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Title: Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models,
Abstract: Biomedical events describe complex interactions between various biomedical
entities. Event trigger is a word or a phrase which typically signifies the
occurrence of an event. Event trigger identification is an important first step
in all event extraction methods. However many of the current approaches either
rely on complex hand-crafted features or consider features only within a
window. In this paper we propose a method that takes the advantage of recurrent
neural network (RNN) to extract higher level features present across the
sentence. Thus hidden state representation of RNN along with word and entity
type embedding as features avoid relying on the complex hand-crafted features
generated using various NLP toolkits. Our experiments have shown to achieve
state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have
also performed category-wise analysis of the result and discussed the
importance of various features in trigger identification task. | [
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] |
Title: Method of Reduction of Variables for Bilinear Matrix Inequality Problems in System and Control Designs,
Abstract: Bilinear matrix inequality (BMI) problems in system and control designs are
investigated in this paper. A solution method of reduction of variables (MRVs)
is proposed. This method consists of a principle of variable classification, a
procedure for problem transformation, and a hybrid algorithm that combines
deterministic and stochastic search engines. The classification principle is
used to classify the decision variables of a BMI problem into two categories:
1) external and 2) internal variables. Theoretical analysis is performed to
show that when the classification principle is applicable, a BMI problem can be
transformed into an unconstrained optimization problem that has fewer decision
variables. Stochastic search and deterministic search are then applied to
determine the decision variables of the unconstrained problem externally and
explore the internal problem structure, respectively. The proposed method can
address feasibility, single-objective, and multiobjective problems constrained
by BMIs in a unified manner. A number of numerical examples in system and
control designs are provided to validate the proposed methodology. Simulations
show that the MRVs can outperform existing BMI solution methods in most
benchmark problems and achieve similar levels of performance in the remaining
problems. | [
1,
0,
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0,
0,
0
] |
Title: The Observability Concept in a Class of Hybrid Control systems,
Abstract: In the discrete modeling approach for hybrid control systems, the continuous
plant is reduced to a discrete event approximation, called the DES-plant, that
is governed by a discrete event system, representing the controller. The
observability of the DES-plant model is crucial for the synthesis of the
controller and for the proper closed loop evolution of the hybrid control
system. Based on a version of the framework for hybrid control systems proposed
by Antsaklis, the paper analysis the relation between the properties of the
cellular space of the continuous plant and a mechanism of plant-symbols
generation, on one side, and the observability of the DES-plant automaton on
the other side. Finally an observable discrete event abstraction of the
continuous double integrator is presented. | [
1,
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0
] |
Title: A study of posture judgement on vehicles using wearable acceleration sensor,
Abstract: We study methods to estimate drivers' posture in vehicles using acceleration
data of wearable sensor and conduct field tests. To prevent fatal accidents,
demands for safety management of bus and taxi are high. However, acceleration
of vehicles is added to wearable sensor in vehicles. Therefore, we study
methods to estimate driving posture using acceleration data acquired from shirt
type wearable sensor hitoe and conduct field tests. | [
1,
0,
0,
0,
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0
] |
Title: Smoothed nonparametric two-sample tests,
Abstract: We propose new smoothed median and the Wilcoxon's rank sum test. As is
pointed out by Maesono et al.(2016), some nonparametric discrete tests have a
problem with their significance probability. Because of this problem, the
selection of the median and the Wilcoxon's test can be biased too, however, we
show new smoothed tests are free from the problem. Significance probabilities
and local asymptotic powers of the new tests are studied, and we show that they
inherit good properties of the discrete tests. | [
0,
0,
1,
1,
0,
0
] |
Title: A stack-vector routing protocol for automatic tunneling,
Abstract: In a network, a tunnel is a part of a path where a protocol is encapsulated
in another one. A tunnel starts with an encapsulation and ends with the
corresponding decapsulation. Several tunnels can be nested at some stage,
forming a protocol stack. Tunneling is very important nowadays and it is
involved in several tasks: IPv4/IPv6 transition, VPNs, security (IPsec, onion
routing), etc. However, tunnel establishment is mainly performed manually or by
script, which present obvious scalability issues. Some works attempt to
automate a part of the process (e.g., TSP, ISATAP, etc.). However, the
determination of the tunnel(s) endpoints is not fully automated, especially in
the case of an arbitrary number of nested tunnels. The lack of routing
protocols performing automatic tunneling is due to the unavailability of path
computation algorithms taking into account encapsulations and decapsulations.
There is a polynomial centralized algorithm to perform the task. However, to
the best of our knowledge, no fully distributed path computation algorithm is
known. Here, we propose the first fully distributed algorithm for path
computation with automatic tunneling, i.e., taking into account encapsulation,
decapsulation and conversion of protocols. Our algorithm is a generalization of
the distributed Bellman-Ford algorithm, where the distance vector is replaced
by a protocol stack vector. This allows to know how to route a packet with some
protocol stack. We prove that the messages size of our algorithm is polynomial,
even if the shortest path can be of exponential length. We also prove that the
algorithm converges after a polynomial number of steps in a synchronized
setting. We adapt our algorithm into a proto-protocol for routing with
automatic tunneling and we show its efficiency through simulations. | [
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] |
Title: Inferring Narrative Causality between Event Pairs in Films,
Abstract: To understand narrative, humans draw inferences about the underlying
relations between narrative events. Cognitive theories of narrative
understanding define these inferences as four different types of causality,
that include pairs of events A, B where A physically causes B (X drop, X
break), to pairs of events where A causes emotional state B (Y saw X, Y felt
fear). Previous work on learning narrative relations from text has either
focused on "strict" physical causality, or has been vague about what relation
is being learned. This paper learns pairs of causal events from a corpus of
film scene descriptions which are action rich and tend to be told in
chronological order. We show that event pairs induced using our methods are of
high quality and are judged to have a stronger causal relation than event pairs
from Rel-grams. | [
1,
0,
0,
0,
0,
0
] |
Title: On Hom-Gerstenhaber algebras and Hom-Lie algebroids,
Abstract: We define the notion of hom-Batalin-Vilkovisky algebras and strong
differential hom-Gerstenhaber algebras as a special class of hom-Gerstenhaber
algebras and provide canonical examples associated to some well-known
hom-structures. Representations of a hom-Lie algebroid on a hom-bundle are
defined and a cohomology of a regular hom-Lie algebroid with coefficients in a
representation is studied. We discuss about relationship between these classes
of hom-Gerstenhaber algebras and geometric structures on a vector bundle. As an
application, we associate a homology to a regular hom-Lie algebroid and then
define a hom-Poisson homology associated to a hom-Poisson manifold. | [
0,
0,
1,
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] |
Title: Supercongruences between truncated ${}_3F_2$ hypergeometric series,
Abstract: We establish four supercongruences between truncated ${}_3F_2$ hypergeometric
series involving $p$-adic Gamma functions, which extend some of the
Rodriguez-Villegas supercongruences. | [
0,
0,
1,
0,
0,
0
] |
Title: Indoor Localization Using Visible Light Via Fusion Of Multiple Classifiers,
Abstract: A multiple classifiers fusion localization technique using received signal
strengths (RSSs) of visible light is proposed, in which the proposed system
transmits different intensity modulated sinusoidal signals by LEDs and the
signals received by a Photo Diode (PD) placed at various grid points. First, we
obtain some {\emph{approximate}} received signal strengths (RSSs) fingerprints
by capturing the peaks of power spectral density (PSD) of the received signals
at each given grid point. Unlike the existing RSSs based algorithms, several
representative machine learning approaches are adopted to train multiple
classifiers based on these RSSs fingerprints. The multiple classifiers
localization estimators outperform the classical RSS-based LED localization
approaches in accuracy and robustness. To further improve the localization
performance, two robust fusion localization algorithms, namely, grid
independent least square (GI-LS) and grid dependent least square (GD-LS), are
proposed to combine the outputs of these classifiers. We also use a singular
value decomposition (SVD) based LS (LS-SVD) method to mitigate the numerical
stability problem when the prediction matrix is singular. Experiments conducted
on intensity modulated direct detection (IM/DD) systems have demonstrated the
effectiveness of the proposed algorithms. The experimental results show that
the probability of having mean square positioning error (MSPE) of less than 5cm
achieved by GD-LS is improved by 93.03\% and 93.15\%, respectively, as compared
to those by the RSS ratio (RSSR) and RSS matching methods with the FFT length
of 2000. | [
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1,
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] |
Title: Data Fusion Reconstruction of Spatially Embedded Complex Networks,
Abstract: We introduce a kernel Lasso (kLasso) optimization that simultaneously
accounts for spatial regularity and network sparsity to reconstruct spatial
complex networks from data. Through a kernel function, the proposed approach
exploits spatial embedding distances to penalize overabundance of spatially
long-distance connections. Examples of both synthetic and real-world spatial
networks show that the proposed method improves significantly upon existing
network reconstruction techniques that mainly concerns sparsity but not spatial
regularity. Our results highlight the promise of data fusion in the
reconstruction of complex networks, by utilizing both microscopic node-level
dynamics (e.g., time series data) and macroscopic network-level information
(metadata). | [
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] |
Title: Interpreting Classifiers through Attribute Interactions in Datasets,
Abstract: In this work we present the novel ASTRID method for investigating which
attribute interactions classifiers exploit when making predictions. Attribute
interactions in classification tasks mean that two or more attributes together
provide stronger evidence for a particular class label. Knowledge of such
interactions makes models more interpretable by revealing associations between
attributes. This has applications, e.g., in pharmacovigilance to identify
interactions between drugs or in bioinformatics to investigate associations
between single nucleotide polymorphisms. We also show how the found attribute
partitioning is related to a factorisation of the data generating distribution
and empirically demonstrate the utility of the proposed method. | [
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] |
Title: Testing approximate predictions of displacements of cosmological dark matter halos,
Abstract: We present a test to quantify how well some approximate methods, designed to
reproduce the mildly non-linear evolution of perturbations, are able to
reproduce the clustering of DM halos once the grouping of particles into halos
is defined and kept fixed. The following methods have been considered:
Lagrangian Perturbation Theory (LPT) up to third order, Truncated LPT,
Augmented LPT, MUSCLE and COLA. The test runs as follows: halos are defined by
applying a friends-of-friends (FoF) halo finder to the output of an N-body
simulation. The approximate methods are then applied to the same initial
conditions of the simulation, producing for all particles displacements from
their starting position and velocities. The position and velocity of each halo
are computed by averaging over the particles that belong to that halo,
according to the FoF halo finder. This procedure allows us to perform a
well-posed test of how clustering of the matter density and halo density fields
are recovered, without asking to the approximate method an accurate
reconstruction of halos. We have considered the results at $z=0,0.5,1$, and we
have analysed power spectrum in real and redshift space, object-by-object
difference in position and velocity, density Probability Distribution Function
(PDF) and its moments, phase difference of Fourier modes. We find that higher
LPT orders are generally able to better reproduce the clustering of halos,
while little or no improvement is found for the matter density field when going
to 2LPT and 3LPT. Augmentation provides some improvement when coupled with
2LPT, while its effect is limited when coupled with 3LPT. Little improvement is
brought by MUSCLE with respect to Augmentation. The more expensive
particle-mesh code COLA outperforms all LPT methods [abridged] | [
0,
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] |
Title: Efficient and Secure Routing Protocol for WSN-A Thesis,
Abstract: Advances in Wireless Sensor Network (WSN) have provided the availability of
small and low-cost sensors with the capability of sensing various types of
