closely matches 32-bit floating point in training all three models,
without any need for tuning of model hyperparameters. Our results suggest
Flexpoint as a promising numerical format for future hardware for training and
inference.
| 1 | 0 | 0 | 1 | 0 | 0 |
Bounds on the expected size of the maximum agreement subtree for a given tree shape | We show that the expected size of the maximum agreement subtree of two
$n$-leaf trees, uniformly random among all trees with the shape, is
$\Theta(\sqrt{n})$. To derive the lower bound, we prove a global structural
result on a decomposition of rooted binary trees into subgroups of leaves
called blobs. To obtain the upper bound, we generalize a first moment argument
for random tree distributions that are exchangeable and not necessarily
sampling consistent.
| 0 | 0 | 0 | 0 | 1 | 0 |
Magnetic ground state of SrRuO$_3$ thin film and applicability of standard first-principles approximations to metallic magnetism | A systematic first-principles study has been performed to understand the
magnetism of thin film SrRuO$_3$ which lots of research efforts have been
devoted to but no clear consensus has been reached about its ground state
properties. The relative t$_{2g}$ level difference, lattice distortion as well
as the layer thickness play together in determining the spin order. In
particular, it is important to understand the difference between two standard
approximations, namely LDA and GGA, in describing this metallic magnetism.
Landau free energy analysis and the magnetization-energy-ratio plot clearly
show the different tendency of favoring the magnetic moment formation, and it
is magnified when applied to the thin film limit where the experimental
information is severely limited. As a result, LDA gives a qualitatively
different prediction from GGA in the experimentally relevant region of strain
whereas both approximations give reasonable results for the bulk phase. We
discuss the origin of this difference and the applicability of standard methods
to the correlated oxide and the metallic magnetic systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
No minimal tall Borel ideal in the Katětov order | Answering a question of the second listed author we show that there is no
tall Borel ideal minimal among all tall Borel ideals in the Katětov order.
| 0 | 0 | 1 | 0 | 0 | 0 |
RelNN: A Deep Neural Model for Relational Learning | Statistical relational AI (StarAI) aims at reasoning and learning in noisy
domains described in terms of objects and relationships by combining
probability with first-order logic. With huge advances in deep learning in the
current years, combining deep networks with first-order logic has been the
focus of several recent studies. Many of the existing attempts, however, only
focus on relations and ignore object properties. The attempts that do consider
object properties are limited in terms of modelling power or scalability. In
this paper, we develop relational neural networks (RelNNs) by adding hidden
layers to relational logistic regression (the relational counterpart of
logistic regression). We learn latent properties for objects both directly and
through general rules. Back-propagation is used for training these models. A
modular, layer-wise architecture facilitates utilizing the techniques developed
within deep learning community to our architecture. Initial experiments on
eight tasks over three real-world datasets show that RelNNs are promising
models for relational learning.
| 1 | 0 | 0 | 1 | 0 | 0 |
$ΔN_{\text{eff}}$ and entropy production from early-decaying gravitinos | Gravitinos are a fundamental prediction of supergravity, their mass ($m_{G}$)
is informative of the value of the SUSY breaking scale, and, if produced during
reheating, their number density is a function of the reheating temperature
($T_{\text{rh}}$). As a result, constraining their parameter space provides in
turn significant constraints on particles physics and cosmology. We have
previously shown that for gravitinos decaying into photons or charged particles
during the ($\mu$ and $y$) distortion eras, upcoming CMB spectral distortions
bounds are highly effective in constraining the $T_{\text{rh}}-m_{G}$ space.
For heavier gravitinos (with lifetimes shorter than a few $\times10^6$ sec),
distortions are quickly thermalized and energy injections cause a temperature
rise for the CMB bath. If the decay occurs after neutrino decoupling, its
overall effect is a suppression of the effective number of relativistic degrees
of freedom ($N_{\text{eff}}$). In this paper, we utilize the observational
bounds on $N_{\text{eff}}$ to constrain gravitino decays, and hence provide new
constaints on gravitinos and reheating. For gravitino masses less than $\approx
10^5$ GeV, current observations give an upper limit on the reheating scale in
the range of $\approx 5 \times 10^{10}- 5 \times 10^{11}$GeV. For masses
greater than $\approx 4 \times 10^3$ GeV they are more stringent than previous
bounds from BBN constraints, coming from photodissociation of deuterium, by
almost 2 orders of magnitude.
| 0 | 1 | 0 | 0 | 0 | 0 |
Game-Theoretic Choice of Curing Rates Against Networked SIS Epidemics by Human Decision-Makers | We study networks of human decision-makers who independently decide how to
protect themselves against Susceptible-Infected-Susceptible (SIS) epidemics.
