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The Brown-Peterson spectrum is not $E_{2(p^2+2)}$ at odd primes | Recently, Lawson has shown that the 2-primary Brown-Peterson spectrum does
not admit the structure of an $E_{12}$ ring spectrum, thus answering a question
of May in the negative. We extend Lawson's result to odd primes by proving that
the p-primary Brown-Peterson spectrum does not admit the structure of an
$E_{2(p^2+2)}$ ring spectrum. We also show that there can be no map $MU \to BP$
of $E_{2p+3}$ ring spectra at any prime.
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Beliefs in Markov Trees - From Local Computations to Local Valuation | This paper is devoted to expressiveness of hypergraphs for which uncertainty
propagation by local computations via Shenoy/Shafer method applies. It is
demonstrated that for this propagation method for a given joint belief
distribution no valuation of hyperedges of a hypergraph may provide with
simpler hypergraph structure than valuation of hyperedges by conditional
distributions. This has vital implication that methods recovering belief
networks from data have no better alternative for finding the simplest
hypergraph structure for belief propagation. A method for recovery
tree-structured belief networks has been developed and specialized for
Dempster-Shafer belief functions
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A model for random fire induced tree-grass coexistence in savannas | Tree-grass coexistence in savanna ecosystems depends strongly on
environmental disturbances out of which crucial is fire. Most modeling attempts
in the literature lack stochastic approach to fire occurrences which is
essential to reflect their unpredictability. Existing models that actually
include stochasticity of fire are usually analyzed only numerically. We
introduce new minimalistic model of tree-grass coexistence where fires occur
according to stochastic process. We use the tools of linear semigroup theory to
provide more careful mathematical analysis of the model. Essentially we show
that there exists a unique stationary distribution of tree and grass biomasses.
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A depth-based method for functional time series forecasting | An approach is presented for making predictions about functional time series.
The method is applied to data coming from periodically correlated processes and
electricity demand, obtaining accurate point forecasts and narrow prediction
bands that cover high proportions of the forecasted functional datum, for a
given confidence level. The method is computationally efficient and
substantially different to other functional time series methods, offering a new
insight for the analysis of these data structures.
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Dihedral Molecular Configurations Interacting by Lennard-Jones and Coulomb Forces | In this paper, we investigate periodic vibrations of a group of particles
with a dihedral configuration in the plane governed by the Lennard-Jones and
Coulomb forces. Using the gradient equivariant degree, we provide a full
topological classification of the periodic solutions with both temporal and
spatial symmetries. In the process, we provide with general formulae for the
spectrum of the linearized system which allows us to obtain the critical
frequencies of the particle motions which indicate the set of all critical
periods of small amplitude periodic solutions emerging from a given stationary
symmetric orbit of solutions.
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Understanding news story chains using information retrieval and network clustering techniques | Content analysis of news stories (whether manual or automatic) is a
cornerstone of the communication studies field. However, much research is
conducted at the level of individual news articles, despite the fact that news
events (especially significant ones) are frequently presented as "stories" by
news outlets: chains of connected articles covering the same event from
different angles. These stories are theoretically highly important in terms of
increasing public recall of news items and enhancing the agenda-setting power
of the press. Yet thus far, the field has lacked an efficient method for
detecting groups of articles which form stories in a way that enables their
analysis.
In this work, we present a novel, automated method for identifying linked
news stories from within a corpus of articles. This method makes use of
techniques drawn from the field of information retrieval to identify textual
closeness of pairs of articles, and then clustering techniques taken from the
field of network analysis to group these articles into stories. We demonstrate
the application of the method to a corpus of 61,864 articles, and show how it
can efficiently identify valid story clusters within the corpus. We use the
results to make observations about the prevalence and dynamics of stories
within the UK news media, showing that more than 50% of news production takes
place within stories.
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Fair Kernel Learning | New social and economic activities massively exploit big data and machine
learning algorithms to do inference on people's lives. Applications include
automatic curricula evaluation, wage determination, and risk assessment for
credits and loans. Recently, many governments and institutions have raised
concerns about the lack of fairness, equity and ethics in machine learning to
treat these problems. It has been shown that not including sensitive features
that bias fairness, such as gender or race, is not enough to mitigate the
discrimination when other related features are included. Instead, including
fairness in the objective function has been shown to be more efficient.
We present novel fair regression and dimensionality reduction methods built
on a previously proposed fair classification framework. Both methods rely on
using the Hilbert Schmidt independence criterion as the fairness term. Unlike
previous approaches, this allows us to simplify the problem and to use multiple
sensitive variables simultaneously. Replacing the linear formulation by kernel
functions allows the methods to deal with nonlinear problems. For both linear
and nonlinear formulations the solution reduces to solving simple matrix
inversions or generalized eigenvalue problems. This simplifies the evaluation
of the solutions for different trade-off values between the predictive error
and fairness terms. We illustrate the usefulness of the proposed methods in toy
examples, and evaluate their performance on real world datasets to predict
income using gender and/or race discrimination as sensitive variables, and
contraceptive method prediction under demographic and socio-economic sensitive
descriptors.
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Underwater Surveying via Bearing only Cooperative Localization | Bearing only cooperative localization has been used successfully on aerial
and ground vehicles. In this paper we present an extension of the approach to
the underwater domain. The focus is on adapting the technique to handle the
challenging visibility conditions underwater. Furthermore, data from inertial,
magnetic, and depth sensors are utilized to improve the robustness of the
estimation. In addition to robotic applications, the presented technique can be
used for cave mapping and for marine archeology surveying, both by human
divers. Experimental results from different environments, including a fresh
water, low visibility, lake in South Carolina; a cavern in Florida; and coral
reefs in Barbados during the day and during the night, validate the robustness
and the accuracy of the proposed approach.
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Sterile Neutrinos and B-L Symmetry | We revisit the relation between the neutrino masses and the spontaneous
breaking of the B-L gauge symmetry. We discuss the main scenarios for Dirac and
Majorana neutrinos and point out two simple mechanisms for neutrino masses. In
this context the neutrino masses can be generated either at tree level or at
quantum level and one predicts the existence of very light sterile neutrinos
with masses below the eV scale. The predictions for lepton number violating
processes such as mu to e and mu to e gamma are discussed in detail. The impact
from the cosmological constraints on the effective number of relativistic
degree of freedom is investigated.
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Effects of parametric uncertainties in cascaded open quantum harmonic oscillators and robust generation of Gaussian invariant states | This paper is concerned with the generation of Gaussian invariant states in
cascades of open quantum harmonic oscillators governed by linear quantum
stochastic differential equations. We carry out infinitesimal perturbation
analysis of the covariance matrix for the invariant Gaussian state of such a
system and the related purity functional subject to inaccuracies in the energy
and coupling matrices of the subsystems. This leads to the problem of balancing
the state-space realizations of the component oscillators through symplectic
similarity transformations in order to minimize the mean square sensitivity of
the purity functional to small random perturbations of the parameters. This
results in a quadratic optimization problem with an effective solution in the
case of cascaded one-mode oscillators, which is demonstrated by a numerical
example. We also discuss a connection of the sensitivity index with classical
statistical distances and outline infinitesimal perturbation analysis for
translation invariant cascades of identical oscillators. The findings of the
paper are applicable to robust state generation in quantum stochastic networks.
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A computer-based recursion algorithm for automatic charge of power device of electric vehicles carrying electromagnet | This paper proposes a computer-based recursion algorithm for automatic charge
of power device of electric vehicles carrying electromagnet. The charging
system includes charging cable with one end connecting gang socket,
electromagnetic gear driving the connecting socket and a charging pile breaking
or closing, and detecting part for detecting electric vehicle static call or
start state. The gang socket mentioned above is linked to electromagnetic gear,
and the detecting part is connected with charging management system containing
the intelligent charging power module which controls the electromagnetic drive
action to close socket with a charging pile at static state and to break at
start state. Our work holds an electric automobile with convenience, safety low
maintenance cost.
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Abelian Tensor Models on the Lattice | We consider a chain of Abelian Klebanov-Tarnopolsky fermionic tensor models
coupled through quartic nearest-neighbor interactions. We characterize the
gauge-singlet spectrum for small chains ($L=2,3,4,5$) and observe that the
spectral statistics exhibits strong evidences in favor of quasi-many body
localization.
| 0 | 1 | 0 | 0 | 0 | 0 |
Distance-based Confidence Score for Neural Network Classifiers | The reliable measurement of confidence in classifiers' predictions is very
important for many applications and is, therefore, an important part of
classifier design. Yet, although deep learning has received tremendous
attention in recent years, not much progress has been made in quantifying the
prediction confidence of neural network classifiers. Bayesian models offer a
mathematically grounded framework to reason about model uncertainty, but
usually come with prohibitive computational costs. In this paper we propose a
simple, scalable method to achieve a reliable confidence score, based on the
data embedding derived from the penultimate layer of the network. We
investigate two ways to achieve desirable embeddings, by using either a
distance-based loss or Adversarial Training. We then test the benefits of our
method when used for classification error prediction, weighting an ensemble of
classifiers, and novelty detection. In all tasks we show significant
improvement over traditional, commonly used confidence scores.
| 1 | 0 | 0 | 1 | 0 | 0 |
Benford analysis of quantum critical phenomena: First digit provides high finite-size scaling exponent while first two and further are not much better | Benford's law is an empirical edict stating that the lower digits appear more
often than higher ones as the first few significant digits in statistics of
natural phenomena and mathematical tables. A marked proportion of such analyses
is restricted to the first significant digit. We employ violation of Benford's
law, up to the first four significant digits, for investigating magnetization
and correlation data of paradigmatic quantum many-body systems to detect
cooperative phenomena, focusing on the finite-size scaling exponents thereof.
We find that for the transverse field quantum XY model, behavior of the very
first significant digit of an observable, at an arbitrary point of the
parameter space, is enough to capture the quantum phase transition in the model
with a relatively high scaling exponent. A higher number of significant digits
do not provide an appreciable further advantage, in particular, in terms of an
increase in scaling exponents. Since the first significant digit of a physical
quantity is relatively simple to obtain in experiments, the results have
potential implications for laboratory observations in noisy environments.
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A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI | Two popular classes of methods for approximate inference are Markov chain
Monte Carlo (MCMC) and variational inference. MCMC tends to be accurate if run
for a long enough time, while variational inference tends to give better
approximations at shorter time horizons. However, the amount of time needed for
MCMC to exceed the performance of variational methods can be quite high,
motivating more fine-grained tradeoffs. This paper derives a distribution over
variational parameters, designed to minimize a bound on the divergence between
the resulting marginal distribution and the target, and gives an example of how
to sample from this distribution in a way that interpolates between the
behavior of existing methods based on Langevin dynamics and stochastic gradient
variational inference (SGVI).
| 1 | 0 | 0 | 1 | 0 | 0 |
Making Sense of Bell's Theorem and Quantum Nonlocality | Bell's theorem has fascinated physicists and philosophers since his 1964
paper, which was written in response to the 1935 paper of Einstein, Podolsky,
and Rosen. Bell's theorem and its many extensions have led to the claim that
quantum mechanics and by inference nature herself are nonlocal in the sense
that a measurement on a system by an observer at one location has an immediate
effect on a distant "entangled" system (one with which the original system has
previously interacted). Einstein was repulsed by such "spooky action at a
distance" and was led to question whether quantum mechanics could provide a
complete description of physical reality. In this paper I argue that quantum
mechanics does not require spooky action at a distance of any kind and yet it
is entirely reasonable to question the assumption that quantum mechanics can
provide a complete description of physical reality. The magic of entangled
quantum states has little to do with entanglement and everything to do with
superposition, a property of all quantum systems and a foundational tenet of
quantum mechanics.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Some properties of dyadic operators | In this paper, the objects of our investigation are some dyadic operators,
including dyadic shifts, multilinear paraproducts and multilinear Haar
multipliers. We mainly focus on the continuity and compactness of these
operators. First, we consider the continuity properties of these operators.