physical and environmental conditions, data processing, and wireless
communication. Since WSN protocols are application specific, the focus has been
given to the routing protocols that might differ depending on the application
and network architecture. In this work, novel routing protocols have been
proposed which is a cluster-based security protocol is named as Efficient and
Secure Routing Protocol (ESRP) for WSN. The goal of ESRP is to provide an
energy efficient routing solution with dynamic security features for clustered
WSN. During the network formation, a node which is connected to a Personal
Computer (PC) has been selected as a sink node. Once the sensor nodes were
deployed, the sink node logically segregates the other nodes in a cluster
structure and subsequently creates a WSN. This centralized cluster formation
method is used to reduce the node level processing burden and avoid multiple
communications. In order to ensure reliable data delivery, various security
features have been incorporated in the proposed protocol such as Modified
Zero-Knowledge Protocol (MZKP), Promiscuous hearing method, Trapping of
adversaries and Mine detection. One of the unique features of this ESRP is that
it can dynamically decide about the selection of these security methods, based
on the residual energy of nodes. | [
1,
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] |
Title: A convex formulation of traffic dynamics on transportation networks,
Abstract: This article proposes a numerical scheme for computing the evolution of
vehicular traffic on a road network over a finite time horizon. The traffic
dynamics on each link is modeled by the Hamilton-Jacobi (HJ) partial
differential equation (PDE), which is an equivalent form of the
Lighthill-Whitham-Richards PDE. The main contribution of this article is the
construction of a single convex optimization program which computes the traffic
flow at a junction over a finite time horizon and decouples the PDEs on
connecting links. Compared to discretization schemes which require the
computation of all traffic states on a time-space grid, the proposed convex
optimization approach computes the boundary flows at the junction using only
the initial condition on links and the boundary conditions of the network. The
computed boundary flows at the junction specify the boundary condition for the
HJ PDE on connecting links, which then can be separately solved using an
existing semi-explicit scheme for single link HJ PDE. As demonstrated in a
numerical example of ramp metering control, the proposed convex optimization
approach also provides a natural framework for optimal traffic control
applications. | [
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] |
Title: Computational and informatics advances for reproducible data analysis in neuroimaging,
Abstract: The reproducibility of scientific research has become a point of critical
concern. We argue that openness and transparency are critical for
reproducibility, and we outline an ecosystem for open and transparent science
that has emerged within the human neuroimaging community. We discuss the range
of open data sharing resources that have been developed for neuroimaging data,
and the role of data standards (particularly the Brain Imaging Data Structure)
in enabling the automated sharing, processing, and reuse of large neuroimaging
datasets. We outline how the open-source Python language has provided the basis
for a data science platform that enables reproducible data analysis and
visualization. We also discuss how new advances in software engineering, such
as containerization, provide the basis for greater reproducibility in data
analysis. The emergence of this new ecosystem provides an example for many
areas of science that are currently struggling with reproducibility. | [
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] |
Title: New constraints on the millimetre emission of six debris disks,
Abstract: The presence of dusty debris around main sequence stars denotes the existence
of planetary systems. Such debris disks are often identified by the presence of
excess continuum emission at infrared and (sub-)millimetre wavelengths, with
measurements at longer wavelengths tracing larger and cooler dust grains. The
exponent of the slope of the disk emission at sub-millimetre wavelengths, `q',
defines the size distribution of dust grains in the disk. This size
distribution is a function of the rigid strength of the dust producing parent
planetesimals. As part of the survey `PLAnetesimals around TYpical Pre-main
seqUence Stars' (PLATYPUS) we observed six debris disks at 9-mm using the
Australian Telescope Compact Array. We obtain marginal (~3-\sigma) detections
of three targets: HD 105, HD 61005, and HD 131835. Upper limits for the three
remaining disks, HD20807, HD109573, and HD109085, provide further constraint of
the (sub-)millimetre slope of their spectral energy distributions. The values
of q (or their limits) derived from our observations are all smaller than the
oft-assumed steady state collisional cascade model (q = 3.5), but lie well
within the theoretically expected range for debris disks q ~ 3 to 4. The
measured q values for our targets are all < 3.3, consistent with both
collisional modelling results and theoretical predictions for parent
planetesimal bodies being `rubble piles' held together loosely by their
self-gravity. | [
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] |
Title: Connected Vehicular Transportation: Data Analytics and Traffic-dependent Networking,
Abstract: With onboard operating systems becoming increasingly common in vehicles, the
real-time broadband infotainment and Intelligent Transportation System (ITS)
service applications in fast-motion vehicles become ever demanding, which are
highly expected to significantly improve the efficiency and safety of our daily
on-road lives. The emerging ITS and vehicular applications, e.g., trip
planning, however, require substantial efforts on the real-time pervasive
information collection and big data processing so as to provide quick decision
making and feedbacks to the fast moving vehicles, which thus impose the
significant challenges on the development of an efficient vehicular
communication platform. In this article, we present TrasoNET, an integrated
network framework to provide realtime intelligent transportation services to
connected vehicles by exploring the data analytics and networking techniques.
TrasoNET is built upon two key components. The first one guides vehicles to the
appropriate access networks by exploring the information of realtime traffic
status, specific user preferences, service applications and network conditions.
The second component mainly involves a distributed automatic access engine,
which enables individual vehicles to make distributed access decisions based on
access recommender, local observation and historic information. We showcase the
application of TrasoNET in a case study on real-time traffic sensing based on
real traces of taxis. | [
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] |
Title: Strongly ergodic equivalence relations: spectral gap and type III invariants,
Abstract: We obtain a spectral gap characterization of strongly ergodic equivalence
relations on standard measure spaces. We use our spectral gap criterion to
prove that a large class of skew-product equivalence relations arising from
measurable $1$-cocycles with values into locally compact abelian groups are
strongly ergodic. By analogy with the work of Connes on full factors, we
introduce the Sd and $\tau$ invariants for type ${\rm III}$ strongly ergodic
equivalence relations. As a corollary to our main results, we show that for any
type ${\rm III_1}$ ergodic equivalence relation $\mathcal R$, the Maharam
extension $\mathord{\text {c}}(\mathcal R)$ is strongly ergodic if and only if
$\mathcal R$ is strongly ergodic and the invariant $\tau(\mathcal R)$ is the
usual topology on $\mathbf R$. We also obtain a structure theorem for almost
periodic strongly ergodic equivalence relations analogous to Connes' structure
theorem for almost periodic full factors. Finally, we prove that for arbitrary
strongly ergodic free actions of bi-exact groups (e.g. hyperbolic groups), the
Sd and $\tau$ invariants of the orbit equivalence relation and of the
associated group measure space von Neumann factor coincide. | [
0,
0,
1,
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] |
Title: Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers,
Abstract: Clustering is the process of finding and analyzing underlying group structure
in data. In recent years, data as become increasingly higher dimensional and,
therefore, an increased need has arisen for dimension reduction techniques for
clustering. Although such techniques are firmly established in the literature
for multivariate data, there is a relative paucity in the area of matrix
variate or three way data. Furthermore, the few methods that are available all
assume matrix variate normality, which is not always sensible if cluster
skewness or excess kurtosis is present. Mixtures of bilinear factor analyzers
models using skewed matrix variate distributions are proposed. In all, four
such mixture models are presented, based on matrix variate skew-t, generalized
hyperbolic, variance gamma and normal inverse Gaussian distributions,
respectively. | [
0,
0,
0,
1,
0,
0
] |
Title: Transfer Learning to Learn with Multitask Neural Model Search,
Abstract: Deep learning models require extensive architecture design exploration and
hyperparameter optimization to perform well on a given task. The exploration of
the model design space is often made by a human expert, and optimized using a
combination of grid search and search heuristics over a large space of possible
choices. Neural Architecture Search (NAS) is a Reinforcement Learning approach
that has been proposed to automate architecture design. NAS has been
successfully applied to generate Neural Networks that rival the best
human-designed architectures. However, NAS requires sampling, constructing, and
training hundreds to thousands of models to achieve well-performing
architectures. This procedure needs to be executed from scratch for each new
task. The application of NAS to a wide set of tasks currently lacks a way to
transfer generalizable knowledge across tasks. In this paper, we present the
Multitask Neural Model Search (MNMS) controller. Our goal is to learn a
generalizable framework that can condition model construction on successful
model searches for previously seen tasks, thus significantly speeding up the
search for new tasks. We demonstrate that MNMS can conduct an automated
architecture search for multiple tasks simultaneously while still learning
well-performing, specialized models for each task. We then show that
pre-trained MNMS controllers can transfer learning to new tasks. By leveraging
knowledge from previous searches, we find that pre-trained MNMS models start
from a better location in the search space and reduce search time on unseen
tasks, while still discovering models that outperform published human-designed
models. | [
1,
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1,
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] |
Title: Counterintuitive Reconstruction of the Polar O-Terminated ZnO Surface With Zinc Vacancies and Hydrogen,
Abstract: Understanding the structure of ZnO surface reconstructions and their
resultant properties is crucial to the rational design of ZnO-containing
devices ranging from optoelectronics to catalysts. Here, we are motivated by
recent experimental work which showed a new surface reconstruction containing
Zn vacancies ordered in a Zn(3x3) pattern in the subsurface of (0001)-O
terminated ZnO. A reconstruction with Zn vacancies on (0001)-O is surprising
and counterintuitive because Zn vacancies enhance the surface dipole rather
than reduce it. In this work, we show using Density Functional Theory (DFT)
that subsurface Zn vacancies can form on (0001)-O when coupled with adsorption
of surface H and are in fact stable under a wide range of common conditions. We
also show these vacancies have a significant ordering tendency and that
Sb-doping created subsurface inversion domain boundaries (IDBs) enhances the
driving force of Zn vacancy alignment into large domains of the Zn(3x3)
reconstruction. | [
0,
1,
0,
0,
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] |
Title: Decomposing the Quantile Ratio Index with applications to Australian income and wealth data,
Abstract: The quantile ratio index introduced by Prendergast and Staudte 2017 is a
simple and effective measure of relative inequality for income data that is
resistant to outliers. It measures the average relative distance of a randomly
chosen income from its symmetric quantile. Another useful property of this
index is investigated here: given a partition of the income distribution into a
union of sets of symmetric quantiles, one can find the conditional inequality
for each set as measured by the quantile ratio index and readily combine them
in a weighted average to obtain the index for the entire population. When
applied to data for various years, one can track how these contributions to
inequality vary over time, as illustrated here for Australian Bureau of
Statistics income and wealth data. | [
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1,
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] |
Title: Metamorphic Moving Horizon Estimation,
Abstract: This paper considers a practical scenario where a classical estimation method
might have already been implemented on a certain platform when one tries to
apply more advanced techniques such as moving horizon estimation (MHE). We are
interested to utilize MHE to upgrade, rather than completely discard, the
existing estimation technique. This immediately raises the question how one can
improve the estimation performance gradually based on the pre-estimator. To
this end, we propose a general methodology which incorporates the pre-estimator
with a tuning parameter {\lambda} between 0 and 1 into the quadratic cost
functions that are usually adopted in MHE. We examine the above idea in two
standard MHE frameworks that have been proposed in the existing literature. For
both frameworks, when {\lambda} = 0, the proposed strategy exactly matches the
existing classical estimator; when the value of {\lambda} is increased, the
proposed strategy exhibits a more aggressive normalized forgetting effect
towards the old data, thereby increasing the estimation performance gradually. | [
