title
stringlengths 7
239
| abstract
stringlengths 7
2.76k
| cs
int64 0
1
| phy
int64 0
1
| math
int64 0
1
| stat
int64 0
1
| quantitative biology
int64 0
1
| quantitative finance
int64 0
1
|
|---|---|---|---|---|---|---|---|
Regrasping by Fixtureless Fixturing | This paper presents a fixturing strategy for regrasping that does not require
a physical fixture. To regrasp an object in a gripper, a robot pushes the
object against external contact/s in the environment such that the external
contact keeps the object stationary while the fingers slide over the object. We
call this manipulation technique fixtureless fixturing. Exploiting the
mechanics of pushing, we characterize a convex polyhedral set of pushes that
results in fixtureless fixturing. These pushes are robust against uncertainty
in the object inertia, grasping force, and the friction at the contacts. We
propose a sampling-based planner that uses the sets of robust pushes to rapidly
build a tree of reachable grasps. A path in this tree is a pushing strategy,
possibly involving pushes from different sides, to regrasp the object. We
demonstrate the experimental validity and robustness of the proposed
manipulation technique with different regrasp examples on a manipulation
platform. Such a fast and flexible regrasp planner facilitates versatile and
flexible automation solutions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Subset Labeled LDA for Large-Scale Multi-Label Classification | Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard
unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address
multi-label learning tasks. Previous work has shown it to perform in par with
other state-of-the-art multi-label methods. Nonetheless, with increasing label
sets sizes LLDA encounters scalability issues. In this work, we introduce
Subset LLDA, a simple variant of the standard LLDA algorithm, that not only can
effectively scale up to problems with hundreds of thousands of labels but also
improves over the LLDA state-of-the-art. We conduct extensive experiments on
eight data sets, with label sets sizes ranging from hundreds to hundreds of
thousands, comparing our proposed algorithm with the previously proposed LLDA
algorithms (Prior--LDA, Dep--LDA), as well as the state of the art in extreme
multi-label classification. The results show a steady advantage of our method
over the other LLDA algorithms and competitive results compared to the extreme
multi-label classification algorithms.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Hybrid Approach to Video Source Identification | Multimedia Forensics allows to determine whether videos or images have been
captured with the same device, and thus, eventually, by the same person.
Currently, the most promising technology to achieve this task, exploits the
unique traces left by the camera sensor into the visual content. Anyway, image
and video source identification are still treated separately from one another.
This approach is limited and anachronistic if we consider that most of the
visual media are today acquired using smartphones, that capture both images and
videos. In this paper we overcome this limitation by exploring a new approach
that allows to synergistically exploit images and videos to study the device
from which they both come. Indeed, we prove it is possible to identify the
source of a digital video by exploiting a reference sensor pattern noise
generated from still images taken by the same device of the query video. The
proposed method provides comparable or even better performance, when compared
to the current video identification strategies, where a reference pattern is
estimated from video frames. We also show how this strategy can be effective
even in case of in-camera digitally stabilized videos, where a non-stabilized
reference is not available, by solving some state-of-the-art limitations. We
explore a possible direct application of this result, that is social media
profile linking, i.e. discovering relationships between two or more social
media profiles by comparing the visual contents - images or videos - shared
therein.
| 1 | 0 | 0 | 0 | 0 | 0 |
Asymptotics of ABC | We present an informal review of recent work on the asymptotics of
Approximate Bayesian Computation (ABC). In particular we focus on how does the
ABC posterior, or point estimates obtained by ABC, behave in the limit as we
have more data? The results we review show that ABC can perform well in terms
of point estimation, but standard implementations will over-estimate the
uncertainty about the parameters. If we use the regression correction of
Beaumont et al. then ABC can also accurately quantify this uncertainty. The
theoretical results also have practical implications for how to implement ABC.
| 0 | 0 | 1 | 1 | 0 | 0 |
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity | We consider the problem of learning sparse polymatrix games from observations
of strategic interactions. We show that a polynomial time method based on
$\ell_{1,2}$-group regularized logistic regression recovers a game, whose Nash
equilibria are the $\epsilon$-Nash equilibria of the game from which the data
was generated (true game), in $\mathcal{O}(m^4 d^4 \log (pd))$ samples of
strategy profiles --- where $m$ is the maximum number of pure strategies of a
player, $p$ is the number of players, and $d$ is the maximum degree of the game
graph. Under slightly more stringent separability conditions on the payoff
matrices of the true game, we show that our method learns a game with the exact
same Nash equilibria as the true game. We also show that $\Omega(d \log (pm))$
samples are necessary for any method to consistently recover a game, with the
same Nash-equilibria as the true game, from observations of strategic
interactions. We verify our theoretical results through simulation experiments.
| 1 | 0 | 0 | 0 | 0 | 0 |
Superposition solutions to the extended KdV equation for water surface waves | The KdV equation can be derived in the shallow water limit of the Euler
equations. Over the last few decades, this equation has been extended to
include higher order effects. Although this equation has only one conservation
law, exact periodic and solitonic solutions exist. Khare and Saxena
\cite{KhSa,KhSa14,KhSa15} demonstrated the possibility of generating new exact
solutions by combining known ones for several fundamental equations (e.g.,
Korteweg - de Vries, Nonlinear Schrödinger). Here we find that this
construction can be repeated for higher order, non-integrable extensions of
these equations. Contrary to many statements in the literature, there seems to
be no correlation between integrability and the number of nonlinear one
variable wave solutions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Boosting the Actor with Dual Critic | This paper proposes a new actor-critic-style algorithm called Dual
Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian
dual form of the Bellman optimality equation, which can be viewed as a
two-player game between the actor and a critic-like function, which is named as
dual critic. Compared to its actor-critic relatives, Dual-AC has the desired
property that the actor and dual critic are updated cooperatively to optimize
the same objective function, providing a more transparent way for learning the
critic that is directly related to the objective function of the actor. We then
provide a concrete algorithm that can effectively solve the minimax
optimization problem, using techniques of multi-step bootstrapping, path
regularization, and stochastic dual ascent algorithm. We demonstrate that the
proposed algorithm achieves the state-of-the-art performances across several
benchmarks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Counting Dominating Sets of Graphs | Counting dominating sets in a graph $G$ is closely related to the
neighborhood complex of $G$. We exploit this relation to prove that the number
of dominating sets $d(G)$ of a graph is determined by the number of complete
bipartite subgraphs of its complement. More precisely, we state the following.
Let $G$ be a simple graph of order $n$ such that its complement has exactly
$a(G)$ subgraphs isomorphic to $K_{2p,2q}$ and exactly $b(G)$ subgraphs
isomorphic to $K_{2p+1,2q+1}$. Then $d(G) = 2^n -1 + 2[a(G)-b(G)]$. We also
show some new relations between the domination polynomial and the neighborhood
polynomial of a graph.
| 0 | 0 | 1 | 0 | 0 | 0 |
High SNR Consistent Compressive Sensing | High signal to noise ratio (SNR) consistency of model selection criteria in
linear regression models has attracted a lot of attention recently. However,
most of the existing literature on high SNR consistency deals with model order
selection. Further, the limited literature available on the high SNR
consistency of subset selection procedures (SSPs) is applicable to linear
regression with full rank measurement matrices only. Hence, the performance of
SSPs used in underdetermined linear models (a.k.a compressive sensing (CS)
algorithms) at high SNR is largely unknown. This paper fills this gap by
deriving necessary and sufficient conditions for the high SNR consistency of
popular CS algorithms like $l_0$-minimization, basis pursuit de-noising or
LASSO, orthogonal matching pursuit and Dantzig selector. Necessary conditions
analytically establish the high SNR inconsistency of CS algorithms when used
with the tuning parameters discussed in literature. Novel tuning parameters
with SNR adaptations are developed using the sufficient conditions and the
choice of SNR adaptations are discussed analytically using convergence rate
analysis. CS algorithms with the proposed tuning parameters are numerically
shown to be high SNR consistent and outperform existing tuning parameters in
the moderate to high SNR regime.
| 1 | 0 | 0 | 1 | 0 | 0 |
Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions | Reduction of communication and efficient partitioning are key issues for
achieving scalability in hierarchical $N$-Body algorithms like FMM. In the
present work, we propose four independent strategies to improve partitioning
and reduce communication. First of all, we show that the conventional wisdom of
using space-filling curve partitioning may not work well for boundary integral
problems, which constitute about 50% of FMM's application user base. We propose
an alternative method which modifies orthogonal recursive bisection to solve
the cell-partition misalignment that has kept it from scaling previously.
Secondly, we optimize the granularity of communication to find the optimal
balance between a bulk-synchronous collective communication of the local
essential tree and an RDMA per task per cell. Finally, we take the dynamic
sparse data exchange proposed by Hoefler et al. and extend it to a hierarchical
sparse data exchange, which is demonstrated at scale to be faster than the MPI
library's MPI_Alltoallv that is commonly used.
| 1 | 0 | 0 | 0 | 0 | 0 |
Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement | In this paper, we focus on fully automatic traffic surveillance camera
calibration, which we use for speed measurement of passing vehicles. We improve
over a recent state-of-the-art camera calibration method for traffic
surveillance based on two detected vanishing points. More importantly, we
propose a novel automatic scene scale inference method. The method is based on
matching bounding boxes of rendered 3D models of vehicles with detected
bounding boxes in the image. The proposed method can be used from arbitrary
viewpoints, since it has no constraints on camera placement. We evaluate our
method on the recent comprehensive dataset for speed measurement BrnoCompSpeed.
Experiments show that our automatic camera calibration method by detection of
two vanishing points reduces error by 50% (mean distance ratio error reduced
from 0.18 to 0.09) compared to the previous state-of-the-art method. We also
show that our scene scale inference method is more precise, outperforming both
state-of-the-art automatic calibration method for speed measurement (error
reduction by 86% -- 7.98km/h to 1.10km/h) and manual calibration (error
reduction by 19% -- 1.35km/h to 1.10km/h). We also present qualitative results
of the proposed automatic camera calibration method on video sequences obtained
from real surveillance cameras in various places, and under different lighting
conditions (night, dawn, day).
| 1 | 0 | 0 | 0 | 0 | 0 |
Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting | The success of autonomous systems will depend upon their ability to safely
navigate human-centric environments. This motivates the need for a real-time,
probabilistic forecasting algorithm for pedestrians, cyclists, and other agents
since these predictions will form a necessary step in assessing the risk of any
action. This paper presents a novel approach to probabilistic forecasting for
pedestrians based on weighted sums of ordinary differential equations that are
learned from historical trajectory information within a fixed scene. The
resulting algorithm is embarrassingly parallel and is able to work at real-time
speeds using a naive Python implementation. The quality of predicted locations
of agents generated by the proposed algorithm is validated on a variety of
examples and considerably higher than existing state of the art approaches over
long time horizons.
| 1 | 0 | 1 | 0 | 0 | 0 |
Distance Measure Machines | This paper presents a distance-based discriminative framework for learning
with probability distributions. Instead of using kernel mean embeddings or
generalized radial basis kernels, we introduce embeddings based on
dissimilarity of distributions to some reference distributions denoted as
templates. Our framework extends the theory of similarity of Balcan et al.