Then, by the Fréchet-Kolmogorov-Riesz-Tsuji theorem, the non-compactness
properties of these dyadic operators will be studied. Moreover, we show that
their commutators are compact with \textit{CMO} functions, which is quite
different from the non-compaceness properties of these dyadic operators. These
results are similar to those for Calderón-Zygmund singular integral
operators.
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Rationalizability and Epistemic Priority Orderings | At the beginning of a dynamic game, players may have exogenous theories about
how the opponents are going to play. Suppose that these theories are commonly
known. Then, players will refine their first-order beliefs, and challenge their
own theories, through strategic reasoning. I develop and characterize
epistemically a new solution concept, Selective Rationalizability, which
accomplishes this task under the following assumption: when the observed
behavior is not compatible with the beliefs in players' rationality and
theories of all orders, players keep the orders of belief in rationality that
are per se compatible with the observed behavior, and drop the incompatible
beliefs in the theories. Thus, Selective Rationalizability captures Common
Strong Belief in Rationality (Battigalli and Siniscalchi, 2002) and refines
Extensive-Form Rationalizability (Pearce, 1984; BS, 2002), whereas
Strong-$\Delta$-Rationalizability (Battigalli, 2003; Battigalli and
Siniscalchi, 2003) captures the opposite epistemic priority choice. Selective
Rationalizability can be extended to encompass richer epistemic priority
orderings among different theories of opponents' behavior. This allows to
establish a surprising connection with strategic stability (Kohlberg and
Mertens, 1986).
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Communications for Wearable Devices | Wearable devices are transforming computing and the human-computer
interaction and they are a primary means for motion recognition of reflexive
systems. We review basic wearable deployments and their open wireless
communications. An algorithm that uses accelerometer data to provide a control
and communication signal is described. Challenges in the further deployment of
wearable device in the field of body area network and biometric verification
are discussed.
| 1 | 0 | 0 | 0 | 0 | 0 |
Nonlocal Nonlinear Schrödinger Equations and Their Soliton Solutions | We study standard and nonlocal nonlinear Schrödinger (NLS) equations
obtained from the coupled NLS system of equations (Ablowitz-Kaup-Newell-Segur
(AKNS) equations) by using standard and nonlocal reductions respectively. By
using the Hirota bilinear method we first find soliton solutions of the coupled
NLS system of equations then using the reduction formulas we find the soliton
solutions of the standard and nonlocal NLS equations. We give examples for
particular values of the parameters and plot the function $|q(t,x)|^2$ for the
standard and nonlocal NLS equations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning | Prediction is an appealing objective for self-supervised learning of
behavioral skills, particularly for autonomous robots. However, effectively
utilizing predictive models for control, especially with raw image inputs,
poses a number of major challenges. How should the predictions be used? What
happens when they are inaccurate? In this paper, we tackle these questions by
proposing a method for learning robotic skills from raw image observations,
using only autonomously collected experience. We show that even an imperfect
model can complete complex tasks if it can continuously retry, but this
requires the model to not lose track of the objective (e.g., the object of
interest). To enable a robot to continuously retry a task, we devise a
self-supervised algorithm for learning image registration, which can keep track
of objects of interest for the duration of the trial. We demonstrate that this
idea can be combined with a video-prediction based controller to enable complex
behaviors to be learned from scratch using only raw visual inputs, including
grasping, repositioning objects, and non-prehensile manipulation. Our
real-world experiments demonstrate that a model trained with 160 robot hours of
autonomously collected, unlabeled data is able to successfully perform complex
manipulation tasks with a wide range of objects not seen during training.
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S-Isomap++: Multi Manifold Learning from Streaming Data | Manifold learning based methods have been widely used for non-linear
dimensionality reduction (NLDR). However, in many practical settings, the need
to process streaming data is a challenge for such methods, owing to the high
computational complexity involved. Moreover, most methods operate under the
assumption that the input data is sampled from a single manifold, embedded in a
high dimensional space. We propose a method for streaming NLDR when the
observed data is either sampled from multiple manifolds or irregularly sampled
from a single manifold. We show that existing NLDR methods, such as Isomap,
fail in such situations, primarily because they rely on smoothness and
continuity of the underlying manifold, which is violated in the scenarios
explored in this paper. However, the proposed algorithm is able to learn
effectively in presence of multiple, and potentially intersecting, manifolds,
while allowing for the input data to arrive as a massive stream.
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Extreme radio-wave scattering associated with hot stars | We use data on extreme radio scintillation to demonstrate that this
phenomenon is associated with hot stars in the solar neighbourhood. The ionized
gas responsible for the scattering is found at distances up to 1.75pc from the
host star, and on average must comprise 1.E5 distinct structures per star. We
detect azimuthal velocities of the plasma, relative to the host star, up to 9.7
km/s, consistent with warm gas expanding at the sound speed. The circumstellar
plasma structures that we infer are similar in several respects to the cometary
knots seen in the Helix, and in other planetary nebulae. There the ionized gas
appears as a skin around tiny molecular clumps. Our analysis suggests that
molecular clumps are ubiquitous circumstellar features, unrelated to the
evolutionary state of the star. The total mass in such clumps is comparable to
the stellar mass.
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Tracking network dynamics: a survey of distances and similarity metrics | From longitudinal biomedical studies to social networks, graphs have emerged
as a powerful framework for describing evolving interactions between agents in
complex systems. In such studies, after pre-processing, the data can be
represented by a set of graphs, each representing a system's state at different
points in time. The analysis of the system's dynamics depends on the selection
of the appropriate analytical tools. After characterizing similarities between
states, a critical step lies in the choice of a distance between graphs capable
of reflecting such similarities. While the literature offers a number of
distances that one could a priori choose from, their properties have been
little investigated and no guidelines regarding the choice of such a distance
have yet been provided. In particular, most graph distances consider that the
nodes are exchangeable and do not take into account node identities. Accounting
for the alignment of the graphs enables us to enhance these distances'
sensitivity to perturbations in the network and detect important changes in
graph dynamics. Thus the selection of an adequate metric is a decisive --yet
delicate--practical matter.
In the spirit of Goldenberg, Zheng and Fienberg's seminal 2009 review, the
purpose of this article is to provide an overview of commonly-used graph
distances and an explicit characterization of the structural changes that they
are best able to capture. We use as a guiding thread to our discussion the
application of these distances to the analysis of both a longitudinal
microbiome dataset and a brain fMRI study. We show examples of using
permutation tests to detect the effect of covariates on the graphs'
variability. Synthetic examples provide intuition as to the qualities and
drawbacks of the different distances. Above all, we provide some guidance for
choosing one distance over another in certain types of applications.
| 1 | 0 | 0 | 1 | 0 | 0 |
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces | Both neural networks and decision trees are popular machine learning methods
and are widely used to solve problems from diverse domains. These two
classifiers are commonly used base classifiers in an ensemble framework. In
this paper, we first present a new variant of oblique decision tree based on a
linear classifier, then construct an ensemble classifier based on the fusion of
a fast neural network, random vector functional link network and oblique
decision trees. Random Vector Functional Link Network has an elegant closed
form solution with extremely short training time. The neural network partitions
each training bag (obtained using bagging) at the root level into C subsets
where C is the number of classes in the dataset and subsequently, C oblique
decision trees are trained on such partitions. The proposed method provides a
rich insight into the data by grouping the confusing or hard to classify
samples for each class and thus, provides an opportunity to employ fine-grained
classification rule over the data. The performance of the ensemble classifier
is evaluated on several multi-class datasets where it demonstrates a superior
performance compared to other state-of- the-art classifiers.
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Graded components of local cohomology modules | Let $A$ be a regular ring containing a field of characteristic zero and let
$R = A[X_1,\ldots, X_m]$. Consider $R$ as standard graded with $deg \ A = 0$
and $deg \ X_i = 1$ for all $i$. In this paper we present a comprehensive study
of graded components of local cohomology modules $H^i_I(R)$ where $I$ is an
\emph{arbitrary} homogeneous ideal in $R$. Our study seems to be the first in
this regard.
| 0 | 0 | 1 | 0 | 0 | 0 |
Drug Selection via Joint Push and Learning to Rank | Selecting the right drugs for the right patients is a primary goal of
precision medicine. In this manuscript, we consider the problem of cancer drug
selection in a learning-to-rank framework. We have formulated the cancer drug
selection problem as to accurately predicting 1). the ranking positions of
sensitive drugs and 2). the ranking orders among sensitive drugs in cancer cell
lines based on their responses to cancer drugs. We have developed a new
learning-to-rank method, denoted as pLETORg , that predicts drug ranking
structures in each cell line via using drug latent vectors and cell line latent
vectors. The pLETORg method learns such latent vectors through explicitly
enforcing that, in the drug ranking list of each cell line, the sensitive drugs
are pushed above insensitive drugs, and meanwhile the ranking orders among
sensitive drugs are correct. Genomics information on cell lines is leveraged in
learning the latent vectors. Our experimental results on a benchmark cell
line-drug response dataset demonstrate that the new pLETORg significantly
outperforms the state-of-the-art method in prioritizing new sensitive drugs.
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Two-dimensional electron gas at the interface of the ferroelectric-antiferromagnetic heterostructure Ba_0.8Sr_0.2TiO_3/LaMnO_3 | The temperature dependence of the electrical resistivity of the
heterostructures consisting of single crystalline LaMnO$_3$ samples with
different crystallographic orientations covered by the epitaxial ferroelectric
Ba$_{0.8}$Sr$_{0.2}$TiO$_3$ film has been studied. Results obtained for the
heterostructure have been compared with the electrical resistivity of the
single crystalline LaMnO$_3$ without the film. It was found that for the
samples with the films where the polarization axis is perpendicular to the
crystal surface the electrical resistivity strongly decreases, and at the
temperature below ~160 K undergoes the insulator-metal transition. Ab-initio
calculations were also performed for the structural and electronic properties
of the BaTiO$_3$/LaMnO$_3$ heterostructure. Transition to the 2D electron gas
at the interface is shown.
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Linear Discriminant Generative Adversarial Networks | We develop a novel method for training of GANs for unsupervised and class
conditional generation of images, called Linear Discriminant GAN (LD-GAN). The
discriminator of an LD-GAN is trained to maximize the linear separability
between distributions of hidden representations of generated and targeted
samples, while the generator is updated based on the decision hyper-planes
computed by performing LDA over the hidden representations. LD-GAN provides a
concrete metric of separation capacity for the discriminator, and we
experimentally show that it is possible to stabilize the training of LD-GAN
simply by calibrating the update frequencies between generators and
discriminators in the unsupervised case, without employment of normalization
methods and constraints on weights. In the class conditional generation tasks,
the proposed method shows improved training stability together with better
generalization performance compared to WGAN that employs an auxiliary
classifier.