Motivated by studies in behavioral economics showing that humans perceive
probabilities in a nonlinear fashion, we examine the impacts of such
misperceptions on the equilibrium protection strategies. In our setting, nodes
choose their curing rates to minimize the infection probability under the
degree-based mean-field approximation of the SIS epidemic plus the cost of
their selected curing rate. We establish the existence of a degree based
equilibrium under both true and nonlinear perceptions of infection
probabilities (under suitable assumptions). When the per-unit cost of curing
rate is sufficiently high, we show that true expectation minimizers choose the
curing rate to be zero at the equilibrium, while curing rate is nonzero under
nonlinear probability weighting.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins | Membrane proteins constitute a large portion of the human proteome and
perform a variety of important functions as membrane receptors, transport
proteins, enzymes, signaling proteins, and more. The computational studies of
membrane proteins are usually much more complicated than those of globular
proteins. Here we propose a new continuum model for Poisson-Boltzmann
calculations of membrane channel proteins. Major improvements over the existing
continuum slab model are as follows: 1) The location and thickness of the slab
model are fine-tuned based on explicit-solvent MD simulations. 2) The highly
different accessibility in the membrane and water regions are addressed with a
two-step, two-probe grid labeling procedure, and 3) The water pores/channels
are automatically identified. The new continuum membrane model is optimized (by
adjusting the membrane probe, as well as the slab thickness and center) to best
reproduce the distributions of buried water molecules in the membrane region as
sampled in explicit water simulations. Our optimization also shows that the
widely adopted water probe of 1.4 {\AA} for globular proteins is a very
reasonable default value for membrane protein simulations. It gives an overall
minimum number of inconsistencies between the continuum and explicit
representations of water distributions in membrane channel proteins, at least
in the water accessible pore/channel regions that we focus on. Finally, we
validate the new membrane model by carrying out binding affinity calculations
for a potassium channel, and we observe a good agreement with experiment
results.
| 0 | 1 | 0 | 0 | 0 | 0 |
An ALMA survey of submillimetre galaxies in the Extended Chandra Deep Field South: Spectroscopic redshifts | We present spectroscopic redshifts of S(870)>2mJy submillimetre galaxies
(SMGs) which have been identified from the ALMA follow-up observations of 870um
detected sources in the Extended Chandra Deep Field South (the ALMA-LESS
survey). We derive spectroscopic redshifts for 52 SMGs, with a median of
z=2.4+/-0.1. However, the distribution features a high redshift tail, with ~25%
of the SMGs at z>3. Spectral diagnostics suggest that the SMGs are young
starbursts, and the velocity offsets between the nebular emission and UV ISM
absorption lines suggest that many are driving winds, with velocity offsets up
to 2000km/s. Using the spectroscopic redshifts and the extensive UV-to-radio
photometry in this field, we produce optimised spectral energy distributions
(SEDs) using Magphys, and use the SEDs to infer a median stellar mass of
M*=(6+/-1)x10^{10}Msol for our SMGs with spectroscopic redshifts. By combining
these stellar masses with the star-formation rates (measured from the
far-infrared SEDs), we show that SMGs (on average) lie a factor ~5 above the
main-sequence at z~2. We provide this library of 52 template fits with robust
and well-sampled SEDs available as a resource for future studies of SMGs, and
also release the spectroscopic catalog of ~2000 (mostly infrared-selected)
galaxies targeted as part of the spectroscopic campaign.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the structure of Hausdorff moment sequences of complex matrices | The paper treats several aspects of the truncated matricial
$[\alpha,\beta]$-Hausdorff type moment problems. It is shown that each
$[\alpha,\beta]$-Hausdorff moment sequence has a particular intrinsic
structure. More precisely, each element of this sequence varies within a closed
bounded matricial interval. The case that the corresponding moment coincides
with one of the endpoints of the interval plays a particular important role.
This leads to distinguished molecular solutions of the truncated matricial
$[\alpha,\beta]$-Hausdorff moment problem, which satisfy some extremality
properties. The proofs are mainly of algebraic character. The use of the
parallel sum of matrices is an essential tool in the proofs.
| 0 | 0 | 1 | 0 | 0 | 0 |
Lower bounds on the Bergman metric near points of infinite type | Let $\Omega$ be a pseudoconvex domain in $\mathbb C^n$ satisfying an
$f$-property for some function $f$. We show that the Bergman metric associated
to $\Omega$ has the lower bound $\tilde g(\delta_\Omega(z)^{-1})$ where
$\delta_\Omega(z)$ is the distance from $z$ to the boundary $\partial\Omega$
and $\tilde g$ is a specific function defined by $f$. This refines
Khanh-Zampieri's work in \cite{KZ12} with reducing the smoothness assumption of
the boundary.
| 0 | 0 | 1 | 0 | 0 | 0 |
Minimal Approximately Balancing Weights: Asymptotic Properties and Practical Considerations | In observational studies and sample surveys, and regression settings,
weighting methods are widely used to adjust for or balance observed covariates.