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] |
Title: Erosion distance for generalized persistence modules,
Abstract: The persistence diagram of Cohen-Steiner, Edelsbrunner, and Harer was
recently generalized by Patel to the case of constructible persistence modules
with values in a symmetric monoidal category with images. Patel also introduced
a distance for persistence diagrams, the erosion distance. Motivated by this
work, we extend the erosion distance to a distance of rank invariants of
generalized persistence modules by using the generalization of the interleaving
distance of Bubenik, de Silva, and Scott as a guideline. This extension of the
erosion distance also gives, as a special case, a distance for multidimensional
persistent homology groups with torsion introduced by Frosini. We show that the
erosion distance is stable with respect to the interleaving distance, and that
it gives a lower bound for the natural pseudo-distance in the case of sublevel
set persistent homology of continuous functions. | [
0,
0,
1,
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0
] |
Title: Real representations of finite symplectic groups over fields of characteristic two,
Abstract: We prove that when $q$ is a power of $2$, every complex irreducible
representation of $\mathrm{Sp}(2n, \mathbb{F}_q)$ may be defined over the real
numbers, that is, all Frobenius-Schur indicators are 1. We also obtain a
generating function for the sum of the degrees of the unipotent characters of
$\mathrm{Sp}(2n, \mathbb{F}_q)$, or of $\mathrm{SO}(2n+1, \mathbb{F}_q)$, for
any prime power $q$. | [
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] |
Title: Risk measure estimation for $β$-mixing time series and applications,
Abstract: In this paper, we discuss the application of extreme value theory in the
context of stationary $\beta$-mixing sequences that belong to the Fréchet
domain of attraction. In particular, we propose a methodology to construct
bias-corrected tail estimators. Our approach is based on the combination of two
estimators for the extreme value index to cancel the bias. The resulting
estimator is used to estimate an extreme quantile. In a simulation study, we
outline the performance of our proposals that we compare to alternative
estimators recently introduced in the literature. Also, we compute the
asymptotic variance in specific examples when possible. Our methodology is
applied to two datasets on finance and environment. | [
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] |
Title: Neural Task Programming: Learning to Generalize Across Hierarchical Tasks,
Abstract: In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives. | [
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] |
Title: Towards Planning and Control of Hybrid Systems with Limit Cycle using LQR Trees,
Abstract: We present a multi-query recovery policy for a hybrid system with goal limit
cycle. The sample trajectories and the hybrid limit cycle of the dynamical
system are stabilized using locally valid Time Varying LQR controller policies
which probabilistically cover a bounded region of state space. The original LQR
Tree algorithm builds such trees for non-linear static and non-hybrid systems
like a pendulum or a cart-pole. We leverage the idea of LQR trees to plan with
a continuous control set, unlike methods that rely on discretization like
dynamic programming to plan for hybrid dynamical systems where it is hard to
capture the exact event of discrete transition. We test the algorithm on a
compass gait model by stabilizing a dynamic walking hybrid limit cycle with
point foot contact from random initial conditions. We show results from the
simulation where the system comes back to a stable behavior with initial
position or velocity perturbation and noise. | [
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] |
Title: The Observable Properties of Cool Winds from Galaxies, AGN, and Star Clusters. I. Theoretical Framework,
Abstract: Winds arising from galaxies, star clusters, and active galactic nuclei are
crucial players in star and galaxy formation, but it has proven remarkably
difficult to use observations of them to determine physical properties of
interest, particularly mass fluxes. Much of the difficulty stems from a lack of
a theory that links a physically-realistic model for winds' density, velocity,
and covering factors to calculations of light emission and absorption. In this
paper we provide such a model. We consider a wind launched from a turbulent
region with a range of column densities, derive the differential acceleration
of gas as a function of column density, and use this result to compute winds'
absorption profiles, emission profiles, and emission intensity maps in both
optically thin and optically thick species. The model is sufficiently simple
that all required computations can be done analytically up to straightforward
numerical integrals, rendering it suitable for the problem of deriving physical
parameters by fitting models to observed data. We show that our model produces
realistic absorption and emission profiles for some example cases, and argue
that the most promising methods of deducing mass fluxes are based on
combinations of absorption lines of different optical depths, or on combining
absorption with measurements of molecular line emission. In the second paper in
this series, we expand on these ideas by introducing a set of observational
diagnostics that are significantly more robust that those commonly in use, and
that can be used to obtain improved estimates of wind properties. | [
0,
1,
0,
0,
0,
0
] |
Title: Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems,
Abstract: In this paper, we propose a novel ranking framework for collaborative
filtering with the overall aim of learning user preferences over items by
minimizing a pairwise ranking loss. We show the minimization problem involves
dependent random variables and provide a theoretical analysis by proving the
consistency of the empirical risk minimization in the worst case where all
users choose a minimal number of positive and negative items. We further derive
a Neural-Network model that jointly learns a new representation of users and
items in an embedded space as well as the preference relation of users over the
pairs of items. The learning objective is based on three scenarios of ranking
losses that control the ability of the model to maintain the ordering over the
items induced from the users' preferences, as well as, the capacity of the
dot-product defined in the learned embedded space to produce the ordering. The
proposed model is by nature suitable for implicit feedback and involves the
estimation of only very few parameters. Through extensive experiments on
several real-world benchmarks on implicit data, we show the interest of
learning the preference and the embedding simultaneously when compared to
learning those separately. We also demonstrate that our approach is very
competitive with the best state-of-the-art collaborative filtering techniques
proposed for implicit feedback. | [
1,
0,
0,
1,
0,
0
] |
Title: On the Sublinear Regret of Distributed Primal-Dual Algorithms for Online Constrained Optimization,
Abstract: This paper introduces consensus-based primal-dual methods for distributed
online optimization where the time-varying system objective function
$f_t(\mathbf{x})$ is given as the sum of local agents' objective functions,
i.e., $f_t(\mathbf{x}) = \sum_i f_{i,t}(\mathbf{x}_i)$, and the system
constraint function $\mathbf{g}(\mathbf{x})$ is given as the sum of local
agents' constraint functions, i.e., $\mathbf{g}(\mathbf{x}) = \sum_i
\mathbf{g}_i (\mathbf{x}_i) \preceq \mathbf{0}$. At each stage, each agent
commits to an adaptive decision pertaining only to the past and locally
available information, and incurs a new cost function reflecting the change in
the environment. Our algorithm uses weighted averaging of the iterates for each
agent to keep local estimates of the global constraints and dual variables. We
show that the algorithm achieves a regret of order $O(\sqrt{T})$ with the time
horizon $T$, in scenarios when the underlying communication topology is
time-varying and jointly-connected. The regret is measured in regard to the
cost function value as well as the constraint violation. Numerical results for
online routing in wireless multi-hop networks with uncertain channel rates are
provided to illustrate the performance of the proposed algorithm. | [
0,
0,
1,
0,
0,
0
] |
Title: On the Underapproximation of Reach Sets of Abstract Continuous-Time Systems,
Abstract: We consider the problem of proving that each point in a given set of states
("target set") can indeed be reached by a given nondeterministic
continuous-time dynamical system from some initial state. We consider this
problem for abstract continuous-time models that can be concretized as various
kinds of continuous and hybrid dynamical systems.
The approach to this problem proposed in this paper is based on finding a
suitable superset S of the target set which has the property that each partial
trajectory of the system which lies entirely in S either is defined as the
initial time moment, or can be locally extended backward in time, or can be
locally modified in such a way that the resulting trajectory can be locally
extended back in time.
This reformulation of the problem has a relatively simple logical expression
and is convenient for applying various local existence theorems and local
dynamics analysis methods to proving reachability which makes it suitable for
reasoning about the behavior of continuous and hybrid dynamical systems in
proof assistants such as Mizar, Isabelle, etc. | [
1,
0,
0,
0,
0,
0
] |
Title: A Bayesian nonparametric approach to log-concave density estimation,
Abstract: The estimation of a log-concave density on $\mathbb{R}$ is a canonical
problem in the area of shape-constrained nonparametric inference. We present a
Bayesian nonparametric approach to this problem based on an exponentiated
Dirichlet process mixture prior and show that the posterior distribution
converges to the log-concave truth at the (near-) minimax rate in Hellinger
distance. Our proof proceeds by establishing a general contraction result based
on the log-concave maximum likelihood estimator that prevents the need for
further metric entropy calculations. We also present two computationally more
feasible approximations and a more practical empirical Bayes approach, which
are illustrated numerically via simulations. | [
0,
0,
1,
1,
0,
0
] |
Title: A Complete Characterization of the 1-Dimensional Intrinsic Cech Persistence Diagrams for Metric Graphs,
Abstract: Metric graphs are special types of metric spaces used to model and represent
simple, ubiquitous, geometric relations in data such as biological networks,
social networks, and road networks. We are interested in giving a qualitative
description of metric graphs using topological summaries. In particular, we
provide a complete characterization of the 1-dimensional intrinsic Cech
persistence diagrams for metric graphs using persistent homology. Together with
complementary results by Adamaszek et. al, which imply results on intrinsic
Cech persistence diagrams in all dimensions for a single cycle, our results
constitute important steps toward characterizing intrinsic Cech persistence
diagrams for arbitrary metric graphs across all dimensions. | [
0,
0,
1,
0,
0,
0
] |
Title: Critical exponent $ω$ in the Gross-Neveu-Yukawa model at $O(1/N)$,
Abstract: The critcal exponent $\omega$ is evaluated at $O(1/N)$ in $d$-dimensions in
the Gross-Neveu model using the large $N$ critical point formalism. It is shown
to be in agreement with the recently determined three loop $\beta$-functions of
the Gross-Neveu-Yukawa model in four dimensions. The same exponent is computed
for the chiral Gross-Neveu and non-abelian Nambu-Jona-Lasinio universality
classes. | [
0,
1,
0,
0,
0,
0
] |
Title: Path Planning for Multiple Heterogeneous Unmanned Vehicles with Uncertain Service Times,
Abstract: This article presents a framework and develops a formulation to solve a path
planning problem for multiple heterogeneous Unmanned Vehicles (UVs) with
uncertain service times for each vehicle--target pair. The vehicles incur a
penalty proportional to the duration of their total service time in excess of a
preset constant. The vehicles differ in their motion constraints and are
located at distinct depots at the start of the mission. The vehicles may also
be equipped with disparate sensors. The objective is to find a tour for each
vehicle that starts and ends at its respective depot such that every target is
visited and serviced by some vehicle while minimizing the sum of the total
travel distance and the expected penalty incurred by all the vehicles. We
formulate the problem as a two-stage stochastic program with recourse, present
the theoretical properties of the formulation and advantages of using such a
formulation, as opposed to a deterministic expected value formulation, to solve
the problem. Extensive numerical simulations also corroborate the effectiveness
of the proposed approach. | [
1,
0,
1,
0,
0,
0
] |
Title: Dropout-based Active Learning for Regression,
Abstract: Active learning is relevant and challenging for high-dimensional regression
models when the annotation of the samples is expensive. Yet most of the
existing sampling methods cannot be applied to large-scale problems, consuming
too much time for data processing. In this paper, we propose a fast active
learning algorithm for regression, tailored for neural network models. It is
based on uncertainty estimation from stochastic dropout output of the network.
Experiments on both synthetic and real-world datasets show comparable or better
performance (depending on the accuracy metric) as compared to the baselines.
This approach can be generalized to other deep learning architectures. It can
be used to systematically improve a machine-learning model as it offers a
computationally efficient way of sampling additional data. | [
0,
0,
0,
1,
0,
0
] |
Title: BARCHAN: Blob Alignment for Robust CHromatographic ANalysis,
Abstract: Comprehensive Two dimensional gas chromatography (GCxGC) plays a central role
into the elucidation of complex samples. The automation of the identification
of peak areas is of prime interest to obtain a fast and repeatable analysis of
chromatograms. To determine the concentration of compounds or pseudo-compounds,
templates of blobs are defined and superimposed on a reference chromatogram.