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Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy | Monoclonal antibodies constitute one of the most important strategies to
treat patients suffering from cancers such as hematological malignancies and
solid tumors. In order to guarantee the quality of those preparations prepared
at hospital, quality control has to be developed. The aim of this study was to
explore a noninvasive, nondestructive, and rapid analytical method to ensure
the quality of the final preparation without causing any delay in the process.
We analyzed four mAbs (Inlfiximab, Bevacizumab, Ramucirumab and Rituximab)
diluted at therapeutic concentration in chloride sodium 0.9% using Raman
spectroscopy. To reduce the prediction errors obtained with traditional
chemometric data analysis, we explored a data-driven approach using statistical
machine learning methods where preprocessing and predictive models are jointly
optimized. We prepared a data analytics workflow and submitted the problem to a
collaborative data challenge platform called Rapid Analytics and Model
Prototyping (RAMP). This allowed to use solutions from about 300 data
scientists during five days of collaborative work. The prediction of the four
mAbs samples was considerably improved with a misclassification rate and the
mean error rate of 0.8% and 4%, respectively.
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Sequential noise-induced escapes for oscillatory network dynamics | It is well known that the addition of noise in a multistable system can
induce random transitions between stable states. The rate of transition can be
characterised in terms of the noise-free system's dynamics and the added noise:
for potential systems in the presence of asymptotically low noise the
well-known Kramers' escape time gives an expression for the mean escape time.
This paper examines some general properties and examples of transitions between
local steady and oscillatory attractors within networks: the transition rates
at each node may be affected by the dynamics at other nodes. We use first
passage time theory to explain some properties of scalings noted in the
literature for an idealised model of initiation of epileptic seizures in small
systems of coupled bistable systems with both steady and oscillatory
attractors. We focus on the case of sequential escapes where a steady attractor
is only marginally stable but all nodes start in this state. As the nodes
escape to the oscillatory regime, we assume that the transitions back are very
infrequent in comparison. We quantify and characterise the resulting sequences
of noise-induced escapes. For weak enough coupling we show that a master
equation approach gives a good quantitative understanding of sequential
escapes, but for strong coupling this description breaks down.
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Confluence of Conditional Term Rewrite Systems via Transformations | Conditional term rewriting is an intuitive yet complex extension of term
rewriting. In order to benefit from the simpler framework of unconditional
rewriting, transformations have been defined to eliminate the conditions of
conditional term rewrite systems.
Recent results provide confluence criteria for conditional term rewrite
systems via transformations, yet they are restricted to CTRSs with certain
syntactic properties like weak left-linearity. These syntactic properties imply
that the transformations are sound for the given CTRS.
This paper shows how to use transformations to prove confluence of
operationally terminating, right-stable deterministic conditional term rewrite
systems without the necessity of soundness restrictions. For this purpose, it
is shown that certain rewrite strategies, in particular almost U-eagerness and
innermost rewriting, always imply soundness.
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Observation of Skyrmions at Room Temperature in Co2FeAl Heusler Alloy Ultrathin Films | Magnetic skyrmions are topological spin structures having immense potential
for energy efficient spintronic devices. However, observations of skyrmions at
room temperature are limited to patterned nanostructures. Here, we report the
observation of stable skyrmions in unpatterned Ta/Co2FeAl(CFA)/MgO thin film
heterostructures at room temperature and in zero external magnetic field
employing magnetic force microscopy. The skyrmions are observed in a trilayer
structure comprised of heavy metal (HM)/ferromagnet (FM)/Oxide interfaces which
result in strong interfacial Dzyaloshinskii-Moriya interaction (i-DMI) as
evidenced by Brillouin light scattering measurements, in agreement with the
results of micromagnetic simulations. We also emphasize on room temperature
observation of multiple skyrmions which can be stabilized for suitable choices
of CFA layer thickness, perpendicular magnetic anisotropy, and i-DMI. These
results open up a new paradigm for designing room temperature spintronic
devices based on skyrmions in FM continuous thin films.
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Bayesian Nonparametric Spectral Estimation | Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a
time series) is distributed across different frequencies. This can become
particularly challenging when only partial and noisy observations of the signal
are available, where current methods fail to handle uncertainty appropriately.
In this context, we propose a joint probabilistic model for signals,
observations and spectra, where SE is addressed as an exact inference problem.
Assuming a Gaussian process prior over the signal, we apply Bayes' rule to find
the analytic posterior distribution of the spectrum given a set of
observations. Besides its expressiveness and natural account of spectral
uncertainty, the proposed model also provides a functional-form representation
of the power spectral density, which can be optimised efficiently. Comparison
with previous approaches, in particular against Lomb-Scargle, is addressed
theoretically and also experimentally in three different scenarios. Code and
demo available at this https URL.
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Imprecise dynamic walking with time-projection control | We present a new walking foot-placement controller based on 3LP, a 3D model
of bipedal walking that is composed of three pendulums to simulate falling,
swing and torso dynamics. Taking advantage of linear equations and closed-form
solutions of the 3LP model, our proposed controller projects intermediate
states of the biped back to the beginning of the phase for which a discrete LQR
controller is designed. After the projection, a proper control policy is
generated by this LQR controller and used at the intermediate time. This
control paradigm reacts to disturbances immediately and includes rules to
account for swing dynamics and leg-retraction. We apply it to a simulated Atlas
robot in position-control, always commanded to perform in-place walking. The
stance hip joint in our robot keeps the torso upright to let the robot
naturally fall, and the swing hip joint tracks the desired footstep location.
Combined with simple Center of Pressure (CoP) damping rules in the low-level
controller, our foot-placement enables the robot to recover from strong pushes
and produce periodic walking gaits when subject to persistent sources of
disturbance, externally or internally. These gaits are imprecise, i.e.,
emergent from asymmetry sources rather than precisely imposing a desired
velocity to the robot. Also in extreme conditions, restricting linearity
assumptions of the 3LP model are often violated, but the system remains robust
in our simulations. An extensive analysis of closed-loop eigenvalues, viable
regions and sensitivity to push timings further demonstrate the strengths of
our simple controller.
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Interplay of dilution and magnetic field in the nearest-neighbor spin-ice model on the pyrochlore lattice | We study the magnetic field effects on the diluted spin-ice materials using
the replica-exchange Monte Carlo simulation. We observe five plateaus in the
magnetization curve of the diluted nearest-neighbor spin-ice model on the
pyrochlore lattice when a magnetic field is applied in the [111] direction.
This is in contrast to the case of the pure model with two plateaus. The origin
of five plateaus is investigated from the spin configuration of two
corner-sharing tetrahedra in the case of the diluted model.
| 0 | 1 | 0 | 0 | 0 | 0 |
RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process | An RNN-based forecasting approach is used to early detect anomalies in
industrial multivariate time series data from a simulated Tennessee Eastman
Process (TEP) with many cyber-attacks. This work continues a previously
proposed LSTM-based approach to the fault detection in simpler data. It is
considered necessary to adapt the RNN network to deal with data containing
stochastic, stationary, transitive and a rich variety of anomalous behaviours.
There is particular focus on early detection with special NAB-metric. A
comparison with the DPCA approach is provided. The generated data set is made
publicly available.
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Modelling of limitations of bulk heterojunction architecture in organic solar cells | Polymer solar cells are considered as very promising candidates for
development of photovoltaics of the future. They are cheap and easy to
fabricate, however, up to now, they possess fundamental drawback, low
effectiveness. In the most popular BHJ (bulk heterojunction) architecture the
actual record of efficiency is about 13 percent. One ask the question how
fundamental this limitation is. In our paper we propose the simple model which
examines the limitations of efficiency by analysis of geometrical aspects of
the BHJ architecture. In this paper we considered two dimensional model. We
calculated the effective length of the donor-acceptor border in the random
mixture of donor and acceptor nanocrystals and further compared it with an
ideal comb architecture. It turns out that in the BHJ architecture, this
effective length is about 2 times smaller than in the comb architecture.
| 0 | 1 | 0 | 0 | 0 | 0 |
Detecting singular weak-dissipation limit for flutter onset in reversible systems | A `flutter machine' is introduced for the investigation of a singular
interface between the classical and reversible Hopf bifurcations that is
theoretically predicted to be generic in nonconservative reversible systems
with vanishing dissipation. In particular, such a singular interface exists for
the Pflüger viscoelastic column moving in a resistive medium, which is proven
by means of the perturbation theory of multiple eigenvalues with the Jordan
block. The laboratory setup, consisting of a cantilevered viscoelastic rod
loaded by a positional force with non-zero curl produced by dry friction,
demonstrates high sensitivity of the classical Hopf bifurcation onset {to the
ratio between} the weak air drag and Kelvin-Voigt damping in the Pflüger
column. Thus, the Whitney umbrella singularity is experimentally confirmed,
responsible for discontinuities accompanying dissipation-induced instabilities
in a broad range of physical contexts.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Hand Combining Two Simple Grippers to Pick up and Arrange Objects for Assembly | This paper proposes a novel robotic hand design for assembly tasks. The idea
is to combine two simple grippers -- an inner gripper which is used for precise
alignment, and an outer gripper which is used for stable holding. Conventional
robotic hands require complicated compliant mechanisms or complicated control
strategy and force sensing to conduct assemble tasks, which makes them costly
and difficult to pick and arrange small objects like screws or washers.
Compared to the conventional hands, the proposed design provides a low-cost
solution for aligning, picking up, and arranging various objects by taking
advantages of the geometric constraints of the positioning fingers and gravity.
It is able to deal with small screws and washers, and eliminate the position
errors of cylindrical objects or objects with cylindrical holes. In the
experiments, both real-world tasks and quantitative analysis are performed to
validate the aligning, picking, and arrangements abilities of the design.
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A Time Hierarchy Theorem for the LOCAL Model | The celebrated Time Hierarchy Theorem for Turing machines states, informally,
that more problems can be solved given more time. The extent to which a time
hierarchy-type theorem holds in the distributed LOCAL model has been open for
many years. It is consistent with previous results that all natural problems in
the LOCAL model can be classified according to a small constant number of
complexities, such as $O(1),O(\log^* n), O(\log n), 2^{O(\sqrt{\log n})}$, etc.
In this paper we establish the first time hierarchy theorem for the LOCAL
model and prove that several gaps exist in the LOCAL time hierarchy.
1. We define an infinite set of simple coloring problems called Hierarchical
$2\frac{1}{2}$-Coloring}. A correctly colored graph can be confirmed by simply
checking the neighborhood of each vertex, so this problem fits into the class
of locally checkable labeling (LCL) problems. However, the complexity of the
$k$-level Hierarchical $2\frac{1}{2}$-Coloring problem is $\Theta(n^{1/k})$,
for $k\in\mathbb{Z}^+$. The upper and lower bounds hold for both general graphs
and trees, and for both randomized and deterministic algorithms.