Recently, a few weighting methods have been proposed that focus on directly
balancing the covariates while minimizing the dispersion of the weights. In
this paper, we call this class of weights minimal approximately balancing
weights (MABW); we study their asymptotic properties and address two
practicalities. We show that, under standard technical conditions, MABW are
consistent estimates of the true inverse probability weights; the resulting
weighting estimator is consistent, asymptotically normal, and
semiparametrically efficient. For applications, we present a finite sample
oracle inequality showing that the loss incurred by balancing too many
functions of the covariates is limited in MABW. We also provide an algorithm
for choosing the degree of approximate balancing in MABW. Finally, we conclude
with numerical results that suggest approximate balancing is preferable to
exact balancing, especially when there is limited overlap in covariate
distributions: the root mean squared error of the weighting estimator can be
reduced by nearly a half.
| 0 | 0 | 1 | 1 | 0 | 0 |
Phonetic-attention scoring for deep speaker features in speaker verification | Recent studies have shown that frame-level deep speaker features can be
derived from a deep neural network with the training target set to discriminate
speakers by a short speech segment. By pooling the frame-level features,
utterance-level representations, called d-vectors, can be derived and used in
the automatic speaker verification (ASV) task. This simple average pooling,
however, is inherently sensitive to the phonetic content of the utterance. An
interesting idea borrowed from machine translation is the attention-based
mechanism, where the contribution of an input word to the translation at a
particular time is weighted by an attention score. This score reflects the
relevance of the input word and the present translation. We can use the same
idea to align utterances with different phonetic contents. This paper proposes
a phonetic-attention scoring approach for d-vector systems. By this approach,
an attention score is computed for each frame pair. This score reflects the
similarity of the two frames in phonetic content, and is used to weigh the
contribution of this frame pair in the utterance-based scoring. This new
scoring approach emphasizes the frame pairs with similar phonetic contents,
which essentially provides a soft alignment for utterances with any phonetic
contents. Experimental results show that compared with the naive average
pooling, this phonetic-attention scoring approach can deliver consistent
performance improvement in ASV tasks of both text-dependent and
text-independent.
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MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility | Collective urban mobility embodies the residents' local insights on the city.
Mobility practices of the residents are produced from their spatial choices,
which involve various considerations such as the atmosphere of destinations,
distance, past experiences, and preferences. The advances in mobile computing
and the rise of geo-social platforms have provided the means for capturing the
mobility practices; however, interpreting the residents' insights is
challenging due to the scale and complexity of an urban environment, and its
unique context. In this paper, we present MobInsight, a framework for making
localized interpretations of urban mobility that reflect various aspects of the
urbanism. MobInsight extracts a rich set of neighborhood features through
holistic semantic aggregation, and models the mobility between all-pairs of
neighborhoods. We evaluate MobInsight with the mobility data of Barcelona and
demonstrate diverse localized and semantically-rich interpretations.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Broad Consequences of Narrow Banking | We investigate the macroeconomic consequences of narrow banking in the
context of stock-flow consistent models. We begin with an extension of the
Goodwin-Keen model incorporating time deposits, government bills, cash, and
central bank reserves to the base model with loans and demand deposits and use
it to describe a fractional reserve banking system. We then characterize narrow
banking by a full reserve requirement on demand deposits and describe the
resulting separation between the payment system and lending functions of the
resulting banking sector. By way of numerical examples, we explore the
properties of fractional and full reserve versions of the model and compare
their asymptotic properties. We find that narrow banking does not lead to any
loss in economic growth when the models converge to a finite equilibrium, while
allowing for more direct monitoring and prevention of financial breakdowns in
the case of explosive asymptotic behaviour.
| 0 | 0 | 0 | 0 | 0 | 1 |
Sitatapatra: Blocking the Transfer of Adversarial Samples | Convolutional Neural Networks (CNNs) are widely used to solve classification
tasks in computer vision. However, they can be tricked into misclassifying
specially crafted `adversarial' samples -- and samples built to trick one model
often work alarmingly well against other models trained on the same task. In
this paper we introduce Sitatapatra, a system designed to block the transfer of
adversarial samples. It diversifies neural networks using a key, as in
cryptography, and provides a mechanism for detecting attacks. What's more, when
adversarial samples are detected they can typically be traced back to the
individual device that was used to develop them. The run-time overheads are
minimal permitting the use of Sitatapatra on constrained systems.
| 1 | 0 | 0 | 1 | 0 | 0 |
Neurofeedback: principles, appraisal and outstanding issues | Neurofeedback is a form of brain training in which subjects are fed back
information about some measure of their brain activity which they are
instructed to modify in a way thought to be functionally advantageous. Over the
last twenty years, NF has been used to treat various neurological and
psychiatric conditions, and to improve cognitive function in various contexts.
However, despite its growing popularity, each of the main steps in NF comes
with its own set of often covert assumptions. Here we critically examine some
conceptual and methodological issues associated with the way general objectives
and neural targets of NF are defined, and review the neural mechanisms through
which NF may act, and the way its efficacy is gauged. The NF process is
characterised in terms of functional dynamics, and possible ways in which it
may be controlled are discussed. Finally, it is proposed that improving NF will
require better understanding of various fundamental aspects of brain dynamics
and a more precise definition of functional brain activity and brain-behaviour
relationships.
| 0 | 0 | 0 | 0 | 1 | 0 |
Automating Image Analysis by Annotating Landmarks with Deep Neural Networks | Image and video analysis is often a crucial step in the study of animal
behavior and kinematics. Often these analyses require that the position of one
or more animal landmarks are annotated (marked) in numerous images. The process
of annotating landmarks can require a significant amount of time and tedious
labor, which motivates the need for algorithms that can automatically annotate
landmarks. In the community of scientists that use image and video analysis to
study the 3D flight of animals, there has been a trend of developing more
automated approaches for annotating landmarks, yet they fall short of being
generally applicable. Inspired by the success of Deep Neural Networks (DNNs) on
many problems in the field of computer vision, we investigate how suitable DNNs
are for accurate and automatic annotation of landmarks in video datasets
representative of those collected by scientists studying animals.