(2008) to the population distribution case and we show that, for some learning
problems, some dissimilarity on distribution achieves low-error linear decision
functions with high probability. Our key result is to prove that the theory
also holds for empirical distributions. Algorithmically, the proposed approach
consists in computing a mapping based on pairwise dissimilarity where learning
a linear decision function is amenable. Our experimental results show that the
Wasserstein distance embedding performs better than kernel mean embeddings and
computing Wasserstein distance is far more tractable than estimating pairwise
Kullback-Leibler divergence of empirical distributions.
| 0 | 0 | 0 | 1 | 0 | 0 |
DSBGK Method to Incorporate the CLL Reflection Model and to Simulate Gas Mixtures | Molecular reflections on usual wall surfaces can be statistically described
by the Maxwell diffuse reflection model, which has been successfully applied in
the DSBGK simulations. We develop the DSBGK algorithm to implement the
Cercignani-Lampis-Lord (CLL) reflection model, which is widely applied to
polished surfaces and used particularly in modeling space shuttles to predict
the heat and force loads exerted by the high-speed flows around the surfaces.
We also extend the DSBGK method to simulate gas mixtures and high contrast of
number densities of different components can be handled at a cost of memory
usage much lower than that needed by the DSMC simulations because the average
numbers of simulated molecules of different components per cell can be equal in
the DSBGK simulations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Tropical formulae for summation over a part of SL(2, Z) | Let $f(a,b,c,d)=\sqrt{a^2+b^2}+\sqrt{c^2+d^2}-\sqrt{(a+c)^2+(b+d)^2}$, let
$(a,b,c,d)$ stand for $a,b,c,d\in\mathbb Z_{\geq 0}$ such that $ad-bc=1$.
Define \begin{equation} \label{eq_main} F(s) = \sum_{(a,b,c,d)} f(a,b,c,d)^s.
\end{equation} In other words, we consider the sum of the powers of the
triangle inequality defects for the lattice parallelograms (in the first
quadrant) of area one.
We prove that $F(s)$ converges when $s>1/2$ and diverges at $s=1/2$. We also
prove $$\sum\limits_{\substack{(a,b,c,d),\\ 1\leq a\leq b, 1\leq c\leq d}}
\frac{1}{(a+b)^2(c+d)^2(a+b+c+d)^2} = 1/24,$$ and show a general method to
obtain such formulae. The method comes from the consideration of the tropical
analogue of the caustic curves, whose moduli give a complete set of continuous
invariants on the space of convex domains.
| 0 | 0 | 1 | 0 | 0 | 0 |
Efficient sampling of conditioned Markov jump processes | We consider the task of generating draws from a Markov jump process (MJP)
between two time points at which the process is known. Resulting draws are
typically termed bridges and the generation of such bridges plays a key role in
simulation-based inference algorithms for MJPs. The problem is challenging due
to the intractability of the conditioned process, necessitating the use of
computationally intensive methods such as weighted resampling or Markov chain
Monte Carlo. An efficient implementation of such schemes requires an
approximation of the intractable conditioned hazard/propensity function that is
both cheap and accurate. In this paper, we review some existing approaches to
this problem before outlining our novel contribution. Essentially, we leverage
the tractability of a Gaussian approximation of the MJP and suggest a
computationally efficient implementation of the resulting conditioned hazard
approximation. We compare and contrast our approach with existing methods using
three examples.
| 0 | 0 | 0 | 1 | 0 | 0 |
Holography and thermalization in optical pump-probe spectroscopy | Using holography, we model experiments in which a 2+1D strange metal is
pumped by a laser pulse into a highly excited state, after which the time
evolution of the optical conductivity is probed. We consider a finite-density
state with mildly broken translation invariance and excite it by oscillating
electric field pulses. At zero density, the optical conductivity would assume
its thermalized value immediately after the pumping has ended. At finite
density, pulses with significant DC components give rise to slow exponential
relaxation, governed by a vector quasinormal mode. In contrast, for
high-frequency pulses the amplitude of the quasinormal mode is strongly
suppressed, so that the optical conductivity assumes its thermalized value
effectively instantaneously. This surprising prediction may provide a stimulus
for taking up the challenge to realize these experiments in the laboratory.
Such experiments would test a crucial open question faced by applied
holography: Are its predictions artefacts of the large $N$ limit or do they
enjoy sufficient UV independence to hold at least qualitatively in real-world
systems?
| 0 | 1 | 0 | 0 | 0 | 0 |
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis | In this paper, we study random subsampling of Gaussian process regression,
one of the simplest approximation baselines, from a theoretical perspective.
Although subsampling discards a large part of training data, we show provable
guarantees on the accuracy of the predictive mean/variance and its
generalization ability. For analysis, we consider embedding kernel matrices
into graphons, which encapsulate the difference of the sample size and enables
us to evaluate the approximation and generalization errors in a unified manner.
The experimental results show that the subsampling approximation achieves a
better trade-off regarding accuracy and runtime than the Nyström and random
Fourier expansion methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Representing Hybrid Automata by Action Language Modulo Theories | Both hybrid automata and action languages are formalisms for describing the
evolution of dynamic systems. This paper establishes a formal relationship
between them. We show how to succinctly represent hybrid automata in an action
language which in turn is defined as a high-level notation for answer set
programming modulo theories (ASPMT) --- an extension of answer set programs to
the first-order level similar to the way satisfiability modulo theories (SMT)
extends propositional satisfiability (SAT). We first show how to represent
linear hybrid automata with convex invariants by an action language modulo
theories. A further translation into SMT allows for computing them using SMT
solvers that support arithmetic over reals. Next, we extend the representation
to the general class of non-linear hybrid automata allowing even non-convex
invariants. We represent them by an action language modulo ODE (Ordinary
Differential Equations), which can be compiled into satisfiability modulo ODE.
We developed a prototype system cplus2aspmt based on these translations, which
allows for a succinct representation of hybrid transition systems that can be
computed effectively by the state-of-the-art SMT solver dReal.
| 1 | 0 | 0 | 0 | 0 | 0 |
An enthalpy-based multiple-relaxation-time lattice Boltzmann method for solid-liquid phase change heat transfer in metal foams | In this paper, an enthalpy-based multiple-relaxation-time (MRT) lattice
Boltzmann (LB) method is developed for solid-liquid phase change heat transfer
in metal foams under local thermal non-equilibrium (LTNE) condition. The
enthalpy-based MRT-LB method consists of three different MRT-LB models: one for
flow field based on the generalized non-Darcy model, and the other two for
phase change material (PCM) and metal foam temperature fields described by the
LTNE model. The moving solid-liquid phase interface is implicitly tracked
through the liquid fraction, which is simultaneously obtained when the energy
equations of PCM and metal foam are solved. The present method has several
distinctive features. First, as compared with previous studies, the present
method avoids the iteration procedure, thus it retains the inherent merits of
the standard LB method and is superior over the iteration method in terms of
accuracy and computational efficiency. Second, a volumetric LB scheme instead
of the bounce-back scheme is employed to realize the no-slip velocity condition
in the interface and solid phase regions, which is consistent with the actual
situation. Last but not least, the MRT collision model is employed, and with
additional degrees of freedom, it has the ability to reduce the numerical
diffusion across phase interface induced by solid-liquid phase change.
Numerical tests demonstrate that the present method can be served as an
accurate and efficient numerical tool for studying metal foam enhanced
solid-liquid phase change heat transfer in latent heat storage. Finally,
comparisons and discussions are made to offer useful information for practical
applications of the present method.
| 0 | 1 | 0 | 0 | 0 | 0 |
Birth of a subaqueous barchan dune | Barchan dunes are crescentic shape dunes with horns pointing downstream. The
present paper reports the formation of subaqueous barchan dunes from initially
conical heaps in a rectangular channel. Because the most unique feature of a
barchan dune is its horns, we associate the timescale for the appearance of
horns to the formation of a barchan dune. A granular heap initially conical was
placed on the bottom wall of a closed conduit and it was entrained by a water
flow in turbulent regime. After a certain time, horns appear and grow, until an
equilibrium length is reached. Our results show the existence of the timescales
$0.5t_c$ and $2.5t_c$ for the appearance and equilibrium of horns,
respectively, where $t_c$ is a characteristic time that scales with the grains
diameter, gravity acceleration, densities of the fluid and grains, and shear
and threshold velocities.
| 0 | 1 | 0 | 0 | 0 | 0 |
Uncoupled isotonic regression via minimum Wasserstein deconvolution | Isotonic regression is a standard problem in shape-constrained estimation
where the goal is to estimate an unknown nondecreasing regression function $f$
from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots
n$. While this problem is well understood both statistically and
computationally, much less is known about its uncoupled counterpart where one
is given only the unordered sets $\{x_1, \ldots, x_n\}$ and $\{y_1, \ldots,
y_n\}$. In this work, we leverage tools from optimal transport theory to derive
minimax rates under weak moments conditions on $y_i$ and to give an efficient
algorithm achieving optimal rates. Both upper and lower bounds employ
moment-matching arguments that are also pertinent to learning mixtures of
distributions and deconvolution.
| 0 | 0 | 0 | 1 | 0 | 0 |
Failure of Smooth Pasting Principle and Nonexistence of Equilibrium Stopping Rules under Time-Inconsistency | This paper considers a time-inconsistent stopping problem in which the
inconsistency arises from non-constant time preference rates. We show that the
smooth pasting principle, the main approach that has been used to construct
explicit solutions for conventional time-consistent optimal stopping problems,
may fail under time-inconsistency. Specifically, we prove that the smooth
pasting principle solves a time-inconsistent problem within the intra-personal
game theoretic framework if and only if a certain inequality on the model
primitives is satisfied. We show that the violation of this inequality can
happen even for very simple non-exponential discount functions. Moreover, we
demonstrate that the stopping problem does not admit any intra-personal
equilibrium whenever the smooth pasting principle fails. The "negative" results
in this paper caution blindly extending the classical approaches for
time-consistent stopping problems to their time-inconsistent counterparts.
| 0 | 0 | 0 | 0 | 0 | 1 |
Perils of Zero-Interaction Security in the Internet of Things | The Internet of Things (IoT) demands authentication systems which can provide
both security and usability. Recent research utilizes the rich sensing
capabilities of smart devices to build security schemes operating without human
interaction, such as zero-interaction pairing (ZIP) and zero-interaction
authentication (ZIA). Prior work proposed a number of ZIP and ZIA schemes and
reported promising results. However, those schemes were often evaluated under
conditions which do not reflect realistic IoT scenarios. In addition, drawing
any comparison among the existing schemes is impossible due to the lack of a
common public dataset and unavailability of scheme implementations.