2. Consider any LCL problem on bounded degree trees. We prove an
automatic-speedup theorem that states that any randomized $n^{o(1)}$-time
algorithm solving the LCL can be transformed into a deterministic $O(\log
n)$-time algorithm. Together with a previous result, this establishes that on
trees, there are no natural deterministic complexities in the ranges
$\omega(\log^* n)$---$o(\log n)$ or $\omega(\log n)$---$n^{o(1)}$.
3. We expose a gap in the randomized time hierarchy on general graphs. Any
randomized algorithm that solves an LCL problem in sublogarithmic time can be
sped up to run in $O(T_{LLL})$ time, which is the complexity of the distributed
Lovasz local lemma problem, currently known to be $\Omega(\log\log n)$ and
$O(\log n)$.
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Second sound in systems of one-dimensional fermions | We study sound in Galilean invariant systems of one-dimensional fermions. At
low temperatures, we find a broad range of frequencies in which in addition to
the waves of density there is a second sound corresponding to ballistic
propagation of heat in the system. The damping of the second sound mode is
weak, provided the frequency is large compared to a relaxation rate that is
exponentially small at low temperatures. At lower frequencies the second sound
mode is damped, and the propagation of heat is diffusive.
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The equational theory of the natural join and inner union is decidable | The natural join and the inner union operations combine relations of a
database. Tropashko and Spight [24] realized that these two operations are the
meet and join operations in a class of lattices, known by now as the relational
lattices. They proposed then lattice theory as an algebraic approach to the
theory of databases, alternative to the relational algebra. Previous works [17,
22] proved that the quasiequational theory of these lattices-that is, the set
of definite Horn sentences valid in all the relational lattices-is undecidable,
even when the signature is restricted to the pure lattice signature. We prove
here that the equational theory of relational lattices is decidable. That, is
we provide an algorithm to decide if two lattice theoretic terms t, s are made
equal under all intepretations in some relational lattice. We achieve this goal
by showing that if an inclusion t $\le$ s fails in any of these lattices, then
it fails in a relational lattice whose size is bound by a triple exponential
function of the sizes of t and s.
| 1 | 0 | 1 | 0 | 0 | 0 |
Doping-induced spin-orbit splitting in Bi-doped ZnO nanowires | Our predictions, based on density-functional calculations, reveal that
surface doping of ZnO nanowires with Bi leads to a linear-in-$k$ splitting of
the conduction-band states, through spin-orbit interaction, due to the lowering
of the symmetry in the presence of the dopant. This finding implies that spin
polarization of the conduction electrons in Bi-doped ZnO nanowires could be
controlled with applied electric (as opposed to magnetic) fields, making them
candidate materials for spin-orbitronic applications. Our findings also show
that the degree of spin splitting could be tuned by adjusting the dopant
concentration. Defect calculations and ab initio molecular dynamics simulations
indicate that stable doping configurations exhibiting the foregoing
linear-in-$k$ splitting could be realized under reasonable thermodynamic
conditions.
| 0 | 1 | 0 | 0 | 0 | 0 |
A robotic vision system to measure tree traits | The autonomous measurement of tree traits, such as branching structure,
branch diameters, branch lengths, and branch angles, is required for tasks such
as robotic pruning of trees as well as structural phenotyping. We propose a
robotic vision system called the Robotic System for Tree Shape Estimation
(RoTSE) to determine tree traits in field settings. The process is composed of
the following stages: image acquisition with a mobile robot unit, segmentation,
reconstruction, curve skeletonization, conversion to a graph representation,
and then computation of traits. Quantitative and qualitative results on apple
trees are shown in terms of accuracy, computation time, and robustness.
Compared to ground truth measurements, the RoTSE produced the following
estimates: branch diameter (mean-squared error $0.99$ mm), branch length
(mean-squared error $45.64$ mm), and branch angle (mean-squared error $10.36$
degrees). The average run time was 8.47 minutes when the voxel resolution was
$3$ mm$^3$.
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Resolution enhancement in in-line holography by numerical compensation of vibrations | Mechanical vibrations of components of the optical system is one of the
sources of blurring of interference pattern in coherent imaging systems. The
problem is especially important in holography where the resolution of the
reconstructed objects depends on the effective size of the hologram, that is on
the extent of the interference pattern, and on the contrast of the interference
fringes. We discuss the mathematical relation between the vibrations, the
hologram contrast and the reconstructed object. We show how vibrations can be
post-filtered out from the hologram or from the reconstructed object assuming a
Gaussian distribution of the vibrations. We also provide a numerical example of
compensation for directional motion blur. We demonstrate our approach for light
optical and electron holograms, acquired with both, plane- as well as
spherical-waves. As a result of such hologram deblurring, the resolution of the
reconstructed objects is enhanced by almost a factor of 2. We believe that our
approach opens up a new venue of post-experimental resolution enhancement in
in-line holography by adapting the rich database/catalogue of motion deblurring
algorithms developed for photography and image restoration applications.
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Embodied Question Answering | We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA.
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Toward Unsupervised Text Content Manipulation | Controlled generation of text is of high practical use. Recent efforts have
made impressive progress in generating or editing sentences with given textual
attributes (e.g., sentiment). This work studies a new practical setting of text
content manipulation. Given a structured record, such as `(PLAYER: Lebron,
POINTS: 20, ASSISTS: 10)', and a reference sentence, such as `Kobe easily
dropped 30 points', we aim to generate a sentence that accurately describes the
full content in the record, with the same writing style (e.g., wording,
transitions) of the reference. The problem is unsupervised due to lack of
parallel data in practice, and is challenging to minimally yet effectively
manipulate the text (by rewriting/adding/deleting text portions) to ensure
fidelity to the structured content. We derive a dataset from a basketball game
report corpus as our testbed, and develop a neural method with unsupervised
competing objectives and explicit content coverage constraints. Automatic and
human evaluations show superiority of our approach over competitive methods
including a strong rule-based baseline and prior approaches designed for style
transfer.
| 1 | 0 | 0 | 0 | 0 | 0 |
Unsupervised learning of object landmarks by factorized spatial embeddings | Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations, as induced by a viewpoint change or an object
deformation, by learning a deep neural network that detects landmarks
consistently with such visual effects. Furthermore, we show that the learned
landmarks establish meaningful correspondences between different object
instances in a category without having to impose this requirement explicitly.
We assess the method qualitatively on a variety of object types, natural and
man-made. We also show that our unsupervised landmarks are highly predictive of
manually-annotated landmarks in face benchmark datasets, and can be used to
regress these with a high degree of accuracy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Data-Efficient Design Exploration through Surrogate-Assisted Illumination | Design optimization techniques are often used at the beginning of the design
process to explore the space of possible designs. In these domains illumination
algorithms, such as MAP-Elites, are promising alternatives to classic
optimization algorithms because they produce diverse, high-quality solutions in
a single run, instead of only a single near-optimal solution. Unfortunately,
these algorithms currently require a large number of function evaluations,
limiting their applicability. In this article we introduce a new illumination
algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate
modeling techniques to create a map of the design space according to
user-defined features while minimizing the number of fitness evaluations. On a
2-dimensional airfoil optimization problem SAIL produces hundreds of diverse
but high-performing designs with several orders of magnitude fewer evaluations
than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of
producing maps of high-performing designs in realistic 3-dimensional
aerodynamic tasks with an accurate flow simulation. Data-efficient design
exploration with SAIL can help designers understand what is possible, beyond
what is optimal, by considering more than pure objective-based optimization.
| 0 | 0 | 0 | 1 | 0 | 0 |
Stochastic seismic waveform inversion using generative adversarial networks as a geological prior | We present an application of deep generative models in the context of
partial-differential equation (PDE) constrained inverse problems. We combine a
generative adversarial network (GAN) representing an a priori model that
creates subsurface geological structures and their petrophysical properties,
with the numerical solution of the PDE governing the propagation of acoustic
waves within the earth's interior. We perform Bayesian inversion using an
approximate Metropolis-adjusted Langevin algorithm (MALA) to sample from the
posterior given seismic observations. Gradients with respect to the model
parameters governing the forward problem are obtained by solving the adjoint of
the acoustic wave equation. Gradients of the mismatch with respect to the
latent variables are obtained by leveraging the differentiable nature of the
deep neural network used to represent the generative model. We show that
approximate MALA sampling allows efficient Bayesian inversion of model
parameters obtained from a prior represented by a deep generative model,
obtaining a diverse set of realizations that reflect the observed seismic
response.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Capillary Surface with No Radial Limits | In 1996, Kirk Lancaster and David Siegel investigated the existence and
behavior of radial limits at a corner of the boundary of the domain of
solutions of capillary and other prescribed mean curvature problems with
contact angle boundary data. In Theorem 3, they provide an example of a
capillary surface in a unit disk $D$ which has no radial limits at
$(0,0)\in\partial D.$ In their example, the contact angle ($\gamma$) cannot be
bounded away from zero and $\pi.$
Here we consider a domain $\Omega$ with a convex corner at $(0,0)$ and find a
capillary surface $z=f(x,y)$ in $\Omega\times\mathbb{R}$ which has no radial
limits at $(0,0)\in\partial\Omega$ such that $\gamma$ is bounded away from $0$
and $\pi.$
| 0 | 0 | 1 | 0 | 0 | 0 |
Brain Computer Interface for Gesture Control of a Social Robot: an Offline Study | Brain computer interface (BCI) provides promising applications in
neuroprosthesis and neurorehabilitation by controlling computers and robotic
devices based on the patient's intentions. Here, we have developed a novel BCI
platform that controls a personalized social robot using noninvasively acquired
brain signals. Scalp electroencephalogram (EEG) signals are collected from a
user in real-time during tasks of imaginary movements. The imagined body
kinematics are decoded using a regression model to calculate the user-intended
velocity. Then, the decoded kinematic information is mapped to control the
gestures of a social robot. The platform here may be utilized as a
human-robot-interaction framework by combining with neurofeedback mechanisms to
enhance the cognitive capability of persons with dementia.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multi-agent Gaussian Process Motion Planning via Probabilistic Inference | This paper deals with motion planning for multiple agents by representing the
problem as a simultaneous optimization of every agent's trajectory. Each
trajectory is considered as a sample from a one-dimensional continuous-time
Gaussian process (GP) generated by a linear time-varying stochastic
differential equation driven by white noise. By formulating the planning
problem as probabilistic inference on a factor graph, the structure of the
pertaining GP can be exploited to find the solution efficiently using numerical
optimization. In contrast to planning each agent's trajectory individually,
where only the current poses of other agents are taken into account, we propose
simultaneous planning of multiple trajectories that works in a predictive
manner. It takes into account the information about each agent's whereabouts at
every future time instant, since full trajectories of each agent are found
jointly during a single optimization procedure. We compare the proposed method
to an individual trajectory planning approach, demonstrating significant
improvement in both success rate and computational efficiency.
| 1 | 0 | 0 | 0 | 0 | 0 |
Faster Algorithms for Mean-Payoff Parity Games | Graph games provide the foundation for modeling and synthesis of reactive
processes. Such games are played over graphs where the vertices are controlled
by two adversarial players. We consider graph games where the objective of the
first player is the conjunction of a qualitative objective (specified as a
parity condition) and a quantitative objective (specified as a mean-payoff
condition). There are two variants of the problem, namely, the threshold
problem where the quantitative goal is to ensure that the mean-payoff value is
above a threshold, and the value problem where the quantitative goal is to
ensure the optimal mean-payoff value; in both cases ensuring the qualitative
parity objective. The previous best-known algorithms for game graphs with $n$
vertices, $m$ edges, parity objectives with $d$ priorities, and maximal
absolute reward value $W$ for mean-payoff objectives, are as follows:
$O(n^{d+1} \cdot m \cdot W)$ for the threshold problem, and $O(n^{d+2} \cdot m
\cdot W)$ for the value problem. Our main contributions are faster algorithms,
and the running times of our algorithms are as follows: $O(n^{d-1} \cdot m
\cdot W)$ for the threshold problem, and $O(n^{d} \cdot m \cdot W \cdot \log
(n\cdot W))$ for the value problem. For mean-payoff parity objectives with two
priorities, our algorithms match the best-known bounds of the algorithms for
mean-payoff games (without conjunction with parity objectives). Our results are
relevant in synthesis of reactive systems with both functional requirement
(given as a qualitative objective) and performance requirement (given as a
quantitative objective).