Our work shows, through extensive experimentation on videos of hawkmoths,
that DNNs are suitable for automatic and accurate landmark localization. In
particular, we show that one of our proposed DNNs is more accurate than the
current best algorithm for automatic localization of landmarks on hawkmoth
videos. Moreover, we demonstrate how these annotations can be used to
quantitatively analyze the 3D flight of a hawkmoth. To facilitate the use of
DNNs by scientists from many different fields, we provide a self contained
explanation of what DNNs are, how they work, and how to apply them to other
datasets using the freely available library Caffe and supplemental code that we
provide.
| 1 | 0 | 0 | 0 | 0 | 0 |
Weak-strong uniqueness in fluid dynamics | We give a survey of recent results on weak-strong uniqueness for compressible
and incompressible Euler and Navier-Stokes equations, and also make some new
observations. The importance of the weak-strong uniqueness principle stems, on
the one hand, from the instances of non-uniqueness for the Euler equations
exhibited in the past years; and on the other hand from the question of
convergence of singular limits, for which weak-strong uniqueness represents an
elegant tool.
| 0 | 1 | 1 | 0 | 0 | 0 |
Cognitive Subscore Trajectory Prediction in Alzheimer's Disease | Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of
multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's
Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple
subscores that quantify different aspects of a patient's cognitive state such
as learning, memory, and language production/comprehension. Although
computer-aided diagnostic techniques for classification of a patient's current
disease state exist, they provide little insight into the relationship between
changes in brain structure and different aspects of a patient's cognitive state
that occur over time in AD. We have developed a Convolutional Neural Network
architecture that can concurrently predict the trajectories of the 13 subscores
comprised by a subject's ADAS-cog examination results from a current minimally
preprocessed structural MRI scan up to 36 months from image acquisition time
without resorting to manual feature extraction. Mean performance metrics are
within range of those of existing techniques that require manual feature
selection and are limited to predicting aggregate scores.
| 1 | 0 | 0 | 1 | 0 | 0 |
Variance Regularizing Adversarial Learning | We introduce a novel approach for training adversarial models by replacing
the discriminator score with a bi-modal Gaussian distribution over the
real/fake indicator variables. In order to do this, we train the Gaussian
classifier to match the target bi-modal distribution implicitly through
meta-adversarial training. We hypothesize that this approach ensures a non-zero
gradient to the generator, even in the limit of a perfect classifier. We test
our method against standard benchmark image datasets as well as show the
classifier output distribution is smooth and has overlap between the real and
fake modes.
| 1 | 0 | 0 | 1 | 0 | 0 |
Improving fairness in machine learning systems: What do industry practitioners need? | The potential for machine learning (ML) systems to amplify social inequities
and unfairness is receiving increasing popular and academic attention. A surge
of recent work has focused on the development of algorithmic tools to assess
and mitigate such unfairness. If these tools are to have a positive impact on
industry practice, however, it is crucial that their design be informed by an
understanding of real-world needs. Through 35 semi-structured interviews and an
anonymous survey of 267 ML practitioners, we conduct the first systematic
investigation of commercial product teams' challenges and needs for support in
developing fairer ML systems. We identify areas of alignment and disconnect
between the challenges faced by industry practitioners and solutions proposed
in the fair ML research literature. Based on these findings, we highlight
directions for future ML and HCI research that will better address industry
practitioners' needs.
| 1 | 0 | 0 | 0 | 0 | 0 |
PICOSEC: Charged particle Timing to 24 picosecond Precision with MicroPattern Gas Detectors | The prospect of pileup induced backgrounds at the High Luminosity LHC
(HL-LHC) has stimulated intense interest in technology for charged particle
timing at high rates. In contrast to the role of timing for particle
identification, which has driven incremental improvements in timing, the LHC
timing challenge dictates a specific level of timing performance- roughly 20-30
picoseconds. Since the elapsed time for an LHC bunch crossing (with standard
design book parameters) has an rms spread of 170 picoseconds, the $\sim50-100$
picosecond resolution now commonly achieved in TOF systems would be
insufficient to resolve multiple "in-time" pileup. Here we present a MicroMegas
based structure which achieves the required time precision (ie 24 picoseconds
for 150 GeV $\mu$'s) and could potentially offer an inexpensive solution
covering large areas with $\sim 1$ cm$^2$ pixel size. We present here a
proof-of-principle which motivates further work in our group toward realizing a
practical design capable of long-term survival in a high rate experiment.