The templates then need to be modified when different chromatograms are
recorded. In this study, we present a chromatogram and template alignment
method based on peak registration called BARCHAN. Peaks are identified using a
robust mathematical morphology tool. The alignment is performed by a
probabilistic estimation of a rigid transformation along the first dimension,
and a non-rigid transformation in the second dimension, taking into account
noise, outliers and missing peaks in a fully automated way. Resulting aligned
chromatograms and masks are presented on two datasets. The proposed algorithm
proves to be fast and reliable. It significantly reduces the time to results
for GCxGC analysis. | [
1,
1,
0,
0,
0,
0
] |
Title: Complex waveguide based on a magneto-optic layer and a dielectric photonic crystal,
Abstract: We theoretically investigate the dispersion and polarization properties of
the electromagnetic waves in a multi-layered structure composed of a
magneto-optic waveguide on dielectric substrate covered by one-dimensional
dielectric photonic crystal. The numerical analysis of such a complex structure
shows polarization filtration of TE- and TM-modes depending on geometrical
parameters of the waveguide and photonic crystal. We consider different regimes
of the modes propagation inside such a structure: when guiding modes propagate
inside the magnetic film and decay in the photonic crystal; when they propagate
in both magnetic film and photonic crystal. | [
0,
1,
0,
0,
0,
0
] |
Title: The Consciousness Prior,
Abstract: A new prior is proposed for representation learning, which can be combined
with other priors in order to help disentangling abstract factors from each
other. It is inspired by the phenomenon of consciousness seen as the formation
of a low-dimensional combination of a few concepts constituting a conscious
thought, i.e., consciousness as awareness at a particular time instant. This
provides a powerful constraint on the representation in that such
low-dimensional thought vectors can correspond to statements about reality
which are true, highly probable, or very useful for taking decisions. The fact
that a few elements of the current state can be combined into such a predictive
or useful statement is a strong constraint and deviates considerably from the
maximum likelihood approaches to modelling data and how states unfold in the
future based on an agent's actions. Instead of making predictions in the
sensory (e.g. pixel) space, the consciousness prior allows the agent to make
predictions in the abstract space, with only a few dimensions of that space
being involved in each of these predictions. The consciousness prior also makes
it natural to map conscious states to natural language utterances or to express
classical AI knowledge in the form of facts and rules, although the conscious
states may be richer than what can be expressed easily in the form of a
sentence, a fact or a rule. | [
1,
0,
0,
1,
0,
0
] |
Title: Some Time-changed fractional Poisson processes,
Abstract: In this paper, we study the fractional Poisson process (FPP) time-changed by
an independent Lévy subordinator and the inverse of the Lévy subordinator,
which we call TCFPP-I and TCFPP-II, respectively. Various distributional
properties of these processes are established. We show that, under certain
conditions, the TCFPP-I has the long-range dependence property and also its law
of iterated logarithm is proved. It is shown that the TCFPP-II is a renewal
process and its waiting time distribution is identified. Its bivariate
distributions and also the governing difference-differential equation are
derived. Some specific examples for both the processes are discussed. Finally,
we present the simulations of the sample paths of these processes. | [
0,
0,
1,
0,
0,
0
] |
Title: Hybrid Indexes to Expedite Spatial-Visual Search,
Abstract: Due to the growth of geo-tagged images, recent web and mobile applications
provide search capabilities for images that are similar to a given query image
and simultaneously within a given geographical area. In this paper, we focus on
designing index structures to expedite these spatial-visual searches. We start
by baseline indexes that are straightforward extensions of the current popular
spatial (R*-tree) and visual (LSH) index structures. Subsequently, we propose
hybrid index structures that evaluate both spatial and visual features in
tandem. The unique challenge of this type of query is that there are
inaccuracies in both spatial and visual features. Therefore, different
traversals of the index structures may produce different images as output, some
of which more relevant to the query than the others. We compare our hybrid
structures with a set of baseline indexes in both performance and result
accuracy using three real world datasets from Flickr, Google Street View, and
GeoUGV. | [
1,
0,
0,
0,
0,
0
] |
Title: Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography,
Abstract: Electron Cryo-Tomography (ECT) enables 3D visualization of macromolecule
structure inside single cells. Macromolecule classification approaches based on
convolutional neural networks (CNN) were developed to separate millions of
macromolecules captured from ECT systematically. However, given the fast
accumulation of ECT data, it will soon become necessary to use CNN models to
efficiently and accurately separate substantially more macromolecules at the
prediction stage, which requires additional computational costs. To speed up
the prediction, we compress classification models into compact neural networks
with little in accuracy for deployment. Specifically, we propose to perform
model compression through knowledge distillation. Firstly, a complex teacher
network is trained to generate soft labels with better classification
feasibility followed by training of customized student networks with simple
architectures using the soft label to compress model complexity. Our tests
demonstrate that our compressed models significantly reduce the number of
parameters and time cost while maintaining similar classification accuracy. | [
0,
0,
0,
1,
1,
0
] |
Title: Low quasiparticle coherence temperature in the one band-Hubbard model: A slave-boson approach,
Abstract: We use the Kotliar-Ruckenstein slave-boson formalism to study the temperature
dependence of paramagnetic phases of the one-band Hubbard model for a variety
of band structures. We calculate the Fermi liquid quasiparticle spectral weight
$Z$ and identify the temperature at which it decreases significantly to a
crossover to a bad metal region. Near the Mott metal-insulator transition, this
coherence temperature $T_\textrm{coh}$ is much lower than the Fermi temperature
of the uncorrelated Fermi gas, as is observed in a broad range of strongly
correlated electron materials. After a proper rescaling of temperature and
interaction, we find a universal behavior that is independent of the band
structure of the system. We obtain the temperature-interaction phase diagram as
a function of doping, and we compare the temperature dependence of the double
occupancy, entropy, and charge compressibility with previous results obtained
with Dynamical Mean-Field Theory. We analyse the stability of the method by
calculating the charge compressibility. | [
0,
1,
0,
0,
0,
0
] |
Title: A Note on Iterated Consistency and Infinite Proofs,
Abstract: Schmerl and Beklemishev's work on iterated reflection achieves two aims: It
introduces the important notion of $\Pi^0_1$-ordinal, characterizing the
$\Pi^0_1$-theorems of a theory in terms of transfinite iterations of
consistency; and it provides an innovative calculus to compute the
$\Pi^0_1$-ordinals for a range of theories. The present note demonstrates that
these achievements are independent: We read off $\Pi^0_1$-ordinals from a
Schütte-style ordinal analysis via infinite proofs, in a direct and
transparent way. | [
0,
0,
1,
0,
0,
0
] |
Title: Asynchronous Coordinate Descent under More Realistic Assumptions,
Abstract: Asynchronous-parallel algorithms have the potential to vastly speed up
algorithms by eliminating costly synchronization. However, our understanding to
these algorithms is limited because the current convergence of asynchronous
(block) coordinate descent algorithms are based on somewhat unrealistic
assumptions. In particular, the age of the shared optimization variables being
used to update a block is assumed to be independent of the block being updated.
Also, it is assumed that the updates are applied to randomly chosen blocks. In
this paper, we argue that these assumptions either fail to hold or will imply
less efficient implementations. We then prove the convergence of
asynchronous-parallel block coordinate descent under more realistic
assumptions, in particular, always without the independence assumption. The
analysis permits both the deterministic (essentially) cyclic and random rules
for block choices. Because a bound on the asynchronous delays may or may not be
available, we establish convergence for both bounded delays and unbounded
delays. The analysis also covers nonconvex, weakly convex, and strongly convex
functions. We construct Lyapunov functions that directly model both objective
progress and delays, so delays are not treated errors or noise. A
continuous-time ODE is provided to explain the construction at a high level. | [
0,
0,
1,
0,
0,
0
] |
Title: Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes,
Abstract: The incorporation of macro-actions (temporally extended actions) into
multi-agent decision problems has the potential to address the curse of
dimensionality associated with such decision problems. Since macro-actions last
for stochastic durations, multiple agents executing decentralized policies in
cooperative environments must act asynchronously. We present an algorithm that
modifies Generalized Advantage Estimation for temporally extended actions,
allowing a state-of-the-art policy optimization algorithm to optimize policies
in Dec-POMDPs in which agents act asynchronously. We show that our algorithm is
capable of learning optimal policies in two cooperative domains, one involving
real-time bus holding control and one involving wildfire fighting with unmanned
aircraft. Our algorithm works by framing problems as "event-driven decision
processes," which are scenarios where the sequence and timing of actions and
events are random and governed by an underlying stochastic process. In addition
to optimizing policies with continuous state and action spaces, our algorithm
also facilitates the use of event-driven simulators, which do not require time
to be discretized into time-steps. We demonstrate the benefit of using
event-driven simulation in the context of multiple agents taking asynchronous
actions. We show that fixed time-step simulation risks obfuscating the sequence
in which closely-separated events occur, adversely affecting the policies
learned. Additionally, we show that arbitrarily shrinking the time-step scales
poorly with the number of agents. | [
1,
0,
0,
0,
0,
0
] |
Title: Early Salient Region Selection Does Not Drive Rapid Visual Categorization,
Abstract: The current dominant visual processing paradigm in both human and machine
research is the feedforward, layered hierarchy of neural-like processing
elements. Within this paradigm, visual saliency is seen by many to have a
specific role, namely that of early selection. Early selection is thought to
enable very fast visual performance by limiting processing to only the most
relevant candidate portions of an image. Though this strategy has indeed led to
improved processing time efficiency in machine algorithms, at least one set of
critical tests of this idea has never been performed with respect to the role
of early selection in human vision. How would the best of the current saliency
models perform on the stimuli used by experimentalists who first provided
evidence for this visual processing paradigm? Would the algorithms really
provide correct candidate sub-images to enable fast categorization on those
same images? Here, we report on a new series of tests of these questions whose
results suggest that it is quite unlikely that such an early selection process
has any role in human rapid visual categorization. | [
0,
0,
0,
0,
1,
0
] |
Title: Preference-based performance measures for Time-Domain Global Similarity method,
Abstract: For Time-Domain Global Similarity (TDGS) method, which transforms the data
cleaning problem into a binary classification problem about the physical
similarity between channels, directly adopting common performance measures
could only guarantee the performance for physical similarity. Nevertheless,
practical data cleaning tasks have preferences for the correctness of original
data sequences. To obtain the general expressions of performance measures based
on the preferences of tasks, the mapping relations between performance of TDGS
method about physical similarity and correctness of data sequences are
investigated by probability theory in this paper. Performance measures for TDGS
method in several common data cleaning tasks are set. Cases when these
preference-based performance measures could be simplified are introduced. | [
1,
0,
0,
0,
0,
0
] |
Title: On the Prospects for Detecting a Net Photon Circular Polarization Produced by Decaying Dark Matter,
Abstract: If dark matter interactions with Standard Model particles are $CP$-violating,
then dark matter annihilation/decay can produce photons with a net circular
polarization. We consider the prospects for experimentally detecting evidence
for such a circular polarization. We identify optimal models for dark matter
interactions with the Standard Model, from the point of view of detectability
of the net polarization, for the case of either symmetric or asymmetric dark
matter. We find that, for symmetric dark matter, evidence for net polarization
could be found by a search of the Galactic Center by an instrument sensitive to
circular polarization with an efficiency-weighted exposure of at least