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Similarity Function Tracking using Pairwise Comparisons | Recent work in distance metric learning has focused on learning
transformations of data that best align with specified pairwise similarity and
dissimilarity constraints, often supplied by a human observer. The learned
transformations lead to improved retrieval, classification, and clustering
algorithms due to the better adapted distance or similarity measures. Here, we
address the problem of learning these transformations when the underlying
constraint generation process is nonstationary. This nonstationarity can be due
to changes in either the ground-truth clustering used to generate constraints
or changes in the feature subspaces in which the class structure is apparent.
We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD),
a general adaptive, online approach for learning and tracking optimal metrics
as they change over time that is highly robust to a variety of nonstationary
behaviors in the changing metric. We apply the OCELAD framework to an ensemble
of online learners. Specifically, we create a retro-initialized composite
objective mirror descent (COMID) ensemble (RICE) consisting of a set of
parallel COMID learners with different learning rates, and demonstrate
parameter-free RICE-OCELAD metric learning on both synthetic data and a highly
nonstationary Twitter dataset. We show significant performance improvements and
increased robustness to nonstationary effects relative to previously proposed
batch and online distance metric learning algorithms.
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Deep Boosted Regression for MR to CT Synthesis | Attenuation correction is an essential requirement of positron emission
tomography (PET) image reconstruction to allow for accurate quantification.
However, attenuation correction is particularly challenging for PET-MRI as
neither PET nor magnetic resonance imaging (MRI) can directly image tissue
attenuation properties. MRI-based computed tomography (CT) synthesis has been
proposed as an alternative to physics based and segmentation-based approaches
that assign a population-based tissue density value in order to generate an
attenuation map. We propose a novel deep fully convolutional neural network
that generates synthetic CTs in a recursive manner by gradually reducing the
residuals of the previous network, increasing the overall accuracy and
generalisability, while keeping the number of trainable parameters within
reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT
pairs and a four-fold random bootstrapped validation with a 80:20 split is
performed. Quantitative results show that the proposed framework outperforms a
state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE)
from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction
error from 14.3% to 7.2%.
| 0 | 0 | 0 | 1 | 0 | 0 |
Discretisation of regularity structures | We introduce a general framework allowing to apply the theory of regularity
structures to discretisations of stochastic PDEs. The approach pursued in this
article is that we do not focus on any one specific discretisation procedure.
Instead, we assume that we are given a scale $\varepsilon > 0$ and a "black
box" describing the behaviour of our discretised objects at scales below
$\varepsilon $.
| 0 | 0 | 1 | 0 | 0 | 0 |
Optimization of Wireless Power Transfer Systems Enhanced by Passive Elements and Metasurfaces | This paper presents a rigorous optimization technique for wireless power
transfer (WPT) systems enhanced by passive elements, ranging from simple
reflectors and intermedi- ate relays all the way to general electromagnetic
guiding and focusing structures, such as metasurfaces and metamaterials. At its
core is a convex semidefinite relaxation formulation of the otherwise nonconvex
optimization problem, of which tightness and optimality can be confirmed by a
simple test of its solutions. The resulting method is rigorous, versatile, and
general -- it does not rely on any assumptions. As shown in various examples,
it is able to efficiently and reliably optimize such WPT systems in order to
find their physical limitations on performance, optimal operating parameters
and inspect their working principles, even for a large number of active
transmitters and passive elements.
| 1 | 0 | 1 | 0 | 0 | 0 |
Sharpening Jensen's Inequality | This paper proposes a new sharpened version of the Jensen's inequality. The
proposed new bound is simple and insightful, is broadly applicable by imposing
minimum assumptions, and provides fairly accurate result in spite of its simple
form. Applications to the moment generating function, power mean inequalities,
and Rao-Blackwell estimation are presented. This presentation can be
incorporated in any calculus-based statistical course.
| 0 | 0 | 1 | 1 | 0 | 0 |
Knotted solutions, from electromagnetism to fluid dynamics | Knotted solutions to electromagnetism and fluid dynamics are investigated,
based on relations we find between the two subjects. We can write fluid
dynamics in electromagnetism language, but only on an initial surface, or for
linear perturbations, and we use this map to find knotted fluid solutions, as
well as new electromagnetic solutions. We find that knotted solutions of
Maxwell electromagnetism are also solutions of more general nonlinear theories,
like Born-Infeld, and including ones which contain quantum corrections from
couplings with other modes, like Euler-Heisenberg and string theory DBI. Null
configurations in electromagnetism can be described as a null pressureless
fluid, and from this map we can find null fluid knotted solutions. A type of
nonrelativistic reduction of the relativistic fluid equations is described,
which allows us to find also solutions of the (nonrelativistic) Euler's
equations.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the intersection graph of ideals of $\mathbb{Z}_m$ | Let $m>1$ be an integer, and let $I(\mathbb{Z}_m)^*$ be the set of all
non-zero proper ideals of $\mathbb{Z}_m$. The intersection graph of ideals of
$\mathbb{Z}_m$, denoted by $G(\mathbb{Z}_m)$, is a graph with vertices
$I(\mathbb{Z}_m)^*$ and two distinct vertices $I,J\in I(\mathbb{Z}_m)^*$ are
adjacent if and only if $I\cap J\neq 0$. Let $n>1$ be an integer and
$\mathbb{Z}_n$ be a $\mathbb{Z}_m$-module. In this paper, we introduce and
study a kind of graph structure of $\mathbb{Z}_m$, denoted by
$G_n(\mathbb{Z}_m)$. It is the undirected graph with the vertex set
$I(\mathbb{Z}_m)^*$, and two distinct vertices $I$ and $J$ are adjacent if and
only if $I\mathbb{Z}_n\cap J\mathbb{Z}_n\neq 0$. Clearly,
$G_m(\mathbb{Z}_m)=G(\mathbb{Z}_m)$. We obtain some graph theoretical
properties of $G_n(\mathbb{Z}_m)$ and we compute some of its numerical
invariants, namely girth, independence number, domination number, maximum
degree and chromatic index. We also determine all integer numbers $n$ and $m$
for which $G_n(\mathbb{Z}_m)$ is Eulerian.
| 0 | 0 | 1 | 0 | 0 | 0 |
Optimization of Executable Formal Interpreters developed in Higher-order Theorem Proving Systems | In recent publications, we presented a novel formal symbolic process virtual
machine (FSPVM) framework that combined higher-order theorem proving and
symbolic execution for verifying the reliability and security of smart
contracts developed in the Ethereum blockchain system without suffering the
standard issues surrounding reusability, consistency, and automation. A
specific FSPVM, denoted as FSPVM-E, was developed in Coq based on a general,
extensible, and reusable formal memory (GERM) framework, an extensible and
universal formal intermediate programming language, denoted as Lolisa, which is
a large subset of the Solidity programming language that uses generalized
algebraic datatypes, and a corresponding formally verified interpreter for
Lolisa, denoted as FEther, which serves as a crucial component of FSPVM-E.
However, our past work has demonstrated that the execution efficiency of the
standard development of FEther is extremely low. As a result, FSPVM-E fails to
achieve its expected verification effect. The present work addresses this issue
by first identifying three root causes of the low execution efficiency of
formal interpreters. We then build abstract models of these causes, and present
respective optimization schemes for rectifying the identified conditions.
Finally, we apply these optimization schemes to FEther, and demonstrate that
its execution efficiency has been improved significantly.
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Healthcare Robotics | Robots have the potential to be a game changer in healthcare: improving
health and well-being, filling care gaps, supporting care givers, and aiding
health care workers. However, before robots are able to be widely deployed, it
is crucial that both the research and industrial communities work together to
establish a strong evidence-base for healthcare robotics, and surmount likely
adoption barriers. This article presents a broad contextualization of robots in
healthcare by identifying key stakeholders, care settings, and tasks; reviewing
recent advances in healthcare robotics; and outlining major challenges and
opportunities to their adoption.
| 1 | 0 | 0 | 0 | 0 | 0 |
Personalized Thread Recommendation for MOOC Discussion Forums | Social learning, i.e., students learning from each other through social
interactions, has the potential to significantly scale up instruction in online
education. In many cases, such as in massive open online courses (MOOCs),
social learning is facilitated through discussion forums hosted by course
providers. In this paper, we propose a probabilistic model for the process of
learners posting on such forums, using point processes. Different from existing
works, our method integrates topic modeling of the post text, timescale
modeling of the decay in post activity over time, and learner topic interest
modeling into a single model, and infers this information from user data. Our
method also varies the excitation levels induced by posts according to the
thread structure, to reflect typical notification settings in discussion
forums. We experimentally validate the proposed model on three real-world MOOC
datasets, with the largest one containing up to 6,000 learners making 40,000
posts in 5,000 threads. Results show that our model excels at thread
recommendation, achieving significant improvement over a number of baselines,
thus showing promise of being able to direct learners to threads that they are
interested in more efficiently. Moreover, we demonstrate analytics that our
model parameters can provide, such as the timescales of different topic
categories in a course.
| 1 | 0 | 0 | 1 | 0 | 0 |
CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model | Background: Widespread adoption of electronic health records (EHRs) has
enabled secondary use of EHR data for clinical research and healthcare
delivery. Natural language processing (NLP) techniques have shown promise in
their capability to extract the embedded information in unstructured clinical
data, and information retrieval (IR) techniques provide flexible and scalable
solutions that can augment the NLP systems for retrieving and ranking relevant
records. Methods: In this paper, we present the implementation of Cohort
Retrieval Enhanced by Analysis of Text from EHRs (CREATE), a cohort retrieval
system that can execute textual cohort selection queries on both structured and
unstructured EHR data. CREATE is a proof-of-concept system that leverages a
combination of structured queries and IR techniques on NLP results to improve
cohort retrieval performance while adopting the Observational Medical Outcomes
Partnership (OMOP) Common Data Model (CDM) to enhance model portability. The
NLP component empowered by cTAKES is used to extract CDM concepts from textual
queries. We design a hierarchical index in Elasticsearch to support CDM concept
search utilizing IR techniques and frameworks. Results: Our case study on 5
cohort identification queries evaluated using the IR metric, P@5 (Precision at
5) at both the patient-level and document-level, demonstrates that CREATE
achieves an average P@5 of 0.90, which outperforms systems using only
structured data or only unstructured data with average P@5s of 0.54 and 0.74,
respectively.