| 0 | 1 | 0 | 0 | 0 | 0 |
The first result on 76Ge neutrinoless double beta decay from CDEX-1 experiment | We report the first result on Ge-76 neutrinoless double beta decay from
CDEX-1 experiment at China Jinping Underground Laboratory. A mass of 994 g
p-type point-contact high purity germanium detector has been installed to
search the neutrinoless double beta decay events, as well as to directly detect
dark matter particles. An exposure of 304 kg*day has been analyzed. The
wideband spectrum from 500 keV to 3 MeV was obtained and the average event rate
at the 2.039 MeV energy range is about 0.012 count per keV per kg per day. The
half-life of Ge-76 neutrinoless double beta decay has been derived based on
this result as: T 1/2 > 6.4*10^22 yr (90% C.L.). An upper limit on the
effective Majorana-neutrino mass of 5.0 eV has been achieved. The possible
methods to further decrease the background level have been discussed and will
be pursued in the next stage of CDEX experiment.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measuring scientific buzz | Keywords are important for information retrieval. They are used to classify
and sort papers. However, these terms can also be used to study trends within
and across fields. We want to explore the lifecycle of new keywords. How often
do new terms come into existence and how long till they fade out? In this
paper, we present our preliminary analysis where we measure the burstiness of
keywords within the field of AI. We examine 150k keywords in approximately 100k
journal and conference papers. We find that nearly 80\% of the keywords die off
before year one for both journals and conferences but that terms last longer in
journals versus conferences. We also observe time periods of thematic bursts in
AI -- one where the terms are more neuroscience inspired and one more oriented
to computational optimization. This work shows promise of using author keywords
to better understand dynamics of buzz within science.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fully Optical Spacecraft Communications: Implementing an Omnidirectional PV-Cell Receiver and 8Mb/s LED Visible Light Downlink with Deep Learning Error Correction | Free space optical communication techniques have been the subject of numerous
investigations in recent years, with multiple missions expected to fly in the
near future. Existing methods require high pointing accuracies, drastically
driving up overall system cost. Recent developments in LED-based visible light
communication (VLC) and past in-orbit experiments have convinced us that the
technology has reached a critical level of maturity. On these premises, we
propose a new optical communication system utilizing a VLC downlink and a high
throughput, omnidirectional photovoltaic cell receiver system. By performing
error-correction via deep learning methods and by utilizing phase-delay
interference, the system is able to deliver data rates that match those of
traditional laser-based solutions. A prototype of the proposed system has been
constructed, demonstrating the scheme to be a feasible alternative to
laser-based methods. This creates an opportunity for the full scale development
of optical communication techniques on small spacecraft as a backup telemetry
beacon or as a high throughput link.
| 1 | 0 | 0 | 0 | 0 | 0 |
Linear compartmental models: input-output equations and operations that preserve identifiability | This work focuses on the question of how identifiability of a mathematical
model, that is, whether parameters can be recovered from data, is related to
identifiability of its submodels. We look specifically at linear compartmental
models and investigate when identifiability is preserved after adding or
removing model components. In particular, we examine whether identifiability is
preserved when an input, output, edge, or leak is added or deleted. Our
approach, via differential algebra, is to analyze specific input-output
equations of a model and the Jacobian of the associated coefficient map. We
clarify a prior determinantal formula for these equations, and then use it to
prove that, under some hypotheses, a model's input-output equations can be
understood in terms of certain submodels we call "output-reachable". Our proofs
use algebraic and combinatorial techniques.
| 0 | 0 | 0 | 0 | 1 | 0 |
On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding | We develop theory for nonlinear dimensionality reduction (NLDR). A number of
NLDR methods have been developed, but there is limited understanding of how
these methods work and the relationships between them. There is limited basis
for using existing NLDR theory for deriving new algorithms. We provide a novel
framework for analysis of NLDR via a connection to the statistical theory of
linear smoothers. This allows us to both understand existing methods and derive
new ones. We use this connection to smoothing to show that asymptotically,
existing NLDR methods correspond to discrete approximations of the solutions of
sets of differential equations given a boundary condition. In particular, we
can characterize many existing methods in terms of just three limiting
differential operators and boundary conditions. Our theory also provides a way
to assert that one method is preferable to another; indeed, we show Local
Tangent Space Alignment is superior within a class of methods that assume a
global coordinate chart defines an isometric embedding of the manifold.
| 0 | 0 | 0 | 1 | 0 | 0 |
Asymmetric Deep Supervised Hashing | Hashing has been widely used for large-scale approximate nearest neighbor
search because of its storage and search efficiency. Recent work has found that
deep supervised hashing can significantly outperform non-deep supervised
hashing in many applications. However, most existing deep supervised hashing
methods adopt a symmetric strategy to learn one deep hash function for both
query points and database (retrieval) points. The training of these symmetric
deep supervised hashing methods is typically time-consuming, which makes them
hard to effectively utilize the supervised information for cases with
large-scale database. In this paper, we propose a novel deep supervised hashing
method, called asymmetric deep supervised hashing (ADSH), for large-scale
nearest neighbor search. ADSH treats the query points and database points in an
asymmetric way. More specifically, ADSH learns a deep hash function only for
query points, while the hash codes for database points are directly learned.
The training of ADSH is much more efficient than that of traditional symmetric
deep supervised hashing methods. Experiments show that ADSH can achieve
state-of-the-art performance in real applications.
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Pseudo-Separation for Assessment of Structural Vulnerability of a Network | Based upon the idea that network functionality is impaired if two nodes in a
network are sufficiently separated in terms of a given metric, we introduce two
combinatorial \emph{pseudocut} problems generalizing the classical min-cut and
multi-cut problems. We expect the pseudocut problems will find broad relevance
to the study of network reliability. We comprehensively analyze the
computational complexity of the pseudocut problems and provide three
approximation algorithms for these problems.