$50000~\text{cm}^2~\text{yr}$, provided the systematic detector uncertainties
are constrained at the $1\%$ level. Better sensitivity can be obtained in the
case of asymmetric dark matter. We discuss the prospects for achieving the
needed level of performance using possible detector technologies. | [
0,
1,
0,
0,
0,
0
] |
Title: Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe's Law and the LPPLS Model,
Abstract: We develop a strong diagnostic for bubbles and crashes in bitcoin, by
analyzing the coincidence (and its absence) of fundamental and technical
indicators. Using a generalized Metcalfe's law based on network properties, a
fundamental value is quantified and shown to be heavily exceeded, on at least
four occasions, by bubbles that grow and burst. In these bubbles, we detect a
universal super-exponential unsustainable growth. We model this universal
pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which
parsimoniously captures diverse positive feedback phenomena, such as herding
and imitation. The LPPLS model is shown to provide an ex-ante warning of market
instabilities, quantifying a high crash hazard and probabilistic bracket of the
crash time consistent with the actual corrections; although, as always, the
precise time and trigger (which straw breaks the camel's back) being exogenous
and unpredictable. Looking forward, our analysis identifies a substantial but
not unprecedented overvaluation in the price of bitcoin, suggesting many months
of volatile sideways bitcoin prices ahead (from the time of writing, March
2018). | [
0,
0,
0,
0,
0,
1
] |
Title: Variational approach for learning Markov processes from time series data,
Abstract: Inference, prediction and control of complex dynamical systems from time
series is important in many areas, including financial markets, power grid
management, climate and weather modeling, or molecular dynamics. The analysis
of such highly nonlinear dynamical systems is facilitated by the fact that we
can often find a (generally nonlinear) transformation of the system coordinates
to features in which the dynamics can be excellently approximated by a linear
Markovian model. Moreover, the large number of system variables often change
collectively on large time- and length-scales, facilitating a low-dimensional
analysis in feature space. In this paper, we introduce a variational approach
for Markov processes (VAMP) that allows us to find optimal feature mappings and
optimal Markovian models of the dynamics from given time series data. The key
insight is that the best linear model can be obtained from the top singular
components of the Koopman operator. This leads to the definition of a family of
score functions called VAMP-r which can be calculated from data, and can be
employed to optimize a Markovian model. In addition, based on the relationship
between the variational scores and approximation errors of Koopman operators,
we propose a new VAMP-E score, which can be applied to cross-validation for
hyper-parameter optimization and model selection in VAMP. VAMP is valid for
both reversible and nonreversible processes and for stationary and
non-stationary processes or realizations. | [
0,
0,
0,
1,
0,
0
] |
Title: A new class of ferromagnetic semiconductors with high Curie temperatures,
Abstract: Ferromagnetic semiconductors (FMSs), which have the properties and
functionalities of both semiconductors and ferromagnets, provide fascinating
opportunities for basic research in condensed matter physics and device
applications. Over the past two decades, however, intensive studies on various
FMS materials, inspired by the influential mean-field Zener (MFZ) model have
failed to realise reliable FMSs that have a high Curie temperature (Tc > 300
K), good compatibility with semiconductor electronics, and characteristics
superior to those of their non-magnetic host semiconductors. Here, we
demonstrate a new n type Fe-doped narrow-gap III-V FMS, (In,Fe)Sb, in which
ferromagnetic order is induced by electron carriers, and its Tc is unexpectedly
high, reaching ~335 K at a modest Fe concentration of 16%. Furthermore, we show
that by utilizing the large anomalous Hall effect of (In,Fe)Sb at room
temperature, it is possible to obtain a Hall sensor with a very high
sensitivity that surpasses that of the best commercially available InSb Hall
sensor devices. Our results reveal a new design rule of FMSs that is not
expected from the conventional MFZ model. (This work was presented at the JSAP
Spring meeting, presentation No. E15a-501-2:
this https URL) | [
0,
1,
0,
0,
0,
0
] |
Title: High-Fidelity, Single-Shot, Quantum-Logic-Assisted Readout in a Mixed-Species Ion Chain,
Abstract: We use a co-trapped ion ($^{88}\mathrm{Sr}^{+}$) to sympathetically cool and
measure the quantum state populations of a memory-qubit ion of a different
atomic species ($^{40}\mathrm{Ca}^{+}$) in a cryogenic, surface-electrode ion
trap. Due in part to the low motional heating rate demonstrated here, the state
populations of the memory ion can be transferred to the auxiliary ion by using
the shared motion as a quantum state bus and measured with an average accuracy
of 96(1)%. This scheme can be used in quantum information processors to reduce
photon-scattering-induced error in unmeasured memory qubits. | [
0,
1,
0,
0,
0,
0
] |
Title: The Intertropical Convergence Zone,
Abstract: This activity has been developed as a resource for the "EU Space Awareness"
educational programme. As part of the suite "Our Fragile Planet" together with
the "Climate Box" it addresses aspects of weather phenomena, the Earth's
climate and climate change as well as Earth observation efforts like in the
European "Copernicus" programme. This resource consists of three parts that
illustrate the power of the Sun driving a global air circulation system that is
also responsible for tropical and subtropical climate zones. Through
experiments, students learn how heated air rises above cool air and how a
continuous heat source produces air convection streams that can even drive a
propeller. Students then apply what they have learnt to complete a worksheet
that presents the big picture of the global air circulation system of the
equator region by transferring the knowledge from the previous activities in to
a larger scale. | [
0,
1,
0,
0,
0,
0
] |
Title: Nonconvex Sparse Logistic Regression with Weakly Convex Regularization,
Abstract: In this work we propose to fit a sparse logistic regression model by a weakly
convex regularized nonconvex optimization problem. The idea is based on the
finding that a weakly convex function as an approximation of the $\ell_0$
pseudo norm is able to better induce sparsity than the commonly used $\ell_1$
norm. For a class of weakly convex sparsity inducing functions, we prove the
nonconvexity of the corresponding sparse logistic regression problem, and study
its local optimality conditions and the choice of the regularization parameter
to exclude trivial solutions. Despite the nonconvexity, a method based on
proximal gradient descent is used to solve the general weakly convex sparse
logistic regression, and its convergence behavior is studied theoretically.
Then the general framework is applied to a specific weakly convex function, and
a necessary and sufficient local optimality condition is provided. The solution
method is instantiated in this case as an iterative firm-shrinkage algorithm,
and its effectiveness is demonstrated in numerical experiments by both randomly
generated and real datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Inapproximability of the independent set polynomial in the complex plane,
Abstract: We study the complexity of approximating the independent set polynomial
$Z_G(\lambda)$ of a graph $G$ with maximum degree $\Delta$ when the activity
$\lambda$ is a complex number.
This problem is already well understood when $\lambda$ is real using
connections to the $\Delta$-regular tree $T$. The key concept in that case is
the "occupation ratio" of the tree $T$. This ratio is the contribution to
$Z_T(\lambda)$ from independent sets containing the root of the tree, divided
by $Z_T(\lambda)$ itself. If $\lambda$ is such that the occupation ratio
converges to a limit, as the height of $T$ grows, then there is an FPTAS for
approximating $Z_G(\lambda)$ on a graph $G$ with maximum degree $\Delta$.
Otherwise, the approximation problem is NP-hard.
Unsurprisingly, the case where $\lambda$ is complex is more challenging.
Peters and Regts identified the complex values of $\lambda$ for which the
occupation ratio of the $\Delta$-regular tree converges. These values carve a
cardioid-shaped region $\Lambda_\Delta$ in the complex plane. Motivated by the
picture in the real case, they asked whether $\Lambda_\Delta$ marks the true
approximability threshold for general complex values $\lambda$.
Our main result shows that for every $\lambda$ outside of $\Lambda_\Delta$,
the problem of approximating $Z_G(\lambda)$ on graphs $G$ with maximum degree
at most $\Delta$ is indeed NP-hard. In fact, when $\lambda$ is outside of
$\Lambda_\Delta$ and is not a positive real number, we give the stronger result
that approximating $Z_G(\lambda)$ is actually #P-hard. If $\lambda$ is a
negative real number outside of $\Lambda_\Delta$, we show that it is #P-hard to
even decide whether $Z_G(\lambda)>0$, resolving in the affirmative a conjecture
of Harvey, Srivastava and Vondrak.
Our proof techniques are based around tools from complex analysis -
specifically the study of iterative multivariate rational maps. | [
1,
0,
0,
0,
0,
0
] |
Title: Bounds on harmonic radius and limits of manifolds with bounded Bakry-Émery Ricci curvature,
Abstract: Under the usual condition that the volume of a geodesic ball is close to the
Euclidean one or the injectivity radii is bounded from below, we prove a lower
bound of the $C^{\alpha} W^{1, q}$ harmonic radius for manifolds with bounded
Bakry-Émery Ricci curvature when the gradient of the potential is bounded.
Under these conditions, the regularity that can be imposed on the metrics under
harmonic coordinates is only $C^\alpha W^{1,q}$, where $q>2n$ and $n$ is the
dimension of the manifolds. This is almost 1 order lower than that in the
classical $C^{1,\alpha} W^{2, p}$ harmonic coordinates under bounded Ricci
curvature condition [And]. The loss of regularity induces some difference in
the method of proof, which can also be used to address the detail of $W^{2, p}$
convergence in the classical case.
Based on this lower bound and the techniques in [ChNa2] and [WZ], we extend
Cheeger-Naber's Codimension 4 Theorem in [ChNa2] to the case where the
manifolds have bounded Bakry-Émery Ricci curvature when the gradient of the
potential is bounded. This result covers Ricci solitons when the gradient of
the potential is bounded.
During the proof, we will use a Green's function argument and adopt a linear
algebra argument in [Bam]. A new ingradient is to show that the diagonal
entries of the matrices in the Transformation Theorem are bounded away from 0.
Together these seem to simplify the proof of the Codimension 4 Theorem, even in
the case where Ricci curvature is bounded. | [
0,
0,
1,
0,
0,
0
] |
Title: Adversarial Attacks on Neural Networks for Graph Data,
Abstract: Deep learning models for graphs have achieved strong performance for the task
of node classification. Despite their proliferation, currently there is no
study of their robustness to adversarial attacks. Yet, in domains where they
are likely to be used, e.g. the web, adversaries are common. Can deep learning
models for graphs be easily fooled? In this work, we introduce the first study
of adversarial attacks on attributed graphs, specifically focusing on models
exploiting ideas of graph convolutions. In addition to attacks at test time, we
tackle the more challenging class of poisoning/causative attacks, which focus
on the training phase of a machine learning model. We generate adversarial
perturbations targeting the node's features and the graph structure, thus,
taking the dependencies between instances in account. Moreover, we ensure that
the perturbations remain unnoticeable by preserving important data
characteristics. To cope with the underlying discrete domain we propose an
efficient algorithm Nettack exploiting incremental computations. Our
experimental study shows that accuracy of node classification significantly
drops even when performing only few perturbations. Even more, our attacks are
transferable: the learned attacks generalize to other state-of-the-art node
classification models and unsupervised approaches, and likewise are successful
even when only limited knowledge about the graph is given. | [
0,
0,
0,
1,
0,
0
] |
Title: Electromagnetic energy, momentum and forces in a dielectric medium with losses,
Abstract: From the energy-momentum tensors of the electromagnetic field and the
mechanical energy-momentum, the equations of energy conservation and balance of
electromagnetic and mechanical forces are obtained. The equation for the
Abraham force in a dielectric medium with losses is obtained | [
0,
1,
0,
0,
0,
0
] |
Title: Multiplication and Presence of Shielding Material from Time-Correlated Pulse-Height Measurements of Subcritical Plutonium Assemblies,
Abstract: We present the results from the first measurements of the Time-Correlated
Pulse-Height (TCPH) distributions from 4.5 kg sphere of $\alpha$-phase
weapons-grade plutonium metal in five configurations: bare, reflected by 1.27
cm and 2.54 cm of tungsten, and 2.54 cm and 7.62 cm of polyethylene. A new
method for characterizing source multiplication and shielding configuration is
also demonstrated. The method relies on solving for the underlying fission
chain timing distribution that drives the spreading of the measured TCPH
distribution. We found that a gamma distribution fits the fission chain timing
distribution well and that the fit parameters correlate with both
multiplication (rate parameter) and shielding material types (shape parameter).