| 1 | 0 | 0 | 0 | 0 | 0 |
The same strain of Piscine orthoreovirus (PRV-1) is involved with the development of different, but related, diseases in Atlantic and Pacific Salmon in British Columbia | Piscine orthoreovirus Strain PRV-1 is the causative agent of heart and
skeletal muscle inflammation (HSMI) in Atlantic salmon (Salmo salar). Given its
high prevalence in net pen salmon, debate has arisen on whether PRV poses a
risk to migratory salmon, especially in British Columbia (BC) where
commercially important wild Pacific salmon are in decline. Various strains of
PRV have been associated with diseases in Pacific salmon, including
erythrocytic inclusion body syndrome (EIBS), HSMI-like disease, and
jaundice/anemia in Japan, Norway, Chile and Canada. We examine the
developmental pathway of HSMI and jaundice/anemia associated with PRV-1 in
farmed Atlantic and Chinook (Oncorhynchus tshawytscha) salmon in BC,
respectively. In situ hybridization localized PRV-1 within developing lesions
in both diseases. The two diseases showed dissimilar pathological pathways,
with inflammatory lesions in heart and skeletal muscle in Atlantic salmon, and
degenerative-necrotic lesions in kidney and liver in Chinook salmon, plausibly
explained by differences in PRV load tolerance in red blood cells. Viral genome
sequencing revealed no consistent differences in PRV-1 variants intimately
involved in the development of both diseases, suggesting that migratory Chinook
salmon may be at more than a minimal risk of disease from exposure to the high
levels of PRV occurring on salmon farms.
| 0 | 0 | 0 | 0 | 1 | 0 |
Charge transfer and metallicity in LaNiO$_3$/LaMnO$_3$ superlattices | Motivated by recent experiments, we use the $+U$ extension of the generalized
gradient approximation to density functional theory to study superlattices
composed of alternating layers of LaNiO$_3$ and LaMnO$_3$. For comparison we
also study a rocksalt ((111) double perovskite) structure and bulk LaNiO$_3$
and LaMnO$_3$. A Wannier function analysis indicates that band parameters are
transferable from bulk to superlattice situations with the exception of the
transition metal d-level energy, which has a contribution from the change in
d-shell occupancy. The charge transfer from Mn to Ni is found to be moderate in
the superlattice, indicating metallic behavior, in contrast to the insulating
behavior found in recent experiments, while the rocksalt structure is found to
be insulating with a large Mn-Ni charge transfer. We suggest a high density of
cation antisite defects may account for the insulating behavior experimentally
observed in short-period superlattices.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Learning the $cμ$ Rule in Single and Parallel Server Networks | We consider learning-based variants of the $c \mu$ rule for scheduling in
single and parallel server settings of multi-class queueing systems.
In the single server setting, the $c \mu$ rule is known to minimize the
expected holding-cost (weighted queue-lengths summed over classes and a fixed
time horizon). We focus on the problem where the service rates $\mu$ are
unknown with the holding-cost regret (regret against the $c \mu$ rule with
known $\mu$) as our objective. We show that the greedy algorithm that uses
empirically learned service rates results in a constant holding-cost regret
(the regret is independent of the time horizon). This free exploration can be
explained in the single server setting by the fact that any work-conserving
policy obtains the same number of samples in a busy cycle.
In the parallel server setting, we show that the $c \mu$ rule may result in
unstable queues, even for arrival rates within the capacity region. We then
present sufficient conditions for geometric ergodicity under the $c \mu$ rule.
Using these results, we propose an almost greedy algorithm that explores only
when the number of samples falls below a threshold. We show that this algorithm
delivers constant holding-cost regret because a free exploration condition is
eventually satisfied.
| 1 | 0 | 0 | 0 | 0 | 0 |
Report: Performance comparison between C2075 and P100 GPU cards using cosmological correlation functions | In this report, some cosmological correlation functions are used to evaluate
the differential performance between C2075 and P100 GPU cards. In the past, the
correlation functions used in this work have been widely studied and exploited
on some previous GPU architectures. The analysis of the performance indicates
that a speedup in the range from 13 to 15 is achieved without any additional
optimization process for the P100 card.
| 1 | 1 | 0 | 0 | 0 | 0 |
Crafting Adversarial Examples For Speech Paralinguistics Applications | Computational paralinguistic analysis is increasingly being used in a wide
range of cyber applications, including security-sensitive applications such as
speaker verification, deceptive speech detection, and medical diagnostics.
While state-of-the-art machine learning techniques, such as deep neural
networks, can provide robust and accurate speech analysis, they are susceptible
to adversarial attacks. In this work, we propose an end-to-end scheme to
generate adversarial examples for computational paralinguistic applications by
perturbing directly the raw waveform of an audio recording rather than specific
acoustic features. Our experiments show that the proposed adversarial
perturbation can lead to a significant performance drop of state-of-the-art
deep neural networks, while only minimally impairing the audio quality.
| 1 | 0 | 0 | 1 | 0 | 0 |
On the Analysis of Bacterial Cooperation with a Characterization of 2D Signal Propagation | The exchange of small molecular signals within microbial populations is
generally referred to as quorum sensing (QS). QS is ubiquitous in nature and
enables microorganisms to respond to fluctuations of living environments by
working together. In this work, a QS-based communication system within a
microbial population in a two-dimensional (2D) environment is analytically
modeled. Notably, the diffusion and degradation of signaling molecules within
the population is characterized. Microorganisms are randomly distributed on a
2D circle where each one releases molecules at random times. The number of
molecules observed at each randomly-distributed bacterium is analyzed. Using
this analysis and some approximation, the expected density of cooperating
bacteria is derived. The analytical results are validated via a particle-based
simulation method. The model can be used to predict and control behavioral
dynamics of microscopic populations that have imperfect signal propagation.
| 0 | 0 | 0 | 0 | 1 | 0 |
The Ebb and Flow of Controversial Debates on Social Media | We explore how the polarization around controversial topics evolves on
Twitter - over a long period of time (2011 to 2016), and also as a response to
major external events that lead to increased related activity. We find that
increased activity is typically associated with increased polarization;
however, we find no consistent long-term trend in polarization over time among
the topics we study.
| 1 | 1 | 0 | 0 | 0 | 0 |
Training of Deep Neural Networks based on Distance Measures using RMSProp | The vanishing gradient problem was a major obstacle for the success of deep
learning. In recent years it was gradually alleviated through multiple
different techniques. However the problem was not really overcome in a
fundamental way, since it is inherent to neural networks with activation
functions based on dot products. In a series of papers, we are going to analyze
alternative neural network structures which are not based on dot products. In
this first paper, we revisit neural networks built up of layers based on
distance measures and Gaussian activation functions. These kinds of networks
were only sparsely used in the past since they are hard to train when using
plain stochastic gradient descent methods. We show that by using Root Mean
Square Propagation (RMSProp) it is possible to efficiently learn multi-layer
neural networks. Furthermore we show that when appropriately initialized these
kinds of neural networks suffer much less from the vanishing and exploding
gradient problem than traditional neural networks even for deep networks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression | In high dimension, it is customary to consider Lasso-type estimators to
enforce sparsity. For standard Lasso theory to hold, the regularization
parameter should be proportional to the noise level, yet the latter is
generally unknown in practice. A possible remedy is to consider estimators,
such as the Concomitant/Scaled Lasso, which jointly optimize over the
regression coefficients as well as over the noise level, making the choice of
the regularization independent of the noise level. However, when data from
different sources are pooled to increase sample size, or when dealing with
multimodal datasets, noise levels typically differ and new dedicated estimators
are needed. In this work we provide new statistical and computational solutions
to deal with such heteroscedastic regression models, with an emphasis on
functional brain imaging with combined magneto- and electroencephalographic
(M/EEG) signals. Adopting the formulation of Concomitant Lasso-type estimators,
we propose a jointly convex formulation to estimate both the regression
coefficients and the (square root of the) noise covariance. When our framework
is instantiated to de-correlated noise, it leads to an efficient algorithm
whose computational cost is not higher than for the Lasso and Concomitant
Lasso, while addressing more complex noise structures. Numerical experiments
demonstrate that our estimator yields improved prediction and support
identification while correctly estimating the noise (square root) covariance.
Results on multimodal neuroimaging problems with M/EEG data are also reported.
| 0 | 0 | 1 | 1 | 0 | 0 |
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging | We propose a novel automatic method for accurate segmentation of the prostate
in T2-weighted magnetic resonance imaging (MRI). Our method is based on
convolutional neural networks (CNNs). Because of the large variability in the
shape, size, and appearance of the prostate and the scarcity of annotated
training data, we suggest training two separate CNNs. A global CNN will
determine a prostate bounding box, which is then resampled and sent to a local
CNN for accurate delineation of the prostate boundary. This way, the local CNN
can effectively learn to segment the fine details that distinguish the prostate
from the surrounding tissue using the small amount of available training data.
To fully exploit the training data, we synthesize additional data by deforming
the training images and segmentations using a learned shape model. We apply the
proposed method on the PROMISE12 challenge dataset and achieve state of the art
results. Our proposed method generates accurate, smooth, and artifact-free
segmentations. On the test images, we achieve an average Dice score of 90.6
with a small standard deviation of 2.2, which is superior to all previous
methods. Our two-step segmentation approach and data augmentation strategy may
be highly effective in segmentation of other organs from small amounts of
annotated medical images.
| 1 | 0 | 0 | 1 | 0 | 0 |
A note on signature of Lefschetz fibrations with planar fiber | Using theorems of Eliashberg and McDuff, Etnyre [Et] proved that the
intersection form of a symplectic filling of a contact 3-manifold supported by
planar open book is negative definite.
In this paper, we prove a signature formula for allowable Lefschetz
fibrations over $D^2$ with planar fiber by computing Maslov index appearing in
Wall's non-additivity formula.
The signature formula leads to an alternative proof of Etnyre's theorem via
works of Niederkrüger and Wendl [NWe] and Wendl [We].
Conversely, Etnyre's theorem, together with the existence theorem of Stein
structures on Lefschetz fibrations over $D^2$ with bordered fiber by Loi and
Piergallini [LP], implies the formula.
| 0 | 0 | 1 | 0 | 0 | 0 |
Wild Bootstrapping Rank-Based Procedures: Multiple Testing in Nonparametric Split-Plot Designs | Split-plot or repeated measures designs are frequently used for planning
experiments in the life or social sciences. Typical examples include the
comparison of different treatments over time, where both factors may possess an
additional factorial structure. For such designs, the statistical analysis
usually consists of several steps. If the global null is rejected, multiple
comparisons are usually performed. Usually, general factorial repeated measures
designs are inferred by classical linear mixed models. Common underlying
assumptions, such as normality or variance homogeneity are often not met in
real data. Furthermore, to deal even with, e.g., ordinal or ordered categorical
data, adequate effect sizes should be used. Here, multiple contrast tests and
simultaneous confidence intervals for general factorial split-plot designs are
developed and equipped with a novel asymptotically correct wild bootstrap
approach.