Motivated by applications in communication networks with strict
Quality-of-Service (QoS) requirements, we demonstrate the utility of the
pseudocut problems by proposing a targeted vulnerability assessment for the
structure of communication networks using QoS metrics; we perform experimental
evaluations of our proposed approximation algorithms in this context.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes | In this paper we use Gaussian Process (GP) regression to propose a novel
approach for predicting volatility of financial returns by forecasting the
envelopes of the time series. We provide a direct comparison of their
performance to traditional approaches such as GARCH. We compare the forecasting
power of three approaches: GP regression on the absolute and squared returns;
regression on the envelope of the returns and the absolute returns; and
regression on the envelope of the negative and positive returns separately. We
use a maximum a posteriori estimate with a Gaussian prior to determine our
hyperparameters. We also test the effect of hyperparameter updating at each
forecasting step. We use our approaches to forecast out-of-sample volatility of
four currency pairs over a 2 year period, at half-hourly intervals. From three
kernels, we select the kernel giving the best performance for our data. We use
two published accuracy measures and four statistical loss functions to evaluate
the forecasting ability of GARCH vs GPs. In mean squared error the GP's perform
20% better than a random walk model, and 50% better than GARCH for the same
data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Out-of-Sample Testing for GANs | We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a
test set to directly measure the reconstruction quality in the original sample
space (no auxiliary networks are necessary), and it also computes the
(log)likelihood for the reconstructed samples in the test set. Further, EvalGAN
is agnostic to the GAN algorithm and the dataset. We decided to test it on
three state-of-the-art GANs over the well-known CIFAR-10 and CelebA datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Quantum periodicity in the critical current of superconducting rings with asymmetric link-up of current leads | We use superconducting rings with asymmetric link-up of current leads for
experimental investigation of winding number change at magnetic field
corresponding to the half of the flux quantum inside the ring. According to the
conventional theory, the critical current of such rings should change by jump
due to this change. Experimental data obtained at measurements of aluminum
rings agree with theoretical prediction in magnetic flux region close to
integer numbers of the flux quantum and disagree in the region close to the
half of the one, where a smooth change is observed instead of the jump. First
measurements of tantalum ring give a hope for the jump. Investigation of this
problem may have both fundamental and practical importance.
| 0 | 1 | 0 | 0 | 0 | 0 |
Understanding Norm Change: An Evolutionary Game-Theoretic Approach (Extended Version) | Human societies around the world interact with each other by developing and
maintaining social norms, and it is critically important to understand how such
norms emerge and change. In this work, we define an evolutionary game-theoretic
model to study how norms change in a society, based on the idea that different
strength of norms in societies translate to different game-theoretic
interaction structures and incentives. We use this model to study, both
analytically and with extensive agent-based simulations, the evolutionary
relationships of the need for coordination in a society (which is related to
its norm strength) with two key aspects of norm change: cultural inertia
(whether or how quickly the population responds when faced with conditions that
make a norm change desirable), and exploration rate (the willingness of agents
to try out new strategies). Our results show that a high need for coordination
leads to both high cultural inertia and a low exploration rate, while a low
need for coordination leads to low cultural inertia and high exploration rate.
This is the first work, to our knowledge, on understanding the evolutionary
causal relationships among these factors.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Story of Parametric Trace Slicing, Garbage and Static Analysis | This paper presents a proposal (story) of how statically detecting
unreachable objects (in Java) could be used to improve a particular runtime
verification approach (for Java), namely parametric trace slicing. Monitoring
algorithms for parametric trace slicing depend on garbage collection to (i)
cleanup data-structures storing monitored objects, ensuring they do not become
unmanageably large, and (ii) anticipate the violation of (non-safety)
properties that cannot be satisfied as a monitored object can no longer appear
later in the trace. The proposal is that both usages can be improved by making
the unreachability of monitored objects explicit in the parametric property and
statically introducing additional instrumentation points generating related
events. The ideas presented in this paper are still exploratory and the
intention is to integrate the described techniques into the MarQ monitoring
tool for quantified event automata.
| 1 | 0 | 0 | 0 | 0 | 0 |
Extended TQFT arising from enriched multi-fusion categories | We define a symmetric monoidal (4,3)-category with duals whose objects are
certain enriched multi-fusion categories. For every modular tensor category
$\mathcal{C}$, there is a self enriched multi-fusion category $\mathfrak{C}$
giving rise to an object of this symmetric monoidal (4,3)-category. We
conjecture that the extended 3D TQFT given by the fully dualizable object
$\mathfrak{C}$ extends the 1-2-3-dimensional Reshetikhin-Turaev TQFT associated
to the modular tensor category $\mathcal{C}$ down to dimension zero.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multipermutation Ulam Sphere Analysis Toward Characterizing Maximal Code Size | Permutation codes, in the form of rank modulation, have shown promise for
applications such as flash memory. One of the metrics recently suggested as
appropriate for rank modulation is the Ulam metric, which measures the minimum
translocation distance between permutations. Multipermutation codes have also
been proposed as a generalization of permutation codes that would improve code
size (and consequently the code rate). In this paper we analyze the Ulam metric
in the context of multipermutations, noting some similarities and differences
between the Ulam metric in the context of permutations. We also consider sphere
sizes for multipermutations under the Ulam metric and resulting bounds on code
size.
| 1 | 0 | 1 | 0 | 0 | 0 |
Thermophysical characteristics of the large main-belt asteroid (349) Dembowska | (349) Dembowska, a large, bright main-belt asteroid, has a fast rotation and
oblique spin axis. It may have experienced partial melting and differentiation.