In this paper, we address these challenges by conducting the first
large-scale comparative study of ZIP and ZIA schemes, carried out under
realistic conditions. We collect and release the most comprehensive dataset in
the domain to date, containing over 4250 hours of audio recordings and 1
billion sensor readings from three different scenarios, and evaluate five
state-of-the-art schemes based on these data. Our study reveals that the
effectiveness of the existing proposals is highly dependent on the scenario
they are used in. In particular, we show that these schemes are subject to
error rates between 0.6% and 52.8%.
| 1 | 0 | 0 | 0 | 0 | 0 |
Coarse-grained simulation of auxetic, two-dimensional crystal dynamics | The increasing number of protein-based metamaterials demands reliable and
efficient methods to study the physicochemical properties they may display. In
this regard, we develop a simulation strategy based on Molecular Dynamics (MD)
that addresses the geometric degrees of freedom of an auxetic two-dimensional
protein crystal. This model consists of a network of impenetrable rigid squares
linked through massless rigid rods, thus featuring a large number of both
holonomic and nonholonomic constraints. Our MD methodology is optimized to
study highly constrained systems and allows for the simulation of long-time
dynamics with reasonably large timesteps. The data extracted from the
simulations shows a persistent motional interdependence among the protein
subunits in the crystal. We characterize the dynamical correlations featured by
these subunits and identify two regimes characterized by their locality or
nonlocality, depending on the geometric parameters of the crystal. From the
same data, we also calculate the Poisson\rq{}s (longitudinal to axial strain)
ratio of the crystal, and learn that, due to holonomic constraints (rigidness
of the rod links), the crystal remains auxetic even after significant changes
in the original geometry. The nonholonomic ones (collisions between subunits)
increase the number of inhomogeneous deformations of the crystal, thus driving
it away from an isotropic response. Our work provides the first simulation of
the dynamics of protein crystals and offers insights into promising mechanical
properties afforded by these materials.
| 0 | 1 | 0 | 0 | 0 | 0 |
Core2Vec: A core-preserving feature learning framework for networks | Recent advances in the field of network representation learning are mostly
attributed to the application of the skip-gram model in the context of graphs.
State-of-the-art analogues of skip-gram model in graphs define a notion of
neighbourhood and aim to find the vector representation for a node, which
maximizes the likelihood of preserving this neighborhood.
In this paper, we take a drastic departure from the existing notion of
neighbourhood of a node by utilizing the idea of coreness. More specifically,
we utilize the well-established idea that nodes with similar core numbers play
equivalent roles in the network and hence induce a novel and an organic notion
of neighbourhood. Based on this idea, we propose core2vec, a new algorithmic
framework for learning low dimensional continuous feature mapping for a node.
Consequently, the nodes having similar core numbers are relatively closer in
the vector space that we learn.
We further demonstrate the effectiveness of core2vec by comparing word
similarity scores obtained by our method where the node representations are
drawn from standard word association graphs against scores computed by other
state-of-the-art network representation techniques like node2vec, DeepWalk and
LINE. Our results always outperform these existing methods
| 1 | 0 | 0 | 0 | 0 | 0 |
Predicting wind pressures around circular cylinders using machine learning techniques | Numerous studies have been carried out to measure wind pressures around
circular cylinders since the early 20th century due to its engineering
significance. Consequently, a large amount of wind pressure data sets have
accumulated, which presents an excellent opportunity for using machine learning
(ML) techniques to train models to predict wind pressures around circular
cylinders. Wind pressures around smooth circular cylinders are a function of
mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident
wind, and circumferential angle of the cylinder. Considering these three
parameters as the inputs, this study trained two ML models to predict mean and
fluctuating pressures respectively. Three machine learning algorithms including
decision tree regressor, random forest, and gradient boosting regression trees
(GBRT) were tested. The GBRT models exhibited the best performance for
predicting both mean and fluctuating pressures, and they are capable of making
accurate predictions for Re ranging from 10^4 to 10^6 and Ti ranging from 0% to
15%. It is believed that the GBRT models provide very efficient and economical
alternative to traditional wind tunnel tests and computational fluid dynamic
simulations for determining wind pressures around smooth circular cylinders
within the studied Re and Ti range.
| 1 | 0 | 0 | 1 | 0 | 0 |
Randomly coloring simple hypergraphs with fewer colors | We study the problem of constructing a (near) uniform random proper
$q$-coloring of a simple $k$-uniform hypergraph with $n$ vertices and maximum
degree $\Delta$. (Proper in that no edge is mono-colored and simple in that two
edges have maximum intersection of size one). We show that if $q\geq
\max\{C_k\log n,500k^3\Delta^{1/(k-1)}\}$ then the Glauber Dynamics will become
close to uniform in $O(n\log n)$ time, given a random (improper) start. This
improves on the results in Frieze and Melsted [5].
| 1 | 0 | 0 | 0 | 0 | 0 |
Contributed Discussion to Uncertainty Quantification for the Horseshoe by Stéphanie van der Pas, Botond Szabó and Aad van der Vaart | We begin by introducing the main ideas of the paper under discussion. We
discuss some interesting issues regarding adaptive component-wise credible
intervals. We then briefly touch upon the concepts of self-similarity and
excessive bias restriction. This is then followed by some comments on the
extensive simulation study carried out in the paper.
| 0 | 0 | 1 | 1 | 0 | 0 |
Channel masking for multivariate time series shapelets | Time series shapelets are discriminative sub-sequences and their similarity
to time series can be used for time series classification. Initial shapelet
extraction algorithms searched shapelets by complete enumeration of all
possible data sub-sequences. Research on shapelets for univariate time series
proposed a mechanism called shapelet learning which parameterizes the shapelets
and learns them jointly with a prediction model in an optimization procedure.
Trivial extension of this method to multivariate time series does not yield
very good results due to the presence of noisy channels which lead to
overfitting. In this paper we propose a shapelet learning scheme for
multivariate time series in which we introduce channel masks to discount noisy
channels and serve as an implicit regularization.
| 1 | 0 | 0 | 0 | 0 | 0 |
Spin mediated enhanced negative magnetoresistance in Ni80Fe20 and p-silicon bilayer | In this work, we present an experimental study of spin mediated enhanced
negative magnetoresistance in Ni80Fe20 (50 nm)/p-Si (350 nm) bilayer. The
resistance measurement shows a reduction of ~2.5% for the bilayer specimen as
compared to 1.3% for Ni80Fe20 (50 nm) on oxide specimen for an out-of-plane
applied magnetic field of 3T. In the Ni80Fe20-only film, the negative
magnetoresistance behavior is attributed to anisotropic magnetoresistance. We
propose that spin polarization due to spin-Hall effect is the underlying cause
of the enhanced negative magnetoresistance observed in the bilayer. Silicon has
weak spin orbit coupling so spin Hall magnetoresistance measurement is not
feasible. We use V2{\omega} and V3{\omega} measurement as a function of
magnetic field and angular rotation of magnetic field in direction normal to
electric current to elucidate the spin-Hall effect. The angular rotation of
magnetic field shows a sinusoidal behavior for both V2{\omega} and V3{\omega},
which is attributed to the spin phonon interactions resulting from the
spin-Hall effect mediated spin polarization. We propose that the spin
polarization leads to a decrease in hole-phonon scattering resulting in
enhanced negative magnetoresistance.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the Limitations of Representing Functions on Sets | Recent work on the representation of functions on sets has considered the use
of summation in a latent space to enforce permutation invariance. In
particular, it has been conjectured that the dimension of this latent space may
remain fixed as the cardinality of the sets under consideration increases.
However, we demonstrate that the analysis leading to this conjecture requires
mappings which are highly discontinuous and argue that this is only of limited
practical use. Motivated by this observation, we prove that an implementation
of this model via continuous mappings (as provided by e.g. neural networks or
Gaussian processes) actually imposes a constraint on the dimensionality of the
latent space. Practical universal function representation for set inputs can
only be achieved with a latent dimension at least the size of the maximum
number of input elements.
| 1 | 0 | 0 | 1 | 0 | 0 |
Learning Models from Data with Measurement Error: Tackling Underreporting | Measurement error in observational datasets can lead to systematic bias in
inferences based on these datasets. As studies based on observational data are
increasingly used to inform decisions with real-world impact, it is critical
that we develop a robust set of techniques for analyzing and adjusting for
these biases. In this paper we present a method for estimating the distribution
of an outcome given a binary exposure that is subject to underreporting. Our
method is based on a missing data view of the measurement error problem, where
the true exposure is treated as a latent variable that is marginalized out of a
joint model. We prove three different conditions under which the outcome
distribution can still be identified from data containing only error-prone
observations of the exposure. We demonstrate this method on synthetic data and
analyze its sensitivity to near violations of the identifiability conditions.
Finally, we use this method to estimate the effects of maternal smoking and
opioid use during pregnancy on childhood obesity, two import problems from
public health. Using the proposed method, we estimate these effects using only
subject-reported drug use data and substantially refine the range of estimates
generated by a sensitivity analysis-based approach. Further, the estimates
produced by our method are consistent with existing literature on both the
effects of maternal smoking and the rate at which subjects underreport smoking.
| 1 | 0 | 0 | 1 | 0 | 0 |
A simple introduction to Karmarkar's Algorithm for Linear Programming | An extremely simple, description of Karmarkar's algorithm with very few
technical terms is given.
| 1 | 0 | 0 | 0 | 0 | 0 |
Magneto-inductive Passive Relaying in Arbitrarily Arranged Networks | We consider a wireless sensor network that uses inductive near-field coupling
for wireless powering or communication, or for both. The severely limited range
of an inductively coupled source-destination pair can be improved using
resonant relay devices, which are purely passive in nature. Utilization of such
magneto-inductive relays has only been studied for regular network topologies,
allowing simplified assumptions on the mutual antenna couplings. In this work
we present an analysis of magneto-inductive passive relaying in arbitrarily
arranged networks. We find that the resulting channel has characteristics
similar to multipath fading: the channel power gain is governed by a
non-coherent sum of phasors, resulting in increased frequency selectivity. We
propose and study two strategies to increase the channel power gain of random
relay networks: i) deactivation of individual relays by open-circuit switching
and ii) frequency tuning. The presented results show that both methods improve
the utilization of available passive relays, leading to reliable and
significant performance gains.
| 1 | 0 | 0 | 0 | 0 | 0 |
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces | Transfer operators such as the Perron--Frobenius or Koopman operator play an
important role in the global analysis of complex dynamical systems. The
eigenfunctions of these operators can be used to detect metastable sets, to
project the dynamics onto the dominant slow processes, or to separate
superimposed signals. We extend transfer operator theory to reproducing kernel
Hilbert spaces and show that these operators are related to Hilbert space
representations of conditional distributions, known as conditional mean
embeddings in the machine learning community. Moreover, numerical methods to
compute empirical estimates of these embeddings are akin to data-driven methods
for the approximation of transfer operators such as extended dynamic mode
decomposition and its variants. One main benefit of the presented kernel-based
approaches is that these methods can be applied to any domain where a
similarity measure given by a kernel is available. We illustrate the results
with the aid of guiding examples and highlight potential applications in
molecular dynamics as well as video and text data analysis.
| 1 | 0 | 0 | 1 | 0 | 0 |
Asymptotics of the bound state induced by $δ$-interaction supported on a weakly deformed plane | In this paper we consider the three-dimensional Schrödinger operator with
a $\delta$-interaction of strength $\alpha > 0$ supported on an unbounded
surface parametrized by the mapping $\mathbb{R}^2\ni x\mapsto (x,\beta f(x))$,
where $\beta \in [0,\infty)$ and $f\colon \mathbb{R}^2\rightarrow\mathbb{R}$,
$f\not\equiv 0$, is a $C^2$-smooth, compactly supported function. The surface
supporting the interaction can be viewed as a local deformation of the plane.