The source-to-detector distance was another free parameter that we were able to
optimize, and proved to be the most well constrained parameter. MCNPX-PoliMi
simulations were used to complement the measurements and help illustrate trends
in these parameters and their relation to multiplication and the amount and
type of material coupled to the subcritical assembly. | [
0,
1,
0,
0,
0,
0
] |
Title: An Efficiently Searchable Encrypted Data Structure for Range Queries,
Abstract: At CCS 2015 Naveed et al. presented first attacks on efficiently searchable
encryption, such as deterministic and order-preserving encryption. These
plaintext guessing attacks have been further improved in subsequent work, e.g.
by Grubbs et al. in 2016. Such cryptanalysis is crucially important to sharpen
our understanding of the implications of security models. In this paper we
present an efficiently searchable, encrypted data structure that is provably
secure against these and even more powerful chosen plaintext attacks. Our data
structure supports logarithmic-time search with linear space complexity. The
indices of our data structure can be used to search by standard comparisons and
hence allow easy retrofitting to existing database management systems. We
implemented our scheme and show that its search time overhead is only 10
milliseconds compared to non-secure search. | [
1,
0,
0,
0,
0,
0
] |
Title: Identifying Vessel Branching from Fluid Stresses on Microscopic Robots,
Abstract: Objects moving in fluids experience patterns of stress on their surfaces
determined by the geometry of nearby boundaries. Flows at low Reynolds number,
as occur in microscopic vessels such as capillaries in biological tissues, have
relatively simple relations between stresses and nearby vessel geometry. Using
these relations, this paper shows how a microscopic robot moving with such
flows can use changes in stress on its surface to identify when it encounters
vessel branches. | [
1,
0,
0,
0,
0,
0
] |
Title: Iteration of Quadratic Polynomials Over Finite Fields,
Abstract: For a finite field of odd cardinality $q$, we show that the sequence of
iterates of $aX^2+c$, starting at $0$, always recurs after $O(q/\log\log q)$
steps. For $X^2+1$ the same is true for any starting value. We suggest that the
traditional "Birthday Paradox" model is inappropriate for iterates of $X^3+c$,
when $q$ is 2 mod 3. | [
0,
0,
1,
0,
0,
0
] |
Title: Constraints on the Growth and Spin of the Supermassive Black Hole in M32 From High Cadence Visible Light Observations,
Abstract: We present 1-second cadence observations of M32 (NGC221) with the CHIMERA
instrument at the Hale 200-inch telescope of the Palomar Observatory. Using
field stars as a baseline for relative photometry, we are able to construct a
light curve of the nucleus in the g-prime and r-prime band with 1sigma=36
milli-mag photometric stability. We derive a temporal power spectrum for the
nucleus and find no evidence for a time-variable signal above the noise as
would be expected if the nuclear black hole were accreting gas. Thus, we are
unable to constrain the spin of the black hole although future work will use
this powerful instrument to target more actively accreting black holes. Given
the black hole mass of (2.5+/-0.5)*10^6 Msun inferred from stellar kinematics,
the absence of a contribution from a nuclear time-variable signal places an
upper limit on the accretion rate which is 4.6*10^{-8} of the Eddington rate, a
factor of two more stringent than past upper limits from HST. The low mass of
the black hole despite the high stellar density suggests that the gas liberated
by stellar interactions was primarily at early cosmic times when the low-mass
black hole had a small Eddington luminosity. This is at least partly driven by
a top-heavy stellar initial mass function at early cosmic times which is an
efficient producer of stellar mass black holes. The implication is that
supermassive black holes likely arise from seeds formed through the coalescence
of 3-100 Msun mass black holes that then accrete gas produced through stellar
interaction processes. | [
0,
1,
0,
0,
0,
0
] |
Title: Sampling for Approximate Bipartite Network Projection,
Abstract: Bipartite networks manifest as a stream of edges that represent transactions,
e.g., purchases by retail customers. Many machine learning applications employ
neighborhood-based measures to characterize the similarity among the nodes,
such as the pairwise number of common neighbors (CN) and related metrics. While
the number of node pairs that share neighbors is potentially enormous, only a
relatively small proportion of them have many common neighbors. This motivates
finding a weighted sampling approach to preferentially sample these node pairs.
This paper presents a new sampling algorithm that provides a fixed size
unbiased estimate of the similarity matrix resulting from a bipartite graph
stream projection. The algorithm has two components. First, it maintains a
reservoir of sampled bipartite edges with sampling weights that favor selection
of high similarity nodes. Second, arriving edges generate a stream of
\textsl{similarity updates} based on their adjacency with the current sample.
These updates are aggregated in a second reservoir sample-based stream
aggregator to yield the final unbiased estimate. Experiments on real world
graphs show that a 10% sample at each stage yields estimates of high similarity
edges with weighted relative errors of about 1%. | [
1,
0,
1,
0,
0,
0
] |
Title: Navigate, Understand, Communicate: How Developers Locate Performance Bugs,
Abstract: Background: Performance bugs can lead to severe issues regarding computation
efficiency, power consumption, and user experience. Locating these bugs is a
difficult task because developers have to judge for every costly operation
whether runtime is consumed necessarily or unnecessarily. Objective: We wanted
to investigate how developers, when locating performance bugs, navigate through
the code, understand the program, and communicate the detected issues. Method:
We performed a qualitative user study observing twelve developers trying to fix
documented performance bugs in two open source projects. The developers worked
with a profiling and analysis tool that visually depicts runtime information in
a list representation and embedded into the source code view. Results: We
identified typical navigation strategies developers used for pinpointing the
bug, for instance, following method calls based on runtime consumption. The
integration of visualization and code helped developers to understand the bug.
Sketches visualizing data structures and algorithms turned out to be valuable
for externalizing and communicating the comprehension process for complex bugs.
Conclusion: Fixing a performance bug is a code comprehension and navigation
problem. Flexible navigation features based on executed methods and a close
integration of source code and performance information support the process. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction,
Abstract: Taxi demand prediction is an important building block to enabling intelligent
transportation systems in a smart city. An accurate prediction model can help
the city pre-allocate resources to meet travel demand and to reduce empty taxis
on streets which waste energy and worsen the traffic congestion. With the
increasing popularity of taxi requesting services such as Uber and Didi Chuxing
(in China), we are able to collect large-scale taxi demand data continuously.
How to utilize such big data to improve the demand prediction is an interesting
and critical real-world problem. Traditional demand prediction methods mostly
rely on time series forecasting techniques, which fail to model the complex
non-linear spatial and temporal relations. Recent advances in deep learning
have shown superior performance on traditionally challenging tasks such as
image classification by learning the complex features and correlations from
large-scale data. This breakthrough has inspired researchers to explore deep
learning techniques on traffic prediction problems. However, existing methods
on traffic prediction have only considered spatial relation (e.g., using CNN)
or temporal relation (e.g., using LSTM) independently. We propose a Deep
Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial
and temporal relations. Specifically, our proposed model consists of three
views: temporal view (modeling correlations between future demand values with
near time points via LSTM), spatial view (modeling local spatial correlation
via local CNN), and semantic view (modeling correlations among regions sharing
similar temporal patterns). Experiments on large-scale real taxi demand data
demonstrate effectiveness of our approach over state-of-the-art methods. | [
0,
0,
0,
1,
0,
0
] |
Title: A description length approach to determining the number of k-means clusters,
Abstract: We present an asymptotic criterion to determine the optimal number of
clusters in k-means. We consider k-means as data compression, and propose to
adopt the number of clusters that minimizes the estimated description length
after compression. Here we report two types of compression ratio based on two
ways to quantify the description length of data after compression. This
approach further offers a way to evaluate whether clusters obtained with
k-means have a hierarchical structure by examining whether multi-stage
compression can further reduce the description length. We applied our criteria
to determine the number of clusters to synthetic data and empirical
neuroimaging data to observe the behavior of the criteria across different
types of data set and suitability of the two types of criteria for different
datasets. We found that our method can offer reasonable clustering results that
are useful for dimension reduction. While our numerical results revealed
dependency of our criteria on the various aspects of dataset such as the
dimensionality, the description length approach proposed here provides a useful
guidance to determine the number of clusters in a principled manner when
underlying properties of the data are unknown and only inferred from
observation of data. | [
1,
0,
0,
1,
0,
0
] |
Title: Complementary legs and rational balls,
Abstract: In this note we study the Seifert rational homology spheres with two
complementary legs, i.e. with a pair of invariants whose fractions add up to
one. We give a complete classification of the Seifert manifolds with 3
exceptional fibers and two complementary legs which bound rational homology
balls. The result translates in a statement on the sliceness of some Montesinos
knots. | [
0,
0,
1,
0,
0,
0
] |
Title: Gravitational Waves from Stellar Black Hole Binaries and the Impact on Nearby Sun-like Stars,
Abstract: We investigate the impact of resonant gravitational waves on quadrupole
acoustic modes of Sun-like stars located nearby stellar black hole binary
systems (such as GW150914 and GW151226). We find that the stimulation of the
low-overtone modes by gravitational radiation can lead to sizeable photometric
amplitude variations, much larger than the predictions for amplitudes driven by
turbulent convection, which in turn are consistent with the photometric
amplitudes observed in most Sun-like stars. For accurate stellar evolution
models, using up-to-date stellar physics, we predict photometric amplitude
variations of $1$ -- $10^3$ ppm for a solar mass star located at a distance
between 1 au and 10 au from the black hole binary, and belonging to the same
multi-star system. The observation of such a phenomenon will be within the
reach of the Plato mission because telescope will observe several portions of
the Milky Way, many of which are regions of high stellar density with a
substantial mixed population of Sun-like stars and black hole binaries. | [
0,
1,
0,
0,
0,
0
] |
Title: Galaxy Rotation and Supermassive Black Hole Binary Evolution,
Abstract: Supermassive black hole (SMBH) binaries residing at the core of merging
galaxies are recently found to be strongly affected by the rotation of their
host galaxies. The highly eccentric orbits that form when the host is
counterrotating emit strong bursts of gravitational waves that propel rapid
SMBH binary coalescence. Most prior work, however, focused on planar orbits and
a uniform rotation profile, an unlikely interaction configuration. However, the
coupling between rotation and SMBH binary evolution appears to be such a strong
dynamical process that it warrants further investigation. This study uses
direct N-body simulations to isolate the effect of galaxy rotation in more
realistic interactions. In particular, we systematically vary the SMBH orbital
plane with respect to the galaxy rotation axis, the radial extent of the
rotating component, and the initial eccentricity of the SMBH binary orbit. We
find that the initial orbital plane orientation and eccentricity alone can
change the inspiral time by an order of magnitude. Because SMBH binary inspiral
and merger is such a loud gravitational wave source, these studies are critical
for the future gravitational wave detector, LISA, an ESA/NASA mission currently
set to launch by 2034. | [
0,
1,
0,
0,
0,
0
] |
Title: A Formal Approach to Exploiting Multi-Stage Attacks based on File-System Vulnerabilities of Web Applications (Extended Version),
Abstract: Web applications require access to the file-system for many different tasks.
When analyzing the security of a web application, secu- rity analysts should
thus consider the impact that file-system operations have on the security of
the whole application. Moreover, the analysis should take into consideration
how file-system vulnerabilities might in- teract with other vulnerabilities
leading an attacker to breach into the web application. In this paper, we first
propose a classification of file- system vulnerabilities, and then, based on
this classification, we present a formal approach that allows one to exploit
file-system vulnerabilities. We give a formal representation of web
applications, databases and file- systems, and show how to reason about
file-system vulnerabilities. We also show how to combine file-system
vulnerabilities and SQL-Injection vulnerabilities for the identification of
complex, multi-stage attacks. We have developed an automatic tool that
implements our approach and we show its efficiency by discussing several
real-world case studies, which are witness to the fact that our tool can
generate, and exploit, complex attacks that, to the best of our knowledge, no
other state-of-the-art-tool for the security of web applications can find. | [
1,
0,
0,
0,
0,
0
] |
Title: Multiple Access Wiretap Channel with Noiseless Feedback,
Abstract: The physical layer security in the up-link of the wireless communication
systems is often modeled as the multiple access wiretap channel (MAC-WT), and
recently it has received a lot attention. In this paper, the MAC-WT has been
re-visited by considering the situation that the legitimate receiver feeds his
received channel output back to the transmitters via two noiseless channels,
respectively. This model is called the MAC-WT with noiseless feedback. Inner
and outer bounds on the secrecy capacity region of this feedback model are
provided. To be specific, we first present a decode-and-forward (DF) inner
bound on the secrecy capacity region of this feedback model, and this bound is
constructed by allowing each transmitter to decode the other one's transmitted
message from the feedback, and then each transmitter uses the decoded message
to re-encode his own messages, i.e., this DF inner bound allows the independent
transmitters to co-operate with each other. Then, we provide a hybrid inner
bound which is strictly larger than the DF inner bound, and it is constructed
by using the feedback as a tool not only to allow the independent transmitters
to co-operate with each other, but also to generate two secret keys
respectively shared between the legitimate receiver and the two transmitters.