We constrain Dembowska's thermophysical properties, e.g., thermal inertia,
roughness fraction, geometric albedo and effective diameter within 3$\sigma$
uncertainty of $\Gamma=20^{+12}_{-7}\rm~Jm^{-2}s^{-0.5}K^{-1}$, $f_{\rm
r}=0.25^{+0.60}_{-0.25}$, $p_{\rm v}=0.309^{+0.026}_{-0.038}$, and $D_{\rm
eff}=155.8^{+7.5}_{-6.2}\rm~km$, by utilizing the Advanced Thermophysical Model
(ATPM) to analyse four sets of thermal infrared data obtained by IRAS, AKARI,
WISE and Subaru/COMICS at different epochs. In addition, by modeling the
thermal lightcurve observed by WISE, we obtain the rotational phases of each
dataset. These rotationally resolved data do not reveal significant variations
of thermal inertia and roughness across the surface, indicating the surface of
Dembowska should be covered by a dusty regolith layer with few rocks or
boulders. Besides, the low thermal inertia of Dembowska show no significant
difference with other asteroids larger than 100 km, indicating the dynamical
lives of these large asteroids are long enough to make the surface to have
sufficiently low thermal inertia. Furthermore, based on the derived surface
thermophysical properties, as well as the known orbital and rotational
parameters, we can simulate Dembowska's surface and subsurface temperature
throughout its orbital period. The surface temperature varies from $\sim40$ K
to $\sim220$ K, showing significant seasonal variation, whereas the subsurface
temperature achieves equilibrium temperature about $120\sim160$ K below
$30\sim50$ cm depth.
| 0 | 1 | 0 | 0 | 0 | 0 |
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning | We propose CM3, a new deep reinforcement learning method for cooperative
multi-agent problems where agents must coordinate for joint success in
achieving different individual goals. We restructure multi-agent learning into
a two-stage curriculum, consisting of a single-agent stage for learning to
accomplish individual tasks, followed by a multi-agent stage for learning to
cooperate in the presence of other agents. These two stages are bridged by
modular augmentation of neural network policy and value functions. We further
adapt the actor-critic framework to this curriculum by formulating local and
global views of the policy gradient and learning via a double critic,
consisting of a decentralized value function and a centralized action-value
function. We evaluated CM3 on a new high-dimensional multi-agent environment
with sparse rewards: negotiating lane changes among multiple autonomous
vehicles in the Simulation of Urban Mobility (SUMO) traffic simulator. Detailed
ablation experiments show the positive contribution of each component in CM3,
and the overall synthesis converges significantly faster to higher performance
policies than existing cooperative multi-agent methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Adaptive twisting sliding mode control for quadrotor unmanned aerial vehicles | This work addresses the problem of robust attitude control of quadcopters.
First, the mathematical model of the quadcopter is derived considering factors
such as nonlinearity, external disturbances, uncertain dynamics and strong
coupling. An adaptive twisting sliding mode control algorithm is then developed
with the objective of controlling the quadcopter to track desired attitudes
under various conditions. For this, the twisting sliding mode control law is
modified with a proposed gain adaptation scheme to improve the control
transient and tracking performance. Extensive simulation studies and
comparisons with experimental data have been carried out for a Solo quadcopter.
The results show that the proposed control scheme can achieve strong robustness
against disturbances while is adaptable to parametric variations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dynamic Laplace: Efficient Centrality Measure for Weighted or Unweighted Evolving Networks | With its origin in sociology, Social Network Analysis (SNA), quickly emerged
and spread to other areas of research, including anthropology, biology,
information science, organizational studies, political science, and computer
science. Being it's objective the investigation of social structures through
the use of networks and graph theory, Social Network Analysis is, nowadays, an
important research area in several domains. Social Network Analysis cope with
different problems namely network metrics, models, visualization and
information spreading, each one with several approaches, methods and
algorithms. One of the critical areas of Social Network Analysis involves the
calculation of different centrality measures (i.e.: the most important vertices
within a graph). Today, the challenge is how to do this fast and efficiently,
as many increasingly larger datasets are available. Recently, the need to apply
such centrality algorithms to non static networks (i.e.: networks that evolve
over time) is also a new challenge. Incremental and dynamic versions of
centrality measures are starting to emerge (betweenness, closeness, etc). Our
contribution is the proposal of two incremental versions of the Laplacian
Centrality measure, that can be applied not only to large graphs but also to,
weighted or unweighted, dynamically changing networks. The experimental
evaluation was performed with several tests in different types of evolving
networks, incremental or fully dynamic. Results have shown that our incremental
versions of the algorithm can calculate node centralities in large networks,
faster and efficiently than the corresponding batch version in both incremental
and full dynamic network setups.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fighting Accounting Fraud Through Forensic Data Analytics | Accounting fraud is a global concern representing a significant threat to the
financial system stability due to the resulting diminishing of the market
confidence and trust of regulatory authorities. Several tricks can be used to
commit accounting fraud, hence the need for non-static regulatory interventions
that take into account different fraudulent patterns. Accordingly, this study
aims to improve the detection of accounting fraud via the implementation of
several machine learning methods to better differentiate between fraud and
non-fraud companies, and to further assist the task of examination within the
riskier firms by evaluating relevant financial indicators. Out-of-sample
results suggest there is a great potential in detecting falsified financial
statements through statistical modelling and analysis of publicly available
accounting information. The proposed methodology can be of assistance to public
auditors and regulatory agencies as it facilitates auditing processes, and
supports more targeted and effective examinations of accounting reports.