It is known that the essential spectrum of this Schrödinger operator
coincides with $[-\frac14\alpha^2,+\infty)$. We prove that for all sufficiently
small $\beta > 0$ its discrete spectrum is non-empty and consists of a unique
simple eigenvalue. Moreover, we obtain an asymptotic expansion of this
eigenvalue in the limit $\beta \rightarrow 0+$. In particular, this eigenvalue
tends to $-\frac14\alpha^2$ exponentially fast as $\beta\rightarrow 0+$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Análise comparativa de pesquisas de origens e destinos: uma abordagem baseada em Redes Complexas | In this paper, a comparative study was conducted between complex networks
representing origin and destination survey data. Similarities were found
between the characteristics of the networks of Brazilian cities with networks
of foreign cities. Power laws were found in the distributions of edge weights
and this scale - free behavior can occur due to the economic characteristics of
the cities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Inverse Kinematics for Control of Tensegrity Soft Robots: Existence and Optimality of Solutions | Tension-network (`tensegrity') robots encounter many control challenges as
articulated soft robots, due to the structures' high-dimensional nonlinear
dynamics. Control approaches have been developed which use the inverse
kinematics of tensegrity structures, either for open-loop control or as
equilibrium inputs for closed-loop controllers. However, current formulations
of the tensegrity inverse kinematics problem are limited in robotics
applications: first, they can lead to higher than needed cable tensions, and
second, may lack solutions when applied to robots with high node-to-cable
ratios. This work provides progress in both directions. To address the first
limitation, the objective function for the inverse kinematics optimization
problem is modified to produce cable tensions as low or lower than before, thus
reducing the load on the robots' motors. For the second, a reformulation of the
static equilibrium constraint is proposed, which produces solutions independent
of the number of nodes within each rigid body. Simulation results using the
second reformulation on a specific tensegrity spine robot show reasonable
open-loop control results, whereas the previous formulation could not produce
any solution.
| 1 | 0 | 0 | 0 | 0 | 0 |
Smallest eigenvalue density for regular or fixed-trace complex Wishart-Laguerre ensemble and entanglement in coupled kicked tops | The statistical behaviour of the smallest eigenvalue has important
implications for systems which can be modeled using a Wishart-Laguerre
ensemble, the regular one or the fixed trace one. For example, the density of
the smallest eigenvalue of the Wishart-Laguerre ensemble plays a crucial role
in characterizing multiple channel telecommunication systems. Similarly, in the
quantum entanglement problem, the smallest eigenvalue of the fixed trace
ensemble carries information regarding the nature of entanglement.
For real Wishart-Laguerre matrices, there exists an elegant recurrence scheme
suggested by Edelman to directly obtain the exact expression for the smallest
eigenvalue density. In the case of complex Wishart-Laguerre matrices, for
finding exact and explicit expressions for the smallest eigenvalue density,
existing results based on determinants become impractical when the determinants
involve large-size matrices. In this work, we derive a recurrence scheme for
the complex case which is analogous to that of Edelman's for the real case.
This is used to obtain exact results for the smallest eigenvalue density for
both the regular, and the fixed trace complex Wishart-Laguerre ensembles. We
validate our analytical results using Monte Carlo simulations. We also study
scaled Wishart-Laguerre ensemble and investigate its efficacy in approximating
the fixed-trace ensemble. Eventually, we apply our result for the fixed-trace
ensemble to investigate the behaviour of the smallest eigenvalue in the
paradigmatic system of coupled kicked tops.
| 0 | 1 | 1 | 1 | 0 | 0 |
Development of probabilistic dam breach model using Bayesian inference | Dam breach models are commonly used to predict outflow hydrographs of
potentially failing dams and are key ingredients for evaluating flood risk. In
this paper a new dam breach modeling framework is introduced that shall improve
the reliability of hydrograph predictions of homogeneous earthen embankment
dams. Striving for a small number of parameters, the simplified physics-based
model describes the processes of failing embankment dams by breach enlargement,
driven by progressive surface erosion. Therein the erosion rate of dam material
is modeled by empirical sediment transport formulations. Embedding the model
into a Bayesian multilevel framework allows for quantitative analysis of
different categories of uncertainties. To this end, data available in
literature of observed peak discharge and final breach width of historical dam
failures was used to perform model inversion by applying Markov Chain Monte
Carlo simulation. Prior knowledge is mainly based on non-informative
distribution functions. The resulting posterior distribution shows that the
main source of uncertainty is a correlated subset of parameters, consisting of
the residual error term and the epistemic term quantifying the breach erosion
rate. The prediction intervals of peak discharge and final breach width are
congruent with values known from literature. To finally predict the outflow
hydrograph for real case applications, an alternative residual model was
formulated that assumes perfect data and a perfect model. The fully
probabilistic fashion of hydrograph prediction has the potential to improve the
adequate risk management of downstream flooding.
| 0 | 0 | 0 | 1 | 0 | 0 |
Large Magellanic Cloud Near-Infrared Synoptic Survey. V. Period-Luminosity Relations of Miras | We study the near-infrared properties of 690 Mira candidates in the central
region of the Large Magellanic Cloud, based on time-series observations at
JHKs. We use densely-sampled I-band observations from the OGLE project to
generate template light curves in the near infrared and derive robust mean
magnitudes at those wavelengths. We obtain near-infrared Period-Luminosity
relations for Oxygen-rich Miras with a scatter as low as 0.12 mag at Ks. We
study the Period-Luminosity-Color relations and the color excesses of
Carbon-rich Miras, which show evidence for a substantially different reddening
law.
| 0 | 0 | 0 | 1 | 0 | 0 |
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network | There is an inherent need for autonomous cars, drones, and other robots to
have a notion of how their environment behaves and to anticipate changes in the
near future. In this work, we focus on anticipating future appearance given the
current frame of a video. Existing work focuses on either predicting the future
appearance as the next frame of a video, or predicting future motion as optical
flow or motion trajectories starting from a single video frame. This work
stretches the ability of CNNs (Convolutional Neural Networks) to predict an
anticipation of appearance at an arbitrarily given future time, not necessarily
the next video frame. We condition our predicted future appearance on a
continuous time variable that allows us to anticipate future frames at a given
temporal distance, directly from the input video frame. We show that CNNs can
learn an intrinsic representation of typical appearance changes over time and
successfully generate realistic predictions at a deliberate time difference in
the near future.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access | We consider the problem of dynamic spectrum access for network utility
maximization in multichannel wireless networks. The shared bandwidth is divided
into K orthogonal channels. In the beginning of each time slot, each user
selects a channel and transmits a packet with a certain transmission
probability. After each time slot, each user that has transmitted a packet
receives a local observation indicating whether its packet was successfully
delivered or not (i.e., ACK signal). The objective is a multi-user strategy for
accessing the spectrum that maximizes a certain network utility in a
distributed manner without online coordination or message exchanges between
users. Obtaining an optimal solution for the spectrum access problem is
computationally expensive in general due to the large state space and partial
observability of the states. To tackle this problem, we develop a novel
distributed dynamic spectrum access algorithm based on deep multi-user
reinforcement leaning. Specifically, at each time slot, each user maps its
current state to spectrum access actions based on a trained deep-Q network used
to maximize the objective function. Game theoretic analysis of the system
dynamics is developed for establishing design principles for the implementation
of the algorithm. Experimental results demonstrate strong performance of the
algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming | We design a new myopic strategy for a wide class of sequential design of
experiment (DOE) problems, where the goal is to collect data in order to to
fulfil a certain problem specific goal. Our approach, Myopic Posterior Sampling
(MPS), is inspired by the classical posterior (Thompson) sampling algorithm for
multi-armed bandits and leverages the flexibility of probabilistic programming
and approximate Bayesian inference to address a broad set of problems.
Empirically, this general-purpose strategy is competitive with more specialised
methods in a wide array of DOE tasks, and more importantly, enables addressing
complex DOE goals where no existing method seems applicable. On the theoretical
side, we leverage ideas from adaptive submodularity and reinforcement learning
to derive conditions under which MPS achieves sublinear regret against natural
benchmark policies.
| 0 | 0 | 0 | 1 | 0 | 0 |
Data-Driven Sparse Structure Selection for Deep Neural Networks | Deep convolutional neural networks have liberated its extraordinary power on
various tasks. However, it is still very challenging to deploy state-of-the-art
models into real-world applications due to their high computational complexity.
How can we design a compact and effective network without massive experiments
and expert knowledge? In this paper, we propose a simple and effective
framework to learn and prune deep models in an end-to-end manner. In our
framework, a new type of parameter -- scaling factor is first introduced to
scale the outputs of specific structures, such as neurons, groups or residual
blocks. Then we add sparsity regularizations on these factors, and solve this
optimization problem by a modified stochastic Accelerated Proximal Gradient
(APG) method. By forcing some of the factors to zero, we can safely remove the
corresponding structures, thus prune the unimportant parts of a CNN. Comparing
with other structure selection methods that may need thousands of trials or
iterative fine-tuning, our method is trained fully end-to-end in one training
pass without bells and whistles. We evaluate our method, Sparse Structure
Selection with several state-of-the-art CNNs, and demonstrate very promising
results with adaptive depth and width selection.
| 1 | 0 | 0 | 0 | 0 | 0 |
Scaling laws and bounds for the turbulent G.O. Roberts dynamo | Numerical simulations of the G.O. Roberts dynamo are presented. Dynamos both
with and without a significant mean field are obtained. Exact bounds are
derived for the total energy which conform with the Kolmogorov phenomenology of
turbulence. Best fits to numerical data show the same functional dependences as
the inequalities obtained from optimum theory.
| 0 | 1 | 0 | 0 | 0 | 0 |
Control Strategies for the Fokker-Planck Equation | Using a projection-based decoupling of the Fokker-Planck equation, control
strategies that allow to speed up the convergence to the stationary
distribution are investigated. By means of an operator theoretic framework for
a bilinear control system, two different feedback control laws are proposed.
Projected Riccati and Lyapunov equations are derived and properties of the
associated solutions are given. The well-posedness of the closed loop systems
is shown and local and global stabilization results, respectively, are
obtained. An essential tool in the construction of the controls is the choice
of appropriate control shape functions. Results for a two dimensional double
well potential illustrate the theoretical findings in a numerical setup.
| 0 | 0 | 1 | 0 | 0 | 0 |
On Popov's formula involving the Von Mangoldt function | We offer a generalization of a formula of Popov involving the Von Mangoldt
function. Some commentary on its relation to other results in analytic number
theory is mentioned as well as an analogue involving the m$\ddot{o}$bius
function.
| 0 | 0 | 1 | 0 | 0 | 0 |
On fibering compact manifold over the circle | In this paper, we show that any compact manifold that carries a
SL(n;R)-foliation is fibered on the circle S^1.
