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Calabi-Yau metrics on canonical bundles of complex flag manifolds | In the present paper we provide a description of complete Calabi-Yau metrics
on the canonical bundle of generalized complex flag manifolds. By means of Lie
theory we give an explicit description of complete Ricci-flat Kähler metrics
obtained through the Calabi ansatz technique. We use this approach to provide
several explicit examples of noncompact complete Calabi-Yau manifolds, these
examples include canonical bundles of non-toric flag manifolds (e.g. Grassmann
manifolds and full flag manifolds).
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The ratio of normalizing constants for Bayesian graphical Gaussian model selection | Many graphical Gaussian selection methods in a Bayesian framework use the
G-Wishart as the conjugate prior on the precision matrix. The Bayes factor to
compare a model governed by a graph G and a model governed by the neighboring
graph G-e, derived from G by deleting an edge e, is a function of the ratios of
prior and posterior normalizing constants of the G-Wishart for G and G-e.
While more recent methods avoid the computation of the posterior ratio,
computing the ratio of prior normalizing constants, (2) below, has remained a
computational stumbling block. In this paper, we propose an explicit analytic
approximation to (2) which is equal to the ratio of two Gamma functions
evaluated at (delta+d)/2 and (delta+d+1)/2 respectively, where delta is the
shape parameter of the G-Wishart and d is the number of paths of length two
between the endpoints of e. This approximation allows us to avoid Monte Carlo
methods, is computationally inexpensive and is scalable to high-dimensional
problems. We show that the ratio of the approximation to the true value is
always between zero and one and so, one cannot incur wild errors.
In the particular case where the paths between the endpoints of e are
disjoint, we show that the approximation is very good. When the paths between
these two endpoints are not disjoint we give a sufficient condition for the
approximation to be good. Numerical results show that the ratio of the
approximation to the true value of the prior ratio is always between .55 and 1
and very often close to 1. We compare the results obtained with a model search
using our approximation and a search using the double Metropolis-Hastings
algorithm to compute the prior ratio. The results are extremely close.
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Angiogenic Factors produced by Hypoxic Cells are a leading driver of Anastomoses in Sprouting Angiogenesis---a computational study | Angiogenesis - the growth of new blood vessels from a pre-existing
vasculature - is key in both physiological processes and on several
pathological scenarios such as cancer progression or diabetic retinopathy. For
the new vascular networks to be functional, it is required that the growing
sprouts merge either with an existing functional mature vessel or with another
growing sprout. This process is called anastomosis. We present a systematic 2D
and 3D computational study of vessel growth in a tissue to address the
capability of angiogenic factor gradients to drive anastomosis formation. We
consider that these growth factors are produced only by tissue cells in
hypoxia, i.e. until nearby vessels merge and become capable of carrying blood
and irrigating their vicinity. We demonstrate that this increased production of
angiogenic factors by hypoxic cells is able to promote vessel anastomoses
events in both 2D and 3D. The simulations also verify that the morphology of
these networks has an increased resilience toward variations in the endothelial
cell's proliferation and chemotactic response. The distribution of tissue
cell`s and the concentration of the growth factors they produce are the major
factors in determining the final morphology of the network.
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Henri Bénard: Thermal convection and vortex shedding | We present in this article the work of Henri Bénard (1874-1939), French
physicist who began the systematic experimental study of two hydrodynamic
systems: the thermal convection of fluids heated from below (the
Rayleigh-Bénard convection and the Bénard-Marangoni convection) and the
periodical vortex shedding behind a bluff body in a flow (the
Bénard-Kármán vortex street). Across his scientific biography, we review
the interplay between experiments and theory in these two major subjects of
fluid mechanics.
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Surjective H-Colouring: New Hardness Results | A homomorphism from a graph G to a graph H is a vertex mapping f from the
vertex set of G to the vertex set of H such that there is an edge between
vertices f(u) and f(v) of H whenever there is an edge between vertices u and v
of G. The H-Colouring problem is to decide whether or not a graph G allows a
homomorphism to a fixed graph H. We continue a study on a variant of this
problem, namely the Surjective H-Colouring problem, which imposes the
homomorphism to be vertex-surjective. We build upon previous results and show
that this problem is NP-complete for every connected graph H that has exactly
two vertices with a self-loop as long as these two vertices are not adjacent.
As a result, we can classify the computational complexity of Surjective
H-Colouring for every graph H on at most four vertices.
| 1 | 0 | 1 | 0 | 0 | 0 |
Subband adaptive filter trained by differential evolution for channel estimation | The normalized subband adaptive filter (NSAF) is widely accepted as a
preeminent adaptive filtering algorithm because of its efficiency under the
colored excitation. However, the convergence rate of NSAF is slow. To address
this drawback, in this paper, a variant of the NSAF, called the differential
evolution (DE)-NSAF (DE-NSAF), is proposed for channel estimation based on DE
strategy. It is worth noticing that there are several papers concerning
designing DE strategies for adaptive filter. But their signal models are still
the single adaptive filter model rather than the fullband adaptive filter model
considered in this paper. Thus, the problem considered in our work is quite
different from those. The proposed DE-NSAF algorithm is based on real-valued
manipulations and has fast convergence rate for searching the global solution
of optimized weight vector. Moreover, a design step of new algorithm is given
in detail. Simulation results demonstrate the improved performance of the
proposed DE-NSAF algorithm in terms of the convergence rate.
| 1 | 0 | 0 | 0 | 0 | 0 |
Distributionally Robust Games: f-Divergence and Learning | In this paper we introduce the novel framework of distributionally robust
games. These are multi-player games where each player models the state of
nature using a worst-case distribution, also called adversarial distribution.
Thus each player's payoff depends on the other players' decisions and on the
decision of a virtual player (nature) who selects an adversarial distribution
of scenarios. This paper provides three main contributions. Firstly, the
distributionally robust game is formulated using the statistical notions of
$f$-divergence between two distributions, here represented by the adversarial
distribution, and the exact distribution. Secondly, the complexity of the
problem is significantly reduced by means of triality theory. Thirdly,
stochastic Bregman learning algorithms are proposed to speedup the computation
of robust equilibria. Finally, the theoretical findings are illustrated in a
convex setting and its limitations are tested with a non-convex non-concave
function.
| 1 | 0 | 1 | 0 | 0 | 0 |
Modeling and Reasoning About Wireless Networks: A Graph-based Calculus Approach | We propose a graph-based process calculus for modeling and reasoning about
wireless networks with local broadcasts. Graphs are used at syntactical level
to describe the topological structures of networks. This calculus is equipped
with a reduction semantics and a labelled transition semantics. The former is
used to define weak barbed congruence. The latter is used to define a
parameterized weak bisimulation emphasizing locations and local broadcasts. We
prove that weak bisimilarity implies weak barbed congruence. The potential
applications are illustrated by some examples and two case studies.
| 1 | 0 | 0 | 0 | 0 | 0 |
Weyl's law on $RCD^*(K,N)$ metric measure spaces | In this paper, we will prove the Weyl's law for the asymptotic formula of
Dirichlet eigenvalues on metric measure spaces with generalized Ricci curvature
bounded from below.
| 0 | 0 | 1 | 0 | 0 | 0 |
The area of the Mandelbrot set and