Finally, we give a sato-type outer bound on the secrecy capacity region of this
feedback model. The results of this paper are further explained via a Gaussian
example. | [
1,
0,
1,
0,
0,
0
] |
Title: Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs,
Abstract: We develop a new modeling framework for Inter-Subject Analysis (ISA). The
goal of ISA is to explore the dependency structure between different subjects
with the intra-subject dependency as nuisance. It has important applications in
neuroscience to explore the functional connectivity between brain regions under
natural stimuli. Our framework is based on the Gaussian graphical models, under
which ISA can be converted to the problem of estimation and inference of the
inter-subject precision matrix. The main statistical challenge is that we do
not impose sparsity constraint on the whole precision matrix and we only assume
the inter-subject part is sparse. For estimation, we propose to estimate an
alternative parameter to get around the non-sparse issue and it can achieve
asymptotic consistency even if the intra-subject dependency is dense. For
inference, we propose an "untangle and chord" procedure to de-bias our
estimator. It is valid without the sparsity assumption on the inverse Hessian
of the log-likelihood function. This inferential method is general and can be
applied to many other statistical problems, thus it is of independent
theoretical interest. Numerical experiments on both simulated and brain imaging
data validate our methods and theory. | [
0,
0,
1,
1,
0,
0
] |
Title: Nesterov's Acceleration For Approximate Newton,
Abstract: Optimization plays a key role in machine learning. Recently, stochastic
second-order methods have attracted much attention due to their low
computational cost in each iteration. However, these algorithms might perform
poorly especially if it is hard to approximate the Hessian well and
efficiently. As far as we know, there is no effective way to handle this
problem. In this paper, we resort to Nesterov's acceleration technique to
improve the convergence performance of a class of second-order methods called
approximate Newton. We give a theoretical analysis that Nesterov's acceleration
technique can improve the convergence performance for approximate Newton just
like for first-order methods. We accordingly propose an accelerated regularized
sub-sampled Newton. Our accelerated algorithm performs much better than the
original regularized sub-sampled Newton in experiments, which validates our
theory empirically. Besides, the accelerated regularized sub-sampled Newton has
good performance comparable to or even better than classical algorithms. | [
1,
0,
0,
0,
0,
0
] |
Title: Color difference makes a difference: four planet candidates around tau Ceti,
Abstract: The removal of noise typically correlated in time and wavelength is one of
the main challenges for using the radial velocity method to detect Earth
analogues. We analyze radial velocity data of tau Ceti and find robust evidence
for wavelength dependent noise. We find this noise can be modeled by a
combination of moving average models and "differential radial velocities". We
apply this noise model to various radial velocity data sets for tau Ceti, and
find four periodic signals at 20.0, 49.3, 160 and 642 d which we interpret as
planets. We identify two new signals with orbital periods of 20.0 and 49.3 d
while the other two previously suspected signals around 160 and 600 d are
quantified to a higher precision. The 20.0 d candidate is independently
detected in KECK data. All planets detected in this work have minimum masses
less than 4$M_\oplus$ with the two long period ones located around the inner
and outer edges of the habitable zone, respectively. We find that the
instrumental noise gives rise to a precision limit of the HARPS around 0.2 m/s.
We also find correlation between the HARPS data and the central moments of the
spectral line profile at around 0.5 m/s level, although these central moments
may contain both noise and signals. The signals detected in this work have
semi-amplitudes as low as 0.3 m/s, demonstrating the ability of the radial
velocity technique to detect relatively weak signals. | [
0,
1,
0,
0,
0,
0
] |
Title: Measuring and avoiding side effects using relative reachability,
Abstract: How can we design reinforcement learning agents that avoid causing
unnecessary disruptions to their environment? We argue that current approaches
to penalizing side effects can introduce bad incentives in tasks that require
irreversible actions, and in environments that contain sources of change other
than the agent. For example, some approaches give the agent an incentive to
prevent any irreversible changes in the environment, including the actions of
other agents. We introduce a general definition of side effects, based on
relative reachability of states compared to a default state, that avoids these
undesirable incentives. Using a set of gridworld experiments illustrating
relevant scenarios, we empirically compare relative reachability to penalties
based on existing definitions and show that it is the only penalty among those
tested that produces the desired behavior in all the scenarios. | [
0,
0,
0,
1,
0,
0
] |
Title: Born Again Neural Networks,
Abstract: Knowledge distillation (KD) consists of transferring knowledge from one
machine learning model (the teacher}) to another (the student). Commonly, the
teacher is a high-capacity model with formidable performance, while the student
is more compact. By transferring knowledge, one hopes to benefit from the
student's compactness. %we desire a compact model with performance close to the
teacher's. We study KD from a new perspective: rather than compressing models,
we train students parameterized identically to their teachers. Surprisingly,
these {Born-Again Networks (BANs), outperform their teachers significantly,
both on computer vision and language modeling tasks. Our experiments with BANs
based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10
(3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional
experiments explore two distillation objectives: (i) Confidence-Weighted by
Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP).
Both methods elucidate the essential components of KD, demonstrating a role of
the teacher outputs on both predicted and non-predicted classes. We present
experiments with students of various capacities, focusing on the under-explored
case where students overpower teachers. Our experiments show significant
advantages from transferring knowledge between DenseNets and ResNets in either
direction. | [
0,
0,
0,
1,
0,
0
] |
Title: Exploit Kits: The production line of the Cybercrime Economy,
Abstract: The annual cost of Cybercrime to the global economy is estimated to be around
400 billion dollar in support of which Exploit Kits have been providing
enabling technology.This paper reviews the recent developments in Exploit Kit
capability and how these are being applied in practice.In doing so it paves the
way for better understanding of the exploit kits economy that may better help
in combatting them and considers industry preparedness to respond. | [
1,
0,
0,
0,
0,
0
] |
Title: Helicity locking in light emitted from a plasmonic nanotaper,
Abstract: Surface plasmon waves carry an intrinsic transverse spin, which is locked to
its propagation direction. Apparently, when a singular plasmonic mode is guided
on a conic surface this spin-locking may lead to a strong circular polarization
of the far-field emission. Specifically, an adiabatically tapered gold nanocone
guides an a priori excited plasmonic vortex upwards where the mode accelerates
and finally beams out from the tip apex. The helicity of this beam is shown to
be single-handed and stems solely from the transverse spin-locking of the
helical plasmonic wave-front. We present a simple geometric model that fully
predicts the emerging light spin in our system. Finally we experimentally
demonstrate the helicity-locking phenomenon by using accurately fabricated
nanostructures and confirm the results with the model and numerical data. | [
0,
1,
0,
0,
0,
0
] |
Title: Declarative Statistics,
Abstract: In this work we introduce declarative statistics, a suite of declarative
modelling tools for statistical analysis. Statistical constraints represent the
key building block of declarative statistics. First, we introduce a range of
relevant counting and matrix constraints and associated decompositions, some of
which novel, that are instrumental in the design of statistical constraints.
Second, we introduce a selection of novel statistical constraints and
associated decompositions, which constitute a self-contained toolbox that can
be used to tackle a wide range of problems typically encountered by
statisticians. Finally, we deploy these statistical constraints to a wide range
of application areas drawn from classical statistics and we contrast our
framework against established practices. | [
1,
0,
0,
1,
0,
0
] |
Title: ABC of ladder operators for rationally extended quantum harmonic oscillator systems,
Abstract: The problem of construction of ladder operators for rationally extended
quantum harmonic oscillator (REQHO) systems of a general form is investigated
in the light of existence of different schemes of the Darboux-Crum-Krein-Adler
transformations by which such systems can be generated from the quantum
harmonic oscillator. Any REQHO system is characterized by the number of
separated states in its spectrum, the number of `valence bands' in which the
separated states are organized, and by the total number of the missing energy
levels and their position. All these peculiarities of a REQHO system are shown
to be detected and reflected by a trinity $(\mathcal{A}^\pm$,
$\mathcal{B}^\pm$, $\mathcal{C}^\pm$) of the basic (primary) lowering and
raising ladder operators related between themselves by certain algebraic
identities with coefficients polynomially-dependent on the Hamiltonian. We show
that all the secondary, higher-order ladder operators are obtainable by a
composition of the basic ladder operators of the trinity which form the set of
the spectrum-generating operators. Each trinity, in turn, can be constructed
from the intertwining operators of the two complementary minimal schemes of the
Darboux-Crum-Krein-Adler transformations. | [
0,
1,
1,
0,
0,
0
] |
Title: On permutation-invariance of limit theorems,
Abstract: By a classical principle of probability theory, sufficiently thin
subsequences of general sequences of random variables behave like i.i.d.\
sequences. This observation not only explains the remarkable properties of
lacunary trigonometric series, but also provides a powerful tool in many areas
of analysis, such the theory of orthogonal series and Banach space theory. In
contrast to i.i.d.\ sequences, however, the probabilistic structure of lacunary
sequences is not permutation-invariant and the analytic properties of such
sequences can change after rearrangement. In a previous paper we showed that
permutation-invariance of subsequences of the trigonometric system and related
function systems is connected with Diophantine properties of the index
sequence. In this paper we will study permutation-invariance of subsequences of
general r.v.\ sequences. | [
0,
0,
1,
0,
0,
0
] |
Title: Superconductivity at 33 - 37 K in $ALn_2$Fe$_4$As$_4$O$_2$ ($A$ = K and Cs; $Ln$ = Lanthanides),
Abstract: We have synthesized 10 new iron oxyarsenides, K$Ln_2$Fe$_4$As$_4$O$_2$ ($Ln$
= Gd, Tb, Dy, and Ho) and Cs$Ln_2$Fe$_4$As$_4$O$_2$ ($Ln$ = Nd, Sm, Gd, Tb, Dy,
and Ho), with the aid of lattice-match [between $A$Fe$_2$As$_2$ ($A$ = K and
Cs) and $Ln$FeAsO] approach. The resultant compounds possess hole-doped
conducting double FeAs layers, [$A$Fe$_4$As$_4$]$^{2-}$, that are separated by
the insulating [$Ln_2$O$_2$]$^{2+}$ slabs. Measurements of electrical
resistivity and dc magnetic susceptibility demonstrate bulk superconductivity
at $T_\mathrm{c}$ = 33 - 37 K. We find that $T_\mathrm{c}$ correlates with the
axis ratio $c/a$ for all 12442-type superconductors discovered. Also,
$T_\mathrm{c}$ tends to increase with the lattice mismatch, implying a role of
lattice instability for the enhancement of superconductivity. | [
0,
1,
0,
0,
0,
0
] |
Title: The Cooperative Output Regulation Problem of Discrete-Time Linear Multi-Agent Systems by the Adaptive Distributed Observer,
Abstract: In this paper, we first present an adaptive distributed observer for a
discrete-time leader system. This adaptive distributed observer will provide,
to each follower, not only the estimation of the leader's signal, but also the
estimation of the leader's system matrix. Then, based on the estimation of the
matrix S, we devise a discrete adaptive algorithm to calculate the solution to
the regulator equations associated with each follower, and obtain an estimated
feedforward control gain. Finally, we solve the cooperative output regulation
problem for discrete-time linear multi-agent systems by both state feedback and
output feedback adaptive distributed control laws utilizing the adaptive
distributed observer. | [
0,
0,
1,
0,
0,
0
] |
Title: Continuous Learning in Single-Incremental-Task Scenarios,
Abstract: It was recently shown that architectural, regularization and rehearsal
strategies can be used to train deep models sequentially on a number of
disjoint tasks without forgetting previously acquired knowledge. However, these
strategies are still unsatisfactory if the tasks are not disjoint but
constitute a single incremental task (e.g., class-incremental learning). In
this paper we point out the differences between multi-task and
single-incremental-task scenarios and show that well-known approaches such as
LWF, EWC and SI are not ideal for incremental task scenarios. A new approach,