| 0 | 0 | 1 | 0 | 0 | 0 |
Phonon-Induced Topological Transition to a Type-II Weyl Semimetal | Given the importance of crystal symmetry for the emergence of topological
quantum states, we have studied, as exemplified in NbNiTe2, the interplay of
crystal symmetry, atomic displacements (lattice vibration), band degeneracy,
and band topology. For NbNiTe2 structure in space group 53 (Pmna) - having an
inversion center arising from two glide planes and one mirror plane with a
2-fold rotation and screw axis - a full gap opening exists between two band
manifolds near the Fermi energy. Upon atomic displacements by optical phonons,
the symmetry lowers to space group 28 (Pma2), eliminating one glide plane along
c, the associated rotation and screw axis, and the inversion center. As a
result, twenty Weyl points emerge, including four type-II Weyl points in the
G-X direction at the boundary between a pair of adjacent electron and hole
bands. Thus, optical phonons may offer control of the transition to a Weyl
fermion state.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multi-hop assortativities for networks classification | Several social, medical, engineering and biological challenges rely on
discovering the functionality of networks from their structure and node
metadata, when it is available. For example, in chemoinformatics one might want
to detect whether a molecule is toxic based on structure and atomic types, or
discover the research field of a scientific collaboration network. Existing
techniques rely on counting or measuring structural patterns that are known to
show large variations from network to network, such as the number of triangles,
or the assortativity of node metadata. We introduce the concept of multi-hop
assortativity, that captures the similarity of the nodes situated at the
extremities of a randomly selected path of a given length. We show that
multi-hop assortativity unifies various existing concepts and offers a
versatile family of 'fingerprints' to characterize networks. These fingerprints
allow in turn to recover the functionalities of a network, with the help of the
machine learning toolbox. Our method is evaluated empirically on established
social and chemoinformatic network benchmarks. Results reveal that our
assortativity based features are competitive providing highly accurate results
often outperforming state of the art methods for the network classification
task.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Bayesian Nonparametrics based Robust Particle Filter Algorithm | This paper is concerned with the online estimation of a nonlinear dynamic
system from a series of noisy measurements. The focus is on cases wherein
outliers are present in-between normal noises. We assume that the outliers
follow an unknown generating mechanism which deviates from that of normal
noises, and then model the outliers using a Bayesian nonparametric model called
Dirichlet process mixture (DPM). A sequential particle-based algorithm is
derived for posterior inference for the outlier model as well as the state of
the system to be estimated. The resulting algorithm is termed DPM based robust
PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to
"speak for itself" to determine the complexity and structure of the outlier
model. Simulation results show that it performs remarkably better than two
state-of-the-art methods especially when outliers appear frequently along time.
| 0 | 0 | 0 | 1 | 0 | 0 |
Highly accurate model for prediction of lung nodule malignancy with CT scans | Computed tomography (CT) examinations are commonly used to predict lung
nodule malignancy in patients, which are shown to improve noninvasive early
diagnosis of lung cancer. It remains challenging for computational approaches
to achieve performance comparable to experienced radiologists. Here we present
NoduleX, a systematic approach to predict lung nodule malignancy from CT data,
based on deep learning convolutional neural networks (CNN). For training and
validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort.
All nodules were identified and classified by four experienced thoracic
radiologists who participated in the LIDC project. NoduleX achieves high
accuracy for nodule malignancy classification, with an AUC of ~0.99. This is
commensurate with the analysis of the dataset by experienced radiologists. Our
approach, NoduleX, provides an effective framework for highly accurate nodule
malignancy prediction with the model trained on a large patient population. Our
results are replicable with software available at
this http URL.
| 0 | 0 | 0 | 1 | 1 | 0 |
Rotating Rayleigh-Taylor turbulence | The turbulent Rayleigh--Taylor system in a rotating reference frame is
investigated by direct numerical simulations within the Oberbeck-Boussinesq
approximation. On the basis of theoretical arguments, supported by our
simulations, we show that the Rossby number decreases in time, and therefore
the Coriolis force becomes more important as the system evolves and produces
many effects on Rayleigh--Taylor turbulence. We find that rotation reduces the
intensity of turbulent velocity fluctuations and therefore the growth rate of
the temperature mixing layer. Moreover, in presence of rotation the conversion
of potential energy into turbulent kinetic energy is found to be less effective
and the efficiency of the heat transfer is reduced. Finally, during the
evolution of the mixing layer we observe the development of a
cyclone-anticyclone asymmetry.
| 0 | 1 | 0 | 0 | 0 | 0 |
Evolutionary dynamics of N-person Hawk-Dove games | In the animal world, the competition between individuals belonging to
different species for a resource often requires the cooperation of several
individuals in groups. This paper proposes a generalization of the Hawk-Dove
Game for an arbitrary number of agents: the N-person Hawk-Dove Game. In this
model, doves exemplify the cooperative behavior without intraspecies conflict,
while hawks represent the aggressive behavior. In the absence of hawks, doves
share the resource equally and avoid conflict, but having hawks around lead to
doves escaping without fighting. Conversely, hawks fight for the resource at
the cost of getting injured. Nevertheless, if doves are present in sufficient
number to expel the hawks, they can aggregate to protect the resource, and thus
avoid being plundered by hawks. We derive and numerically solve an exact
equation for the evolution of the system in both finite and infinite well-mixed
populations, finding the conditions for stable coexistence between both
species. Furthermore, by varying the different parameters, we found a scenario
of bifurcations that leads the system from dominating hawks and coexistence to
bi-stability, multiple interior equilibria and dominating doves.
| 0 | 1 | 0 | 0 | 0 | 0 |
A global model for predicting the arrival of imported dengue infections | With approximately half of the world's population at risk of contracting
dengue, this mosquito-borne disease is of global concern. International
travellers significantly contribute to dengue's rapid and large-scale spread by
importing the disease from endemic into non-endemic countries. To prevent
future outbreaks and dengue from establishing in non-endemic countries,
knowledge about the arrival time and location of infected travellers is
crucial. We propose a network model that predicts the monthly number of dengue
infected air passengers arriving at any given airport. We consider
international air travel volumes, monthly dengue incidence rates and temporal
infection dynamics. Our findings shed light onto dengue importation routes and
reveal country-specific reporting rates that have been until now largely
unknown.
| 1 | 0 | 0 | 0 | 1 | 0 |
Contextually Customized Video Summaries via Natural Language | The best summary of a long video differs among different people due to its
highly subjective nature. Even for the same person, the best summary may change
with time or mood. In this paper, we introduce the task of generating
customized video summaries through simple text. First, we train a deep
architecture to effectively learn semantic embeddings of video frames by
leveraging the abundance of image-caption data via a progressive and residual
manner. Given a user-specific text description, our algorithm is able to select
semantically relevant video segments and produce a temporally aligned video
summary. In order to evaluate our textually customized video summaries, we
conduct experimental comparison with baseline methods that utilize ground-truth
information. Despite the challenging baselines, our method still manages to
show comparable or even exceeding performance. We also show that our method is
able to generate semantically diverse video summaries by only utilizing the
learned visual embeddings.
| 1 | 0 | 0 | 0 | 0 | 0 |
Observation of surface plasmon polaritons in 2D electron gas of surface electron accumulation in InN nanostructures | Recently, heavily doped semiconductors are emerging as an alternate for low
loss plasmonic materials. InN, belonging to the group III nitrides, possesses
the unique property of surface electron accumulation (SEA) which provides two
dimensional electron gas (2DEG) system. In this report, we demonstrated the
surface plasmon properties of InN nanoparticles originating from SEA using the
real space mapping of the surface plasmon fields for the first time. The SEA is
confirmed by Raman studies which are further corroborated by photoluminescence
and photoemission spectroscopic studies. The frequency of 2DEG corresponding to
SEA is found to be in the THz region. The periodic fringes are observed in the
near-field scanning optical microscopic images of InN nanostructures. The
observed fringes are attributed to the interference of propagated and back
reflected surface plasmon polaritons (SPPs). The observation of SPPs is solely
attributed to the 2DEG corresponding to the SEA of InN. In addition, resonance
kind of behavior with the enhancement of the near-field intensity is observed
in the near-field images of InN nanostructures. Observation of SPPs indicates
that InN with SEA can be a promising THz plasmonic material for the light
confinement.
| 0 | 1 | 0 | 0 | 0 | 0 |
Asymmetric Mach-Zehnder atom interferometers | It is shown that using beam splitters with non-equal wave vectors results in
a new recoil diagram which is qualitatively different from the well-known
diagram associated with the Mach-Zehnder atom interferometer. We predict a new
asymmetric Mach-Zehnder atom interferometer (AMZAI) and study it when one uses
a Raman beam splitter. The main feature is that the phase of AMZAI contains a
quantum part proportional to the recoil frequency. A response sensitive only to
the quantum phase was found. A new technique to measure the recoil frequency
and fine structure constant is proposed and studied outside of the Raman-Nath
approximation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Partial Information Stochastic Differential Games for Backward Stochastic Systems Driven By Lévy Processes | In this paper, we consider a partial information two-person zero-sum
stochastic differential game problem where the system is governed by a backward
stochastic differential equation driven by Teugels martingales associated with
a Lévy process and an independent Brownian motion. One sufficient (a
verification theorem) and one necessary conditions for the existence of optimal
controls are proved. To illustrate the general results, a linear quadratic
stochastic differential game problem is discussed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Inter-Session Modeling for Session-Based Recommendation | In recent years, research has been done on applying Recurrent Neural Networks
(RNNs) as recommender systems. Results have been promising, especially in the
session-based setting where RNNs have been shown to outperform state-of-the-art
models. In many of these experiments, the RNN could potentially improve the
recommendations by utilizing information about the user's past sessions, in
addition to its own interactions in the current session. A problem for
session-based recommendation, is how to produce accurate recommendations at the
start of a session, before the system has learned much about the user's current
interests. We propose a novel approach that extends a RNN recommender to be
able to process the user's recent sessions, in order to improve
recommendations. This is done by using a second RNN to learn from recent
sessions, and predict the user's interest in the current session. By feeding
this information to the original RNN, it is able to improve its
recommendations. Our experiments on two different datasets show that the
proposed approach can significantly improve recommendations throughout the
sessions, compared to a single RNN working only on the current session. The
proposed model especially improves recommendations at the start of sessions,
and is therefore able to deal with the cold start problem within sessions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multiscale Modeling of Shock Wave Localization in Porous Energetic Material | Shock wave interactions with defects, such as pores, are known to play a key
role in the chemical initiation of energetic materials. The shock response of
hexanitrostilbene is studied through a combination of large scale reactive
molecular dynamics and mesoscale hydrodynamic simulations. In order to extend
our simulation capability at the mesoscale to include weak shock conditions (<
6 GPa), atomistic simulations of pore collapse are used to define a strain rate
dependent strength model. Comparing these simulation methods allows us to
impose physically-reasonable constraints on the mesoscale model parameters. In
doing so, we have been able to study shock waves interacting with pores as a
function of this viscoplastic material response. We find that the pore collapse
behavior of weak shocks is characteristically different to that of strong
shocks.
| 0 | 1 | 0 | 0 | 0 | 0 |
Cryptoasset Factor Models | We propose factor models for the cross-section of daily cryptoasset returns
and provide source code for data downloads, computing risk factors and
backtesting them out-of-sample. In "cryptoassets" we include all
cryptocurrencies and a host of various other digital assets (coins and tokens)
for which exchange market data is available. Based on our empirical analysis,
we identify the leading factor that appears to strongly contribute into daily
cryptoasset returns. Our results suggest that cross-sectional statistical
arbitrage trading may be possible for cryptoassets subject to efficient
executions and shorting.
| 0 | 0 | 0 | 0 | 0 | 1 |
Multi-dimensional Graph Fourier Transform | Many signals on Cartesian product graphs appear in the real world, such as
digital images, sensor observation time series, and movie ratings on Netflix.
These signals are "multi-dimensional" and have directional characteristics
along each factor graph. However, the existing graph Fourier transform does not
distinguish these directions, and assigns 1-D spectra to signals on product
graphs. Further, these spectra are often multi-valued at some frequencies. Our
main result is a multi-dimensional graph Fourier transform that solves such
problems associated with the conventional GFT. Using algebraic properties of
Cartesian products, the proposed transform rearranges 1-D spectra obtained by
the conventional GFT into the multi-dimensional frequency domain, of which each
dimension represents a directional frequency along each factor graph. Thus, the
multi-dimensional graph Fourier transform enables directional frequency
analysis, in addition to frequency analysis with the conventional GFT.