| 0 | 0 | 0 | 1 | 0 | 0 |
Doubly Stochastic Variational Inference for Deep Gaussian Processes | Gaussian processes (GPs) are a good choice for function approximation as they
are flexible, robust to over-fitting, and provide well-calibrated predictive
uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of
GPs, but inference in these models has proved challenging. Existing approaches
to inference in DGP models assume approximate posteriors that force
independence between the layers, and do not work well in practice. We present a
doubly stochastic variational inference algorithm, which does not force
independence between layers. With our method of inference we demonstrate that a
DGP model can be used effectively on data ranging in size from hundreds to a
billion points. We provide strong empirical evidence that our inference scheme
for DGPs works well in practice in both classification and regression.
| 0 | 0 | 0 | 1 | 0 | 0 |
Electric properties of carbon nano-onion/polyaniline composites: a combined electric modulus and ac conductivity study | The complex electric modulus and the ac conductivity of carbon
nanoonion/polyaniline composites were studied from 1 mHz to 1 MHz at isothermal
conditions ranging from 15 K to room temperature. The temperature dependence of
the electric modulus and the dc conductivity analyses indicate a couple of
hopping mechanisms. The distinction between thermally activated processes and
the determination of cross-over temperature were achieved by exploring the
temperature dependence of the fractional exponent of the dispersive ac
conductivity and the bifurcation of the scaled ac conductivity isotherms. The
results are analyzed by combining the granular metal model(inter-grain charge
tunneling of extended electron states located within mesoscopic highly
conducting polyaniline grains) and a 3D Mott variable range hopping model
(phonon assisted tunneling within the carbon nano-onions and clusters).
| 0 | 1 | 0 | 0 | 0 | 0 |
Uniform Rates of Convergence of Some Representations of Extremes : a first approach | Uniform convergence rates are provided for asymptotic representations of
sample extremes. These bounds which are universal in the sense that they do not
depend on the extreme value index are meant to be extended to arbitrary samples
extremes in coming papers.
| 0 | 0 | 0 | 1 | 0 | 0 |
Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting | Predicting Arctic sea ice extent is a notoriously difficult forecasting
problem, even for lead times as short as one month. Motivated by Arctic
intraannual variability phenomena such as reemergence of sea surface
temperature and sea ice anomalies, we use a prediction approach for sea ice
anomalies based on analog forecasting. Traditional analog forecasting relies on
identifying a single analog in a historical record, usually by minimizing
Euclidean distance, and forming a forecast from the analog's historical
trajectory. Here an ensemble of analogs are used to make forecasts, where the
ensemble weights are determined by a dynamics-adapted similarity kernel, which
takes into account the nonlinear geometry on the underlying data manifold. We
apply this method for forecasting pan-Arctic and regional sea ice area and
volume anomalies from multi-century climate model data, and in many cases find
improvement over the benchmark damped persistence forecast. Examples of success
include the 3--6 month lead time prediction of pan-Arctic area, the winter sea
ice area prediction of some marginal ice zone seas, and the 3--12 month lead
time prediction of sea ice volume anomalies in many central Arctic basins. We
discuss possible connections between KAF success and sea ice reemergence, and
find KAF to be successful in regions and seasons exhibiting high interannual
variability.
| 0 | 1 | 0 | 0 | 0 | 0 |
Eigensolutions and spectral analysis of a model for vertical gene transfer of plasmids | Plasmids are autonomously replicating genetic elements in bacteria. At cell
division plasmids are distributed among the two daughter cells. This gene
transfer from one generation to the next is called vertical gene transfer. We
study the dynamics of a bacterial population carrying plasmids and are in
particular interested in the long-time distribution of plasmids. Starting with
a model for a bacterial population structured by the discrete number of
plasmids, we proceed to the continuum limit in order to derive a continuous
model. The model incorporates plasmid reproduction, division and death of
bacteria, and distribution of plasmids at cell division. It is a hyperbolic
integro-differential equation and a so-called growth-fragmentation-death model.
As we are interested in the long-time distribution of plasmids we study the
associated eigenproblem and show existence of eigensolutions. The stability of
this solution is studied by analyzing the spectrum of the integro-differential
operator given by the eigenproblem. By relating the spectrum with the spectrum
of an integral operator we find a simple real dominating eigenvalue with a
non-negative corresponding eigenfunction. Moreover, we describe an iterative
method for the numerical construction of the eigenfunction.
| 0 | 0 | 0 | 0 | 1 | 0 |
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