Because the regulatory authorities typically require the calculation of
confidence intervals, this work also provides simultaneous confidence intervals
for single contrasts and for the ratio of different contrasts in meaningful
effects. Extensive simulations are conducted to foster the theoretical
findings. Finally, two different datasets exemplify the applicability of the
novel procedure.
| 0 | 0 | 1 | 1 | 0 | 0 |
Graphical Sequent Calculi for Modal Logics | The syntax of modal graphs is defined in terms of the continuous cut and
broken cut following Charles Peirce's notation in the gamma part of his
graphical logic of existential graphs. Graphical calculi for normal modal
logics are developed based on a reformulation of the graphical calculus for
classical propositional logic. These graphical calculi are of the nature of
deep inference. The relationship between graphical calculi and sequent calculi
for modal logics is shown by translations between graphs and modal formulas.
| 1 | 0 | 0 | 0 | 0 | 0 |
Engineering a flux-dependent mobility edge in disordered zigzag chains | There has been great interest in realizing quantum simulators of charged
particles in artificial gauge fields. Here, we perform the first quantum
simulation explorations of the combination of artificial gauge fields and
disorder. Using synthetic lattice techniques based on parametrically-coupled
atomic momentum states, we engineer zigzag chains with a tunable homogeneous
flux. The breaking of time-reversal symmetry by the applied flux leads to
analogs of spin-orbit coupling and spin-momentum locking, which we observe
directly through the chiral dynamics of atoms initialized to single lattice
sites. We additionally introduce precisely controlled disorder in the site
energy landscape, allowing us to explore the interplay of disorder and large
effective magnetic fields. The combination of correlated disorder and
controlled intra- and inter-row tunneling in this system naturally supports
energy-dependent localization, relating to a single-particle mobility edge. We
measure the localization properties of the extremal eigenstates of this system,
the ground state and the most-excited state, and demonstrate clear evidence for
a flux-dependent mobility edge. These measurements constitute the first direct
evidence for energy-dependent localization in a lower-dimensional system, as
well as the first explorations of the combined influence of artificial gauge
fields and engineered disorder. Moreover, we provide direct evidence for
interaction shifts of the localization transitions for both low- and
high-energy eigenstates in correlated disorder, relating to the presence of a
many-body mobility edge. The unique combination of strong interactions,
controlled disorder, and tunable artificial gauge fields present in this
synthetic lattice system should enable myriad explorations into intriguing
correlated transport phenomena.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the tensor semigroup of affine kac-moody lie algebras | In this paper, we are interested in the decomposition of the tensor product
of two representations of a symmetrizable Kac-Moody Lie algebra $\mathfrak g$.
Let $P\_+$ be the set of dominant integral weights. For $\lambda\in P\_+$ ,
$L(\lambda)$ denotes the irreducible, integrable, highest weight representation
of g with highest weight $\lambda$. Let $P\_{+,\mathbb Q}$ be the rational
convex cone generated by $P\_+$. Consider the tensor cone $\Gamma(\mathfrak g)
:= \{(\lambda\_1 ,\lambda\_2, \mu) $\in$ P\_{+,\mathbb Q}^3\,| \exists N
\textgreater{} 1 L(N\mu) \subset L(N \lambda\_1)\otimes L(N \lambda\_2)\}$. If
$\mathfrak g$ is finite dimensional, $\Gamma(\mathfrak g)$ is a polyhedral
convex cone described in 2006 by Belkale-Kumar by an explicit finite list of
inequalities. In general, $\Gamma(\mathfrak g)$ is nor polyhedral, nor closed.
In this article we describe the closure of $\Gamma(\mathfrak g)$ by an explicit
countable family of linear inequalities, when $\mathfrak g$ is untwisted
affine. This solves a Brown-Kumar's conjecture in this case. We also obtain
explicit saturation factors for the semigroup of triples $(\lambda\_1,
\lambda\_2 , \mu) $\in$ P\_+^3$ such that $L(\mu) $\subset$ L(\lambda\_1)
\otimes L(\lambda\_2)$. Note that even the existence of such saturation factors
is not obvious since the semigroup is not finitely generated. For example, in
type $A , we prove that any integer $d\geq 2$ is a saturation factor,
generalizing the case ${\tilde A}\_1$ shown by Brown-Kumar.
| 0 | 0 | 1 | 0 | 0 | 0 |
Electronic and Thermodynamic Properties of the Amino- and Carboxamido-Functionalized C-60-Based Fullerenes: Towards Non-Volatile Carbon Dioxide Scavengers | Development of new greenhouse gas scavengers is actively pursued nowadays.
Volatility caused solvent consumption and significant regeneration costs
associated with the aqueous amine solutions motivate search for more
technologically and economically advanced solutions. We hereby used hybrid
density functional theory to characterize thermodynamics, structure, electronic
and solvation properties of amino and carboxamido functionalized C60 fullerene.
C60 is non-volatile and supports a large density of amino groups on its
surface. Attachment of polar groups to fullerene C60 adjusts its dipole moment
and band gap quite substantially, ultimately resulting in systematically better
hydration thermodynamics. Reaction of polyaminofullerenes with CO2 is favored
enthalpically, but prohibited entropically at standard conditions. Free energy
of the CO2 capture by polyaminofullerenes is non-sensitive to the number of
amino groups per fullerene. This result fosters consideration of
polyaminofullerenes for CO2 fixation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask | Singing voice separation based on deep learning relies on the usage of
time-frequency masking. In many cases the masking process is not a learnable
function or is not encapsulated into the deep learning optimization.
Consequently, most of the existing methods rely on a post processing step using
the generalized Wiener filtering. This work proposes a method that learns and
optimizes (during training) a source-dependent mask and does not need the
aforementioned post processing step. We introduce a recurrent inference
algorithm, a sparse transformation step to improve the mask generation process,
and a learned denoising filter. Obtained results show an increase of 0.49 dB
for the signal to distortion ratio and 0.30 dB for the signal to interference
ratio, compared to previous state-of-the-art approaches for monaural singing
voice separation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Confidence Bands for Coefficients in High Dimensional Linear Models with Error-in-variables | We study high-dimensional linear models with error-in-variables. Such models
are motivated by various applications in econometrics, finance and genetics.
These models are challenging because of the need to account for measurement
errors to avoid non-vanishing biases in addition to handle the high
dimensionality of the parameters. A recent growing literature has proposed
various estimators that achieve good rates of convergence. Our main
contribution complements this literature with the construction of simultaneous
confidence regions for the parameters of interest in such high-dimensional
linear models with error-in-variables.
These confidence regions are based on the construction of moment conditions
that have an additional orthogonal property with respect to nuisance
parameters. We provide a construction that requires us to estimate an
additional high-dimensional linear model with error-in-variables for each
component of interest. We use a multiplier bootstrap to compute critical values
for simultaneous confidence intervals for a subset $S$ of the components. We
show its validity despite of possible model selection mistakes, and allowing
for the cardinality of $S$ to be larger than the sample size.
We apply and discuss the implications of our results to two examples and
conduct Monte Carlo simulations to illustrate the performance of the proposed
procedure.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems | We show that given an estimate $\widehat{A}$ that is close to a general
high-rank positive semi-definite (PSD) matrix $A$ in spectral norm (i.e.,
$\|\widehat{A}-A\|_2 \leq \delta$), the simple truncated SVD of $\widehat{A}$
produces a multiplicative approximation of $A$ in Frobenius norm. This
observation leads to many interesting results on general high-rank matrix
estimation problems, which we briefly summarize below ($A$ is an $n\times n$
high-rank PSD matrix and $A_k$ is the best rank-$k$ approximation of $A$):
(1) High-rank matrix completion: By observing
$\Omega(\frac{n\max\{\epsilon^{-4},k^2\}\mu_0^2\|A\|_F^2\log
n}{\sigma_{k+1}(A)^2})$ elements of $A$ where $\sigma_{k+1}\left(A\right)$ is
the $\left(k+1\right)$-th singular value of $A$ and $\mu_0$ is the incoherence,
the truncated SVD on a zero-filled matrix satisfies $\|\widehat{A}_k-A\|_F \leq
(1+O(\epsilon))\|A-A_k\|_F$ with high probability.
(2)High-rank matrix de-noising: Let $\widehat{A}=A+E$ where $E$ is a Gaussian
random noise matrix with zero mean and $\nu^2/n$ variance on each entry. Then
the truncated SVD of $\widehat{A}$ satisfies $\|\widehat{A}_k-A\|_F \leq
(1+O(\sqrt{\nu/\sigma_{k+1}(A)}))\|A-A_k\|_F + O(\sqrt{k}\nu)$.
(3) Low-rank Estimation of high-dimensional covariance: Given $N$
i.i.d.~samples $X_1,\cdots,X_N\sim\mathcal N_n(0,A)$, can we estimate $A$ with
a relative-error Frobenius norm bound? We show that if $N =
\Omega\left(n\max\{\epsilon^{-4},k^2\}\gamma_k(A)^2\log N\right)$ for
$\gamma_k(A)=\sigma_1(A)/\sigma_{k+1}(A)$, then $\|\widehat{A}_k-A\|_F \leq
(1+O(\epsilon))\|A-A_k\|_F$ with high probability, where
$\widehat{A}=\frac{1}{N}\sum_{i=1}^N{X_iX_i^\top}$ is the sample covariance.
| 0 | 0 | 1 | 1 | 0 | 0 |
Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition | When a human drives a car along a road for the first time, they later
recognize where they are on the return journey typically without needing to
look in their rear-view mirror or turn around to look back, despite significant
viewpoint and appearance change. Such navigation capabilities are typically
attributed to our semantic visual understanding of the environment [1] beyond
geometry to recognizing the types of places we are passing through such as
"passing a shop on the left" or "moving through a forested area". Humans are in
effect using place categorization [2] to perform specific place recognition
even when the viewpoint is 180 degrees reversed. Recent advances in deep neural
networks have enabled high-performance semantic understanding of visual places
and scenes, opening up the possibility of emulating what humans do. In this
work, we develop a novel methodology for using the semantics-aware higher-order
layers of deep neural networks for recognizing specific places from within a
reference database. To further improve the robustness to appearance change, we
develop a descriptor normalization scheme that builds on the success of
normalization schemes for pure appearance-based techniques such as SeqSLAM [3].