Moreover, this rearrangement resolves the multi-valuedness of spectra in some
cases. The multi-dimensional graph Fourier transform is a foundation of novel
filterings and stationarities that utilize dimensional information of graph
signals, which are also discussed in this study. The proposed methods are
applicable to a wide variety of data that can be regarded as signals on
Cartesian product graphs. This study also notes that multivariate graph signals
can be regarded as 2-D univariate graph signals. This correspondence provides
natural definitions of the multivariate graph Fourier transform and the
multivariate stationarity based on their 2-D univariate versions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Criteria for the Application of Double Exponential Transformation | The double exponential formula was introduced for calculating definite
integrals with singular point oscillation functions and Fourier-integrals. The
double exponential transformation is not only useful for numerical computations
but it is also used in different methods of Sinc theory. In this paper we use
double exponential transformation for calculating particular improper
integrals. By improving integral estimates having singular final points. By
comparison between double exponential transformations and single exponential
transformations it is proved that the error margin of double exponential
transformations is smaller. Finally Fourier-integral and double exponential
transformations are discussed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Ultra-high strain in epitaxial silicon carbide nanostructures utilizing residual stress amplification | Strain engineering has attracted great attention, particularly for epitaxial
films grown on a different substrate. Residual strains of SiC have been widely
employed to form ultra-high frequency and high Q factor resonators. However, to
date the highest residual strain of SiC was reported to be limited to
approximately 0.6%. Large strains induced into SiC could lead to several
interesting physical phenomena, as well as significant improvement of resonant
frequencies. We report an unprecedented nano strain-amplifier structure with an
ultra-high residual strain up to 8% utilizing the natural residual stress
between epitaxial 3C SiC and Si. In addition, the applied strain can be tuned
by changing the dimensions of the amplifier structure. The possibility of
introducing such a controllable and ultra-high strain will open the door to
investigating the physics of SiC in large strain regimes, and the development
of ultra sensitive mechanical sensors.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Diverse Club: The Integrative Core of Complex Networks | A complex system can be represented and analyzed as a network, where nodes
represent the units of the network and edges represent connections between
those units. For example, a brain network represents neurons as nodes and axons
between neurons as edges. In many networks, some nodes have a
disproportionately high number of edges. These nodes also have many edges
between each other, and are referred to as the rich club. In many different
networks, the nodes of this club are assumed to support global network
integration. However, another set of nodes potentially exhibits a connectivity
structure that is more advantageous to global network integration. Here, in a
myriad of different biological and man-made networks, we discover the diverse
club--a set of nodes that have edges diversely distributed across the network.
The diverse club exhibits, to a greater extent than the rich club, properties
consistent with an integrative network function--these nodes are more highly
interconnected and their edges are more critical for efficient global
integration. Moreover, we present a generative evolutionary network model that
produces networks with a diverse club but not a rich club, thus demonstrating
that these two clubs potentially evolved via distinct selection pressures.
Given the variety of different networks that we analyzed--the c. elegans, the
macaque brain, the human brain, the United States power grid, and global air
traffic--the diverse club appears to be ubiquitous in complex networks. These
results warrant the distinction and analysis of two critical clubs of nodes in
all complex systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bayesian Semisupervised Learning with Deep Generative Models | Neural network based generative models with discriminative components are a
powerful approach for semi-supervised learning. However, these techniques a)
cannot account for model uncertainty in the estimation of the model's
discriminative component and b) lack flexibility to capture complex stochastic
patterns in the label generation process. To avoid these problems, we first
propose to use a discriminative component with stochastic inputs for increased
noise flexibility. We show how an efficient Gibbs sampling procedure can
marginalize the stochastic inputs when inferring missing labels in this model.
Following this, we extend the discriminative component to be fully Bayesian and
produce estimates of uncertainty in its parameter values. This opens the door
for semi-supervised Bayesian active learning.
| 0 | 0 | 0 | 1 | 0 | 0 |
Robust Detection of Covariate-Treatment Interactions in Clinical Trials | Detection of interactions between treatment effects and patient descriptors
in clinical trials is critical for optimizing the drug development process. The
increasing volume of data accumulated in clinical trials provides a unique
opportunity to discover new biomarkers and further the goal of personalized
medicine, but it also requires innovative robust biomarker detection methods
capable of detecting non-linear, and sometimes weak, signals. We propose a set
of novel univariate statistical tests, based on the theory of random walks,
which are able to capture non-linear and non-monotonic covariate-treatment
interactions. We also propose a novel combined test, which leverages the power
of all of our proposed univariate tests into a single general-case tool. We
present results for both synthetic trials as well as real-world clinical
trials, where we compare our method with state-of-the-art techniques and
demonstrate the utility and robustness of our approach.
| 0 | 0 | 0 | 1 | 0 | 0 |
The Future of RICH Detectors through the Light of the LHCb RICH | The limitations in performance of the present RICH system in the LHCb
experiment are given by the natural chromatic dispersion of the gaseous
Cherenkov radiator, the aberrations of the optical system and the pixel size of
the photon detectors. Moreover, the overall PID performance can be affected by
high detector occupancy as the pattern recognition becomes more difficult with
high particle multiplicities. This paper shows a way to improve performance by
systematically addressing each of the previously mentioned limitations. These
ideas are applied in the present and future upgrade phases of the LHCb
experiment. Although applied to specific circumstances, they are used as a
paradigm on what is achievable in the development and realisation of high
precision RICH detectors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stability of Valuations: Higher Rational Rank | Given a klt singularity $x\in (X, D)$, we show that a quasi-monomial
valuation $v$ with a finitely generated associated graded ring is the minimizer
of the normalized volume function $\widehat{\rm vol}_{(X,D),x}$, if and only if
$v$ induces a degeneration to a K-semistable log Fano cone singularity.
Moreover, such a minimizer is unique among all quasi-monomial valuations up to
rescaling. As a consequence, we prove that for a klt singularity $x\in X$ on
the Gromov-Hausdorff limit of Kähler-Einstein Fano manifolds, the
intermediate K-semistable cone associated to its metric tangent cone is
uniquely determined by the algebraic structure of $x\in X$, hence confirming a
conjecture by Donaldson-Sun.
| 0 | 0 | 1 | 0 | 0 | 0 |
Higgs mode and its decay in a two dimensional antiferromagnet | Condensed-matter analogs of the Higgs boson in particle physics allow
insights into its behavior in different symmetries and dimensionalities.
Evidence for the Higgs mode has been reported in a number of different
settings, including ultracold atomic gases, disordered superconductors, and
dimerized quantum magnets. However, decay processes of the Higgs mode (which
are eminently important in particle physics) have not yet been studied in
condensed matter due to the lack of a suitable material system coupled to a
direct experimental probe. A quantitative understanding of these processes is
particularly important for low-dimensional systems where the Higgs mode decays
rapidly and has remained elusive to most experimental probes. Here, we discover
and study the Higgs mode in a two-dimensional antiferromagnet using
spin-polarized inelastic neutron scattering. Our spin-wave spectra of
Ca$_2$RuO$_4$ directly reveal a well-defined, dispersive Higgs mode, which
quickly decays into transverse Goldstone modes at the antiferromagnetic
ordering wavevector. Through a complete mapping of the transverse modes in the
reciprocal space, we uniquely specify the minimal model Hamiltonian and
describe the decay process. We thus establish a novel condensed matter platform
for research on the dynamics of the Higgs mode.
| 0 | 1 | 0 | 0 | 0 | 0 |
Robust and Efficient Boosting Method using the Conditional Risk | Well-known for its simplicity and effectiveness in classification, AdaBoost,
however, suffers from overfitting when class-conditional distributions have
significant overlap. Moreover, it is very sensitive to noise that appears in
the labels. This article tackles the above limitations simultaneously via
optimizing a modified loss function (i.e., the conditional risk). The proposed
approach has the following two advantages. (1) It is able to directly take into
account label uncertainty with an associated label confidence. (2) It
introduces a "trustworthiness" measure on training samples via the Bayesian
risk rule, and hence the resulting classifier tends to have finite sample
performance that is superior to that of the original AdaBoost when there is a
large overlap between class conditional distributions. Theoretical properties
of the proposed method are investigated. Extensive experimental results using
synthetic data and real-world data sets from UCI machine learning repository
are provided. The empirical study shows the high competitiveness of the
proposed method in predication accuracy and robustness when compared with the
original AdaBoost and several existing robust AdaBoost algorithms.
| 0 | 0 | 0 | 1 | 0 | 0 |
High Dimensional Robust Estimation of Sparse Models via Trimmed Hard Thresholding | We study the problem of sparsity constrained $M$-estimation with arbitrary
corruptions to both {\em explanatory and response} variables in the
high-dimensional regime, where the number of variables $d$ is larger than the
sample size $n$. Our main contribution is a highly efficient gradient-based
optimization algorithm that we call Trimmed Hard Thresholding -- a robust
variant of Iterative Hard Thresholding (IHT) by using trimmed mean in gradient
computations. Our algorithm can deal with a wide class of sparsity constrained
$M$-estimation problems, and we can tolerate a nearly dimension independent
fraction of arbitrarily corrupted samples. More specifically, when the
corrupted fraction satisfies $\epsilon \lesssim {1} /\left({\sqrt{k} \log
(nd)}\right)$, where $k$ is the sparsity of the parameter, we obtain accurate
estimation and model selection guarantees with optimal sample complexity.
Furthermore, we extend our algorithm to sparse Gaussian graphical model
(precision matrix) estimation via a neighborhood selection approach. We
demonstrate the effectiveness of robust estimation in sparse linear, logistic
regression, and sparse precision matrix estimation on synthetic and real-world
US equities data.
| 1 | 0 | 1 | 1 | 0 | 0 |
Stop talking to me -- a communication-avoiding ADER-DG realisation | We present a communication- and data-sensitive formulation of ADER-DG for
hyperbolic differential equation systems. Sensitive here has multiple flavours:
First, the formulation reduces the persistent memory footprint. This reduces
pressure on the memory subsystem. Second, the formulation realises the
underlying predictor-corrector scheme with single-touch semantics, i.e., each
degree of freedom is read on average only once per time step from the main
memory. This reduces communication through the memory controllers. Third, the
formulation breaks up the tight coupling of the explicit time stepping's
algorithmic steps to mesh traversals. This averages out data access peaks.
Different operations and algorithmic steps are ran on different grid entities.