denoted as AR1, combining architectural and regularization strategies is then
specifically proposed. AR1 overhead (in term of memory and computation) is very
small thus making it suitable for online learning. When tested on CORe50 and
iCIFAR-100, AR1 outperformed existing regularization strategies by a good
margin. | [
0,
0,
0,
1,
0,
0
] |
Title: Dynamic Bernoulli Embeddings for Language Evolution,
Abstract: Word embeddings are a powerful approach for unsupervised analysis of
language. Recently, Rudolph et al. (2016) developed exponential family
embeddings, which cast word embeddings in a probabilistic framework. Here, we
develop dynamic embeddings, building on exponential family embeddings to
capture how the meanings of words change over time. We use dynamic embeddings
to analyze three large collections of historical texts: the U.S. Senate
speeches from 1858 to 2009, the history of computer science ACM abstracts from
1951 to 2014, and machine learning papers on the Arxiv from 2007 to 2015. We
find dynamic embeddings provide better fits than classical embeddings and
capture interesting patterns about how language changes. | [
1,
0,
0,
1,
0,
0
] |
Title: Homotopy Decompositions of Gauge Groups over Real Surfaces,
Abstract: We analyse the homotopy types of gauge groups of principal U(n)-bundles
associated to pseudo Real vector bundles in the sense of Atiyah. We provide
satisfactory homotopy decompositions of these gauge groups into factors in
which the homotopy groups are well known. Therefore, we substantially build
upon the low dimensional homotopy groups as provided in a paper by I. Biswas,
J. Huisman, and J. Hurtubise. | [
0,
0,
1,
0,
0,
0
] |
Title: Comparing Classical and Relativistic Kinematics in First-Order Logic,
Abstract: The aim of this paper is to present a new logic-based understanding of the
connection between classical kinematics and relativistic kinematics. We show
that the axioms of special relativity can be interpreted in the language of
classical kinematics. This means that there is a logical translation function
from the language of special relativity to the language of classical kinematics
which translates the axioms of special relativity into consequences of
classical kinematics. We will also show that if we distinguish a class of
observers (representing observers stationary with respect to the "Ether") in
special relativity and exclude the non-slower-than light observers from
classical kinematics by an extra axiom, then the two theories become
definitionally equivalent (i.e., they become equivalent theories in the sense
as the theory of lattices as algebraic structures is the same as the theory of
lattices as partially ordered sets). Furthermore, we show that classical
kinematics is definitionally equivalent to classical kinematics with only
slower-than-light inertial observers, and hence by transitivity of definitional
equivalence that special relativity theory extended with "Ether" is
definitionally equivalent to classical kinematics. So within an axiomatic
framework of mathematical logic, we explicitly show that the transition from
classical kinematics to relativistic kinematics is the knowledge acquisition
that there is no "Ether", accompanied by a redefinition of the concepts of time
and space. | [
0,
0,
1,
0,
0,
0
] |
Title: Anomalous Acoustic Plasmon Mode from Topologically Protected States,
Abstract: Plasmons, the collective excitations of electrons in the bulk or at the
surface, play an important role in the properties of materials, and have
generated the field of Plasmonics. We report the observation of a highly
unusual acoustic plasmon mode on the surface of a three-dimensional topological
insulator (TI), Bi2Se3, using momentum resolved inelastic electron scattering.
In sharp contrast to ordinary plasmon modes, this mode exhibits almost linear
dispersion into the second Brillouin zone and remains prominent with remarkably
weak damping not seen in any other systems. This behavior must be associated
with the inherent robustness of the electrons in the TI surface state, so that
not only the surface Dirac states but also their collective excitations are
topologically protected. On the other hand, this mode has much smaller energy
dispersion than expected from a continuous media excitation picture, which can
be attributed to the strong coupling with surface phonons. | [
0,
1,
0,
0,
0,
0
] |
Title: Towards Gene Expression Convolutions using Gene Interaction Graphs,
Abstract: We study the challenges of applying deep learning to gene expression data. We
find experimentally that there exists non-linear signal in the data, however is
it not discovered automatically given the noise and low numbers of samples used
in most research. We discuss how gene interaction graphs (same pathway,
protein-protein, co-expression, or research paper text association) can be used
to impose a bias on a deep model similar to the spatial bias imposed by
convolutions on an image. We explore the usage of Graph Convolutional Neural
Networks coupled with dropout and gene embeddings to utilize the graph
information. We find this approach provides an advantage for particular tasks
in a low data regime but is very dependent on the quality of the graph used. We
conclude that more work should be done in this direction. We design experiments
that show why existing methods fail to capture signal that is present in the
data when features are added which clearly isolates the problem that needs to
be addressed. | [
0,
0,
0,
1,
1,
0
] |
Title: Publication Trends in Physics Education: A Bibliometric study,
Abstract: A publication trend in Physics Education by employing bibliometric analysis
leads the researchers to describe current scientific movement. This paper tries
to answer "What do Physics education scientists concentrate in their
publications?" by analyzing the productivity and development of publications on
the subject category of Physics Education in the period 1980--2013. The Web of
Science databases in the research areas of "EDUCATION - EDUCATIONAL RESEARCH"
was used to extract the publication trends. The study involves 1360
publications, including 840 articles, 503 proceedings paper, 22 reviews, 7
editorial material, 6 Book review, and one Biographical item. Number of
publications with "Physical Education" in topic increased from 0.14 % (n = 2)
in 1980 to 16.54 % (n = 225) in 2011. Total number of receiving citations is
8071, with approximately citations per papers of 5.93. The results show the
publication and citations in Physic Education has increased dramatically while
the Malaysian share is well ranked. | [
1,
1,
0,
0,
0,
0
] |
Title: Unveiling Swarm Intelligence with Network Science$-$the Metaphor Explained,
Abstract: Self-organization is a natural phenomenon that emerges in systems with a
large number of interacting components. Self-organized systems show robustness,
scalability, and flexibility, which are essential properties when handling
real-world problems. Swarm intelligence seeks to design nature-inspired
algorithms with a high degree of self-organization. Yet, we do not know why
swarm-based algorithms work well and neither we can compare the different
approaches in the literature. The lack of a common framework capable of
characterizing these several swarm-based algorithms, transcending their
particularities, has led to a stream of publications inspired by different
aspects of nature without much regard as to whether they are similar to already
existing approaches. We address this gap by introducing a network-based
framework$-$the interaction network$-$to examine computational swarm-based
systems via the optics of social dynamics. We discuss the social dimension of
several swarm classes and provide a case study of the Particle Swarm
Optimization. The interaction network enables a better understanding of the
plethora of approaches currently available by looking at them from a general
perspective focusing on the structure of the social interactions. | [
1,
0,
0,
0,
0,
0
] |
Title: Chaos and thermalization in small quantum systems,
Abstract: Chaos and ergodicity are the cornerstones of statistical physics and
thermodynamics. While classically even small systems like a particle in a
two-dimensional cavity, can exhibit chaotic behavior and thereby relax to a
microcanonical ensemble, quantum systems formally can not. Recent theoretical
breakthroughs and, in particular, the eigenstate thermalization hypothesis
(ETH) however indicate that quantum systems can also thermalize. In fact ETH
provided us with a framework connecting microscopic models and macroscopic
phenomena, based on the notion of highly entangled quantum states. Such
thermalization was beautifully demonstrated experimentally by A. Kaufman et.
al. who studied relaxation dynamics of a small lattice system of interacting
bosonic particles. By directly measuring the entanglement entropy of
subsystems, as well as other observables, they showed that after the initial
transient time the system locally relaxes to a thermal ensemble while globally
maintaining a zero-entropy pure state. | [
0,
1,
0,
0,
0,
0
] |
Title: Goldstone-like phonon modes in a (111)-strained perovskite,
Abstract: Goldstone modes are massless particles resulting from spontaneous symmetry
breaking. Although such modes are found in elementary particle physics as well
as in condensed matter systems like superfluid helium, superconductors and
magnons - structural Goldstone modes are rare. Epitaxial strain in thin films
can induce structures and properties not accessible in bulk and has been
intensively studied for (001)-oriented perovskite oxides. Here we predict
Goldstone-like phonon modes in (111)-strained SrMnO3 by first-principles
calculations. Under compressive strain the coupling between two in-plane
rotational instabilities give rise to a Mexican hat shaped energy surface
characteristic of a Goldstone mode. Conversely, large tensile strain induces
in-plane polar instabilities with no directional preference, giving rise to a
continuous polar ground state. Such phonon modes with U(1) symmetry could
emulate structural condensed matter Higgs modes. The mass of this Higgs boson,
given by the shape of the Mexican hat energy surface, can be tuned by strain
through proper choice of substrate. | [
0,
1,
0,
0,
0,
0
] |
Title: SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine,
Abstract: Traditional medicine typically applies one-size-fits-all treatment for the
entire patient population whereas precision medicine develops tailored
treatment schemes for different patient subgroups. The fact that some factors
may be more significant for a specific patient subgroup motivates clinicians
and medical researchers to develop new approaches to subgroup detection and
analysis, which is an effective strategy to personalize treatment. In this
study, we propose a novel patient subgroup detection method, called Supervised
Biclustring (SUBIC) using convex optimization and apply our approach to detect
patient subgroups and prioritize risk factors for hypertension (HTN) in a
vulnerable demographic subgroup (African-American). Our approach not only finds
patient subgroups with guidance of a clinically relevant target variable but
also identifies and prioritizes risk factors by pursuing sparsity of the input
variables and encouraging similarity among the input variables and between the
input and target variables | [
1,
0,
0,
1,
0,
0
] |
Title: Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix,
Abstract: This paper proposes a joint framework wherein lifting-based, separable,
image-matched wavelets are estimated from compressively sensed (CS) images and
used for the reconstruction of the same. Matched wavelet can be easily designed
if full image is available. Also matched wavelet may provide better
reconstruction results in CS application compared to standard wavelet
sparsifying basis. Since in CS application, we have compressively sensed image
instead of full image, existing methods of designing matched wavelet cannot be
used. Thus, we propose a joint framework that estimates matched wavelet from
the compressively sensed images and also reconstructs full images. This paper
has three significant contributions. First, lifting-based, image-matched
separable wavelet is designed from compressively sensed images and is also used
to reconstruct the same. Second, a simple sensing matrix is employed to sample
data at sub-Nyquist rate such that sensing and reconstruction time is reduced
considerably without any noticeable degradation in the reconstruction
performance. Third, a new multi-level L-Pyramid wavelet decomposition strategy
is provided for separable wavelet implementation on images that leads to
improved reconstruction performance. Compared to CS-based reconstruction using
standard wavelets with Gaussian sensing matrix and with existing wavelet
decomposition strategy, the proposed methodology provides faster and better
image reconstruction in compressive sensing application. | [
1,
0,
0,
0,
0,
0
] |
Title: Python Implementation and Construction of Finite Abelian Groups,
Abstract: Here we present a working framework to establish finite abelian groups in
python. The primary aim is to allow new A-level students to work with examples
of finite abelian groups using open source software. We include the code used
in the implementation of the framework. We also prove some useful results
regarding finite abelian groups which are used to establish the functions and
help show how number theoretic results can blend with computational power when
studying algebra. The groups established are based modular multiplication and
addition. We include direct products of cyclic groups meaning the user has
access to all finite abelian groups. | [
1,
0,
1,
0,
0,
0
] |
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