Using two different datasets - one road-based, one pedestrian-based, we
evaluate the performance of the system in performing place recognition on
reverse traversals of a route with a limited field of view camera and no
turn-back-and-look behaviours, and compare to existing state-of-the-art
techniques and vanilla off-the-shelf features. The results demonstrate
significant improvements over the existing state of the art, especially for
extreme perceptual challenges that involve both great viewpoint change and
environmental appearance change. We also provide experimental analyses of the
contributions of the various system components.
| 1 | 0 | 0 | 0 | 0 | 0 |
Ultra-broadband On-chip Twisted Light Emitter | On-chip twisted light emitters are essential components for orbital angular
momentum (OAM) communication devices, which could address the growing demand
for high-capacity communication systems by providing an additional degree of
freedom for wavelength/frequency division multiplexing (WDM/FDM). Although
whispering gallery mode enabled OAM emitters have been shown to possess some
advantages, such as being compact and phase accurate, their inherent narrow
bandwidth prevents them from being compatible with WDM/FDM techniques. Here, we
demonstrate an ultra-broadband multiplexed OAM emitter that utilizes a novel
joint path-resonance phase control concept. The emitter has a micron sized
radius and nanometer sized features. Coaxial OAM beams are emitted across the
entire telecommunication band from 1450 to 1650 nm. We applied the emitter for
OAM communication with a data rate of 1.2 Tbit/s assisted by 30-channel optical
frequency combs (OFC). The emitter provides a new solution to further increase
of the capacity in the OFC communication scenario.
| 0 | 1 | 0 | 0 | 0 | 0 |
Linear Additive Markov Processes | We introduce LAMP: the Linear Additive Markov Process. Transitions in LAMP
may be influenced by states visited in the distant history of the process, but
unlike higher-order Markov processes, LAMP retains an efficient
parametrization. LAMP also allows the specific dependence on history to be
learned efficiently from data. We characterize some theoretical properties of
LAMP, including its steady-state and mixing time. We then give an algorithm
based on alternating minimization to learn LAMP models from data. Finally, we
perform a series of real-world experiments to show that LAMP is more powerful
than first-order Markov processes, and even holds its own against deep
sequential models (LSTMs) with a negligible increase in parameter complexity.
| 1 | 0 | 0 | 1 | 0 | 0 |
Radial and circular synchronization clusters in extended starlike network of van der Pol oscillators | We consider extended starlike networks where the hub node is coupled with
several chains of nodes representing star rays. Assuming that nodes of the
network are occupied by nonidentical self-oscillators we study various forms of
their cluster synchronization. Radial cluster emerges when the nodes are
synchronized along a ray, while circular cluster is formed by nodes without
immediate connections but located on identical distances to the hub. By its
nature the circular synchronization is a new manifestation of so called remote
synchronization [Phys. Rev. E 85 (2012), 026208]. We report its long-range form
when the synchronized nodes interact through at least three intermediate nodes.
Forms of long-range remote synchronization are elements of scenario of
transition to the total synchronization of the network. We observe that the far
ends of rays synchronize first. Then more circular clusters appear involving
closer to hub nodes. Subsequently the clusters merge and, finally, all network
become synchronous. Behavior of the extended starlike networks is found to be
strongly determined by the ray length, while varying the number of rays
basically affects fine details of a dynamical picture. Symmetry of the star
also extensively influences the dynamics. In an asymmetric star circular
cluster mainly vanish in favor of radial ones, however, long-range remote
synchronization survives.
| 1 | 1 | 0 | 0 | 0 | 0 |
On the Fourth Power Moment of Fourier Coefficients of Cusp Form | Let $a(n)$ be the Fourier coefficients of a holomorphic cusp form of weight
$\kappa=2n\geqslant12$ for the full modular group and
$A(x)=\sum\limits_{n\leqslant x}a(n)$. In this paper, we establish an
asymptotic formula of the fourth power moment of $A(x)$ and prove that
\begin{equation*}
\int_1^TA^4(x)\mathrm{d}x=\frac{3}{64\kappa\pi^4}s_{4;2}(\tilde{a})
T^{2\kappa}+O\big(T^{2\kappa-\delta_4+\varepsilon}\big) \end{equation*} with
$\delta_4=1/8$, which improves the previous result.
| 0 | 0 | 1 | 0 | 0 | 0 |
Provable benefits of representation learning | There is general consensus that learning representations is useful for a
variety of reasons, e.g. efficient use of labeled data (semi-supervised
learning), transfer learning and understanding hidden structure of data.
Popular techniques for representation learning include clustering, manifold
learning, kernel-learning, autoencoders, Boltzmann machines, etc.
To study the relative merits of these techniques, it's essential to formalize
the definition and goals of representation learning, so that they are all
become instances of the same definition. This paper introduces such a formal
framework that also formalizes the utility of learning the representation. It
is related to previous Bayesian notions, but with some new twists. We show the
usefulness of our framework by exhibiting simple and natural settings -- linear
mixture models and loglinear models, where the power of representation learning
can be formally shown. In these examples, representation learning can be
performed provably and efficiently under plausible assumptions (despite being
NP-hard), and furthermore: (i) it greatly reduces the need for labeled data
(semi-supervised learning) and (ii) it allows solving classification tasks when
simpler approaches like nearest neighbors require too much data (iii) it is
more powerful than manifold learning methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
TherML: Thermodynamics of Machine Learning | In this work we offer a framework for reasoning about a wide class of
existing objectives in machine learning. We develop a formal correspondence
between this work and thermodynamics and discuss its implications.
| 0 | 0 | 0 | 1 | 0 | 0 |
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach | With the advent of Big Data, nowadays in many applications databases
containing large quantities of similar time series are available. Forecasting
time series in these domains with traditional univariate forecasting procedures
leaves great potentials for producing accurate forecasts untapped. Recurrent
neural networks (RNNs), and in particular Long Short-Term Memory (LSTM)
networks, have proven recently that they are able to outperform
state-of-the-art univariate time series forecasting methods in this context
when trained across all available time series. However, if the time series
database is heterogeneous, accuracy may degenerate, so that on the way towards
fully automatic forecasting methods in this space, a notion of similarity
between the time series needs to be built into the methods. To this end, we
present a prediction model that can be used with different types of RNN models
on subgroups of similar time series, which are identified by time series
clustering techniques. We assess our proposed methodology using LSTM networks,
a widely popular RNN variant. Our method achieves competitive results on
benchmarking datasets under competition evaluation procedures. In particular,
in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM
model and outperforms all other methods on the CIF2016 forecasting competition
dataset.
| 1 | 0 | 0 | 1 | 0 | 0 |
Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information | We introduce a new virtual environment for simulating a card game known as
"Big 2". This is a four-player game of imperfect information with a relatively
complicated action space (being allowed to play 1,2,3,4 or 5 card combinations
from an initial starting hand of 13 cards). As such it poses a challenge for
many current reinforcement learning methods. We then use the recently proposed
"Proximal Policy Optimization" algorithm to train a deep neural network to play
the game, purely learning via self-play, and find that it is able to reach a
level which outperforms amateur human players after only a relatively short
amount of training time and without needing to search a tree of future game
states.
| 0 | 0 | 0 | 1 | 0 | 0 |
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms | We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images
of 70,000 fashion products from 10 categories, with 7,000 images per category.
The training set has 60,000 images and the test set has 10,000 images.
Fashion-MNIST is intended to serve as a direct drop-in replacement for the
original MNIST dataset for benchmarking machine learning algorithms, as it
shares the same image size, data format and the structure of training and
testing splits. The dataset is freely available at
this https URL
| 1 | 0 | 0 | 1 | 0 | 0 |
Experimental Evaluation of Book Drawing Algorithms | A $k$-page book drawing of a graph $G=(V,E)$ consists of a linear ordering of
its vertices along a spine and an assignment of each edge to one of the $k$
pages, which are half-planes bounded by the spine. In a book drawing, two edges
cross if and only if they are assigned to the same page and their vertices
alternate along the spine. Crossing minimization in a $k$-page book drawing is
NP-hard, yet book drawings have multiple applications in visualization and
beyond. Therefore several heuristic book drawing algorithms exist, but there is
no broader comparative study on their relative performance. In this paper, we
propose a comprehensive benchmark set of challenging graph classes for book
drawing algorithms and provide an extensive experimental study of the
performance of existing book drawing algorithms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Probing magnetism in the vortex phase of PuCoGa$_5$ by X-ray magnetic circular dichroism | We have measured X-ray magnetic circular dichroism (XMCD) spectra at the Pu
$M_{4,5}$ absorption edges from a newly-prepared high-quality single crystal of
the heavy fermion superconductor $^{242}$PuCoGa$_{5}$, exhibiting a critical
temperature $T_{c} = 18.7~{\rm K}$. The experiment probes the vortex phase
below $T_{c}$ and shows that an external magnetic field induces a Pu 5$f$
magnetic moment at 2 K equal to the temperature-independent moment measured in
the normal phase up to 300 K by a SQUID device. This observation is in
agreement with theoretical models claiming that the Pu atoms in PuCoGa$_{5}$
have a nonmagnetic singlet ground state resulting from the hybridization of the
conduction electrons with the intermediate-valence 5$f$ electronic shell.
Unexpectedly, XMCD spectra show that the orbital component of the $5f$ magnetic
moment increases significantly between 30 and 2 K; the antiparallel spin
component increases as well, leaving the total moment practically constant. We
suggest that this indicates a low-temperature breakdown of the complete
Kondo-like screening of the local 5$f$ moment.
| 0 | 1 | 0 | 0 | 0 | 0 |
Joint secrecy over the K-Transmitter Multiple Access Channel | This paper studies the problem of secure communication over a K-transmitter
multiple access channel in the presence of an external eavesdropper, subject to
a joint secrecy constraint (i.e., information leakage rate from the collection
of K messages to an eavesdropper is made vanishing). As a result, we establish
the joint secrecy achievable rate region. To this end, our results build upon
two techniques in addition to the standard information-theoretic methods. The
first is a generalization of Chia-El Gamal's lemma on entropy bound for a set
of codewords given partial information. The second is to utilize a compact
representation of a list of sets that, together with properties of mutual
information, leads to an efficient Fourier-Motzkin elimination. These two
approaches could also be of independent interests in other contexts.
| 1 | 0 | 0 | 0 | 0 | 0 |
Nonlinear Flexoelectricity in Non-centrosymmetric Crystals | We analytically derive the elastic, dielectric, piezoelectric, and the
flexoelectric phenomenological coefficients as functions of microscopic model
parameters such as ionic positions and spring constants in the two-dimensional
square-lattice model with rock-salt-type ionic arrangement. Monte-Carlo
simulation reveals that a difference in the given elastic constants of the
diagonal springs, each of which connects the same cations or anions, is
responsible for the linear flexoelectric effect in the model. We show the
quadratic flexoelectric effect is present only in non-centrosymmetric systems
and it can overwhelm the linear effect in feasibly large strain gradients.
| 0 | 1 | 0 | 0 | 0 | 0 |
Well-posedness of nonlinear transport equation by stochastic perturbation | We are concerned with multidimensional nonlinear stochastic transport
equation driven by Brownian motions. For irregular fluxes, by using stochastic
BGK approximations and commutator estimates, we gain the existence and
uniqueness of stochastic entropy solutions. Besides, for $BV$ initial data, the
$BV$ and Hölder regularities are also derived for the unique stochastic
entropy solution. Particularly, for the transport equation, we gain a
regularization result, i.e. while the existence fails for the transport
equation, we prove that a multiplicative stochastic perturbation of Brownian
type is enough to render the equation well-posed. This seems to be another
explicit example (the first example is given in [22]) of a PDE of fluid
dynamics that becomes well-posed under the influence of a multiplicative
Brownian type noise.
| 0 | 0 | 1 | 0 | 0 | 0 |
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