Finally, the formulation hides distributed memory data transfer behind the
computation aligned with the mesh traversal. This reduces pressure on the
machine interconnects. All techniques applied by our formulation are elaborated
by means of a rigorous task formalism. They break up ADER-DG's tight causal
coupling of compute steps and can be generalised to other predictor-corrector
schemes.
| 1 | 0 | 0 | 0 | 0 | 0 |
The effect of prior probabilities on quantification and propagation of imprecise probabilities resulting from small datasets | This paper outlines a methodology for Bayesian multimodel uncertainty
quantification (UQ) and propagation and presents an investigation into the
effect of prior probabilities on the resulting uncertainties. The UQ
methodology is adapted from the information-theoretic method previously
presented by the authors (Zhang and Shields, 2018) to a fully Bayesian
construction that enables greater flexibility in quantifying uncertainty in
probability model form. Being Bayesian in nature and rooted in UQ from small
datasets, prior probabilities in both probability model form and model
parameters are shown to have a significant impact on quantified uncertainties
and, consequently, on the uncertainties propagated through a physics-based
model. These effects are specifically investigated for a simplified plate
buckling problem with uncertainties in material properties derived from a small
number of experiments using noninformative priors and priors derived from past
studies of varying appropriateness. It is illustrated that prior probabilities
can have a significant impact on multimodel UQ for small datasets and
inappropriate (but seemingly reasonable) priors may even have lingering effects
that bias probabilities even for large datasets. When applied to uncertainty
propagation, this may result in probability bounds on response quantities that
do not include the true probabilities.
| 0 | 0 | 0 | 1 | 0 | 0 |
Hierarchical loss for classification | Failing to distinguish between a sheepdog and a skyscraper should be worse
and penalized more than failing to distinguish between a sheepdog and a poodle;
after all, sheepdogs and poodles are both breeds of dogs. However, existing
metrics of failure (so-called "loss" or "win") used in textual or visual
classification/recognition via neural networks seldom view a sheepdog as more
similar to a poodle than to a skyscraper. We define a metric that, inter alia,
can penalize failure to distinguish between a sheepdog and a skyscraper more
than failure to distinguish between a sheepdog and a poodle. Unlike previously
employed possibilities, this metric is based on an ultrametric tree associated
with any given tree organization into a semantically meaningful hierarchy of a
classifier's classes.
| 1 | 0 | 0 | 1 | 0 | 0 |
An efficient data structure for counting all linear extensions of a poset, calculating its jump number, and the likes | Achieving the goals in the title (and others) relies on a cardinality-wise
scanning of the ideals of the poset. Specifically, the relevant numbers
attached to the k+1 element ideals are inferred from the corresponding numbers
of the k-element (order) ideals. Crucial in all of this is a compressed
representation (using wildcards) of the ideal lattice. The whole scheme invites
distributed computation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Perception-in-the-Loop Adversarial Examples | We present a scalable, black box, perception-in-the-loop technique to find
adversarial examples for deep neural network classifiers. Black box means that
our procedure only has input-output access to the classifier, and not to the
internal structure, parameters, or intermediate confidence values.
Perception-in-the-loop means that the notion of proximity between inputs can be
directly queried from human participants rather than an arbitrarily chosen
metric. Our technique is based on covariance matrix adaptation evolution
strategy (CMA-ES), a black box optimization approach. CMA-ES explores the
search space iteratively in a black box manner, by generating populations of
candidates according to a distribution, choosing the best candidates according
to a cost function, and updating the posterior distribution to favor the best
candidates. We run CMA-ES using human participants to provide the fitness
function, using the insight that the choice of best candidates in CMA-ES can be
naturally modeled as a perception task: pick the top $k$ inputs perceptually
closest to a fixed input. We empirically demonstrate that finding adversarial
examples is feasible using small populations and few iterations. We compare the
performance of CMA-ES on the MNIST benchmark with other black-box approaches
using $L_p$ norms as a cost function, and show that it performs favorably both
in terms of success in finding adversarial examples and in minimizing the
distance between the original and the adversarial input. In experiments on the
MNIST, CIFAR10, and GTSRB benchmarks, we demonstrate that CMA-ES can find
perceptually similar adversarial inputs with a small number of iterations and
small population sizes when using perception-in-the-loop. Finally, we show that
networks trained specifically to be robust against $L_\infty$ norm can still be
susceptible to perceptually similar adversarial examples.
| 1 | 0 | 0 | 1 | 0 | 0 |
Deep Fluids: A Generative Network for Parameterized Fluid Simulations | This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than traditional CPU solvers, while achieving
compression rates of over 1300x.
| 0 | 0 | 0 | 1 | 0 | 0 |
Bias Reduction in Instrumental Variable Estimation through First-Stage Shrinkage | The two-stage least-squares (2SLS) estimator is known to be biased when its
first-stage fit is poor. I show that better first-stage prediction can
alleviate this bias. In a two-stage linear regression model with Normal noise,
I consider shrinkage in the estimation of the first-stage instrumental variable
coefficients. For at least four instrumental variables and a single endogenous
regressor, I establish that the standard 2SLS estimator is dominated with
respect to bias. The dominating IV estimator applies James-Stein type shrinkage
in a first-stage high-dimensional Normal-means problem followed by a
control-function approach in the second stage. It preserves invariances of the
structural instrumental variable equations.
| 0 | 0 | 1 | 1 | 0 | 0 |
An Unsupervised Learning Classifier with Competitive Error Performance | An unsupervised learning classification model is described. It achieves
classification error probability competitive with that of popular supervised
learning classifiers such as SVM or kNN. The model is based on the incremental
execution of small step shift and rotation operations upon selected
discriminative hyperplanes at the arrival of input samples. When applied, in
conjunction with a selected feature extractor, to a subset of the ImageNet
dataset benchmark, it yields 6.2 % Top 3 probability of error; this exceeds by
merely about 2 % the result achieved by (supervised) k-Nearest Neighbor, both
using same feature extractor. This result may also be contrasted with popular
unsupervised learning schemes such as k-Means which is shown to be practically
useless on same dataset.
| 0 | 0 | 0 | 1 | 0 | 0 |
Exploring the predictability of range-based volatility estimators using RNNs | We investigate the predictability of several range-based stock volatility
estimators, and compare them to the standard close-to-close estimator which is
most commonly acknowledged as the volatility. The patterns of volatility
changes are analyzed using LSTM recurrent neural networks, which are a state of
the art method of sequence learning. We implement the analysis on all current
constituents of the Dow Jones Industrial Average index, and report averaged
evaluation results. We find that changes in the values of range-based
estimators are more predictable than that of the estimator using daily closing
values only.
| 0 | 0 | 0 | 1 | 0 | 1 |
Mean squared displacement and sinuosity of three-dimensional random search movements | Correlated random walks (CRW) have been used for a long time as a null model
for animal's random search movement in two dimensions (2D). An increasing
number of studies focus on animals' movement in three dimensions (3D), but the
key properties of CRW, such as the way the mean squared displacement is related
to the path length, are well known only in 1D and 2D. In this paper I derive
such properties for 3D CRW, in a consistent way with the expression of these
properties in 2D. This should allow 3D CRW to act as a null model when
analyzing actual 3D movements similarly to what is done in 2D
| 0 | 0 | 0 | 0 | 1 | 0 |
Context-Aware Pedestrian Motion Prediction In Urban Intersections | This paper presents a novel context-based approach for pedestrian motion
prediction in crowded, urban intersections, with the additional flexibility of
prediction in similar, but new, environments. Previously, Chen et. al. combined
Markovian-based and clustering-based approaches to learn motion primitives in a
grid-based world and subsequently predict pedestrian trajectories by modeling
the transition between learned primitives as a Gaussian Process (GP). This work
extends that prior approach by incorporating semantic features from the
environment (relative distance to curbside and status of pedestrian traffic
lights) in the GP formulation for more accurate predictions of pedestrian
trajectories over the same timescale. We evaluate the new approach on
real-world data collected using one of the vehicles in the MIT Mobility On
Demand fleet. The results show 12.5% improvement in prediction accuracy and a
2.65 times reduction in Area Under the Curve (AUC), which is used as a metric
to quantify the span of predicted set of trajectories, such that a lower AUC
corresponds to a higher level of confidence in the future direction of
pedestrian motion.
| 1 | 0 | 0 | 1 | 0 | 0 |
EnergyNet: Energy-based Adaptive Structural Learning of Artificial Neural Network Architectures | We present E NERGY N ET , a new framework for analyzing and building
artificial neural network architectures. Our approach adaptively learns the
structure of the networks in an unsupervised manner. The methodology is based
upon the theoretical guarantees of the energy function of restricted Boltzmann
machines (RBM) of infinite number of nodes. We present experimental results to
show that the final network adapts to the complexity of a given problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
Local Algorithms for Hierarchical Dense Subgraph Discovery | Finding the dense regions of a graph and relations among them is a
fundamental problem in network analysis. Core and truss decompositions reveal
dense subgraphs with hierarchical relations. The incremental nature of
algorithms for computing these decompositions and the need for global
information at each step of the algorithm hinders scalable parallelization and
approximations since the densest regions are not revealed until the end. In a
previous work, Lu et al. proposed to iteratively compute the $h$-indices of
neighbor vertex degrees to obtain the core numbers and prove that the
convergence is obtained after a finite number of iterations. This work
generalizes the iterative $h$-index computation for truss decomposition as well
as nucleus decomposition which leverages higher-order structures to generalize
core and truss decompositions. In addition, we prove convergence bounds on the
number of iterations. We present a framework of local algorithms to obtain the
core, truss, and nucleus decompositions. Our algorithms are local, parallel,
offer high scalability, and enable approximations to explore time and quality
trade-offs. Our shared-memory implementation verifies the efficiency,
scalability, and effectiveness of our local algorithms on real-world networks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Robust Gesture-Based Communication for Underwater Human-Robot Interaction in the context of Search and Rescue Diver Missions | We propose a robust gesture-based communication pipeline for divers to
instruct an Autonomous Underwater Vehicle (AUV) to assist them in performing
high-risk tasks and helping in case of emergency. A gesture communication
language (CADDIAN) is developed, based on consolidated and standardized diver
gestures, including an alphabet, syntax and semantics, ensuring a logical
consistency. A hierarchical classification approach is introduced for hand
gesture recognition based on stereo imagery and multi-descriptor aggregation to
specifically cope with underwater image artifacts, e.g. light backscatter or
color attenuation. Once the classification task is finished, a syntax check is
performed to filter out invalid command sequences sent by the diver or
generated by errors in the classifier. Throughout this process, the diver
receives constant feedback from an underwater tablet to acknowledge or abort
the mission at any time. The objective is to prevent the AUV from executing
unnecessary, infeasible or potentially harmful motions. Experimental results
under different environmental conditions in archaeological exploration and
bridge inspection applications show that the system performs well in the field.
| 1 | 0 | 0 | 0 | 0 | 0 |
High-$T_\textrm {C}$ superconductivity in Cs$_3$C$_{60}$ compounds governed by local Cs-C$_{60}$ Coulomb interactions | Unique among alkali-doped $\textit {A}$$_3$C$_{60}$ fullerene compounds, the
A15 and fcc forms of Cs$_3$C$_{60}$ exhibit superconducting states varying
under hydrostatic pressure with highest transition temperatures at $T_\textrm
{C}$$^\textrm {meas}$ = 38.3 and 35.2 K, respectively. Herein it is argued that
these two compounds under pressure represent the optimal materials of the
$\textit {A}$$_3$C$_{60}$ family, and that the C$_{60}$-associated
superconductivity is mediated through Coulombic interactions with charges on
the alkalis. A derivation of the interlayer Coulombic pairing model of
high-$T_\textrm {C}$ superconductivity employing non-planar geometry is
introduced, generalizing the picture of two interacting layers to an
interaction between charge reservoirs located on the C$_{60}$ and alkali ions.
The optimal transition temperature follows the algebraic expression, $T_\textrm
{C0}$ = (12.474 nm$^2$ K)/$\ell$${\zeta}$, where $\ell$ relates to the mean
spacing between interacting surface charges on the C$_{60}$ and ${\zeta}$ is
the average radial distance between the C$_{60}$ surface and the neighboring Cs
ions. Values of $T_\textrm {C0}$ for the measured cation stoichiometries of
Cs$_{3-\textrm{x}}$C$_{60}$ with x $\approx$ 0 are found to be 38.19 and 36.88
K for the A15 and fcc forms, respectively, with the dichotomy in transition
temperature reflecting the larger ${\zeta}$ and structural disorder in the fcc
form. In the A15 form, modeled interacting charges and Coulomb potential
e$^2$/${\zeta}$ are shown to agree quantitatively with findings from
nuclear-spin relaxation and mid-infrared optical conductivity. In the fcc form,
suppression of $T_\textrm {C}$$^\textrm {meas}$ below $T_\textrm {C0}$ is
ascribed to native structural disorder. Phononic effects in conjunction with
Coulombic pairing are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Analysis and mitigation of interface losses in trenched superconducting coplanar waveguide resonators | Improving the performance of superconducting qubits and resonators generally
results from a combination of materials and fabrication process improvements
and design modifications that reduce device sensitivity to residual losses. One
instance of this approach is to use trenching into the device substrate in
combination with superconductors and dielectrics with low intrinsic losses to
improve quality factors and coherence times. Here we demonstrate titanium
nitride coplanar waveguide resonators with mean quality factors exceeding two
million and controlled trenching reaching 2.2 $\mu$m into the silicon
substrate. Additionally, we measure sets of resonators with a range of sizes
and trench depths and compare these results with finite-element simulations to
demonstrate quantitative agreement with a model of interface dielectric loss.
We then apply this analysis to determine the extent to which trenching can
improve resonator performance.
| 0 | 1 | 0 | 0 | 0 | 0 |
Recent Operation of the FNAL Magnetron $H^{-}$ Ion Source | This paper will detail changes in the operational paradigm of the Fermi
National Accelerator Laboratory (FNAL) magnetron $H^{-}$ ion source due to
upgrades in the accelerator system. Prior to November of 2012 the $H^{-}$ ions
for High Energy Physics (HEP) experiments were extracted at ~18 keV vertically
downward into a 90 degree bending magnet and accelerated through a
Cockcroft-Walton accelerating column to 750 keV. Following the upgrade in the
fall of 2012 the $H^{-}$ ions are now directly extracted from a magnetron at 35
keV and accelerated to 750 keV by a Radio Frequency Quadrupole (RFQ). This
change in extraction energy as well as the orientation of the ion source
required not only a redesign of the ion source, but an updated understanding of
its operation at these new values. Discussed in detail are the changes to the
ion source timing, arc discharge current, hydrogen gas pressure, and cesium
delivery system that were needed to maintain consistent operation at >99%
uptime for HEP, with an increased ion source lifetime of over 9 months.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Ball Breaking Away from a Fluid | We consider the withdrawal of a ball from a fluid reservoir to understand the
longevity of the connection between that ball and the fluid it breaks away
from, at intermediate Reynolds numbers. Scaling arguments based on the
processes observed as the ball interacts with the fluid surface were applied to
the `pinch-off time', when the ball breaks its connection with the fluid from
which it has been withdrawn, measured experimentally. At the lowest Reynolds
numbers tested, pinch-off occurs in a `surface seal' close to the reservoir
surface, where at larger Reynolds numbers pinch-off occurs in an `ejecta seal'
close to the ball. Our scaling analysis shows that the connection between ball
and fluid is controlled by the fluid film draining from the ball as it
continues to be winched away from the fluid reservoir. The draining flow itself
depends on the amount of fluid coating the ball on exit from the reservoir. We
consider the possibilities that this coating was created through: a surface
tension driven Landau Levitch Derjaguin wetting of the surface; a
visco-inertial quick coating; or alternatively through the inertia of the fluid
moving with the ball through the reservoir. We show that although the pinch-off
mechanism is controlled by viscosity, the coating mechanism is governed by a
different length and timescale, dictated by the inertial added mass of the ball
when submersed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Unveiling the internal entanglement structure of the Kondo singlet | We disentangle all the individual degrees of freedom in the quantum impurity
problem to deconstruct the Kondo singlet, both in real and energy space, by
studying the contribution of each individual free electron eigenstate. This is
a problem of two spins coupled to a bath, where the bath is formed by the
remaining conduction electrons. Being a mixed state, we resort to the
"concurrence" to quantify entanglement. We identify "projected natural
orbitals" that allow us to individualize a single-particle electronic wave
function that is responsible of more than $90\%$ of the impurity screening. In
the weak coupling regime, the impurity is entangled to an electron at the Fermi
level, while in the strong coupling regime, the impurity counterintuitively
entangles mostly with the high energy electrons and disentangles completely
from the low-energy states carving a "hole" around the Fermi level. This
enables one to use concurrence as a pseudo order parameter to compute the
characteristic "size" of the Kondo cloud, beyond which electrons are are weakly
correlated to the impurity and are dominated by the physics of the boundary.
| 0 | 1 | 0 | 0 | 0 | 0 |
A parallel orbital-updating based plane-wave basis method for electronic structure calculations | Motivated by the recently proposed parallel orbital-updating approach in real
space method, we propose a parallel orbital-updating based plane-wave basis
method for electronic structure calculations, for solving the corresponding
eigenvalue problems. In addition, we propose two new modified parallel
orbital-updating methods. Compared to the traditional plane-wave methods, our
methods allow for two-level parallelization, which is particularly interesting
for large scale parallelization. Numerical experiments show that these new
methods are more reliable and efficient for large scale calculations on modern
supercomputers
| 0 | 1 | 1 | 0 | 0 | 0 |
Dynamics of the multi-soliton waves in the sine-Gordon model with two identical point impurities | The particular type of four-kink multi-solitons (or quadrons) adiabatic
dynamics of the sine-Gordon equation in a model with two identical point
attracting impurities has been studied. This model can be used for describing
magnetization localized waves in multilayer ferromagnet. The quadrons structure
and properties has been numerically investigated. The cases of both large and
small distances between impurities has been viewed. The dependence of the
localized in impurity region nonlinear high-amplitude waves frequencies on the
distance between the impurities has been found. For an analytical description
of two bound localized on impurities nonlinear waves dynamics, using
perturbation theory, the system of differential equations for harmonic
oscillators with elastic link has been found. The analytical model
qualitatively describes the results of the sine-Gordon equation numerical
simulation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder | Estimates of the Hubble constant, $H_0$, from the distance ladder and the
cosmic microwave background (CMB) differ at the $\sim$3-$\sigma$ level,
indicating a potential issue with the standard $\Lambda$CDM cosmology.
Interpreting this tension correctly requires a model comparison calculation
depending on not only the traditional `$n$-$\sigma$' mismatch but also the
tails of the likelihoods. Determining the form of the tails of the local $H_0$
likelihood is impossible with the standard Gaussian least-squares
approximation, as it requires using non-Gaussian distributions to faithfully
represent anchor likelihoods and model outliers in the Cepheid and supernova
(SN) populations, and simultaneous fitting of the full distance-ladder dataset
to correctly propagate uncertainties. We have developed a Bayesian hierarchical
model that describes the full distance ladder, from nearby geometric anchors
through Cepheids to Hubble-Flow SNe. This model does not rely on any
distributions being Gaussian, allowing outliers to be modeled and obviating the
need for arbitrary data cuts. Sampling from the $\sim$3000-parameter joint
posterior using Hamiltonian Monte Carlo, we find $H_0$ = (72.72 $\pm$ 1.67)
${\rm km\,s^{-1}\,Mpc^{-1}}$ when applied to the outlier-cleaned Riess et al.
(2016) data, and ($73.15 \pm 1.78$) ${\rm km\,s^{-1}\,Mpc^{-1}}$ with SN
outliers reintroduced. Our high-fidelity sampling of the low-$H_0$ tail of the
distance-ladder likelihood allows us to apply Bayesian model comparison to
assess the evidence for deviation from $\Lambda$CDM. We set up this comparison
to yield a lower limit on the odds of the underlying model being $\Lambda$CDM
given the distance-ladder and Planck XIII (2016) CMB data. The odds against
$\Lambda$CDM are at worst 10:1 or 7:1, depending on whether the SNe outliers
are cut or modeled, or 60:1 if an approximation to the Planck Int. XLVI (2016)
likelihood is used.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access | A multi-user multi-armed bandit (MAB) framework is used to develop algorithms
for uncoordinated spectrum access. The number of users is assumed to be unknown
to each user. A stochastic setting is first considered, where the rewards on a
channel are the same for each user. In contrast to prior work, it is assumed
that the number of users can possibly exceed the number of channels, and that
rewards can be non-zero even under collisions. The proposed algorithm consists
of an estimation phase and an allocation phase. It is shown that if every user
adopts the algorithm, the system wide regret is constant with time with high
probability. The regret guarantees hold for any number of users and channels,
in particular, even when the number of users is less than the number of
channels. Next, an adversarial multi-user MAB framework is considered, where
the rewards on the channels are user-dependent. It is assumed that the number
of users is less than the number of channels, and that the users receive zero
reward on collision. The proposed algorithm combines the Exp3.P algorithm
developed in prior work for single user adversarial bandits with a collision
resolution mechanism to achieve sub-linear regret. It is shown that if every
user employs the proposed algorithm, the system wide regret is of the order
$O(T^\frac{3}{4})$ over a horizon of time $T$. The algorithms in both
stochastic and adversarial scenarios are extended to the dynamic case where the
number of users in the system evolves over time and are shown to lead to
sub-linear regret.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation | In this paper, we analyze the effects of contact models on contact-implicit
trajectory optimization for manipulation. We consider three different
approaches: (1) a contact model that is based on complementarity constraints,
(2) a smooth contact model, and our proposed method (3) a variable smooth
contact model. We compare these models in simulation in terms of physical
accuracy, quality of motions, and computation time. In each case, the
optimization process is initialized by setting all torque variables to zero,
namely, without a meaningful initial guess. For simulations, we consider a
pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our
results demonstrate that the optimization based on the proposed variable smooth
contact model provides a good trade-off between the physical fidelity and
quality of motions at the cost of increased computation time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Computationally Efficient Measures of Internal Neuron Importance | The challenge of assigning importance to individual neurons in a network is
of interest when interpreting deep learning models. In recent work, Dhamdhere
et al. proposed Total Conductance, a "natural refinement of Integrated
Gradients" for attributing importance to internal neurons. Unfortunately, the
authors found that calculating conductance in tensorflow required the addition
of several custom gradient operators and did not scale well. In this work, we
show that the formula for Total Conductance is mathematically equivalent to
Path Integrated Gradients computed on a hidden layer in the network. We provide
a scalable implementation of Total Conductance using standard tensorflow
gradient operators that we call Neuron Integrated Gradients. We compare Neuron
Integrated Gradients to DeepLIFT, a pre-existing computationally efficient
approach that is applicable to calculating internal neuron importance. We find
that DeepLIFT produces strong empirical results and is faster to compute, but
because it lacks the theoretical properties of Neuron Integrated Gradients, it
may not always be preferred in practice. Colab notebook reproducing results:
this http URL
| 0 | 0 | 0 | 1 | 0 | 0 |
Subsets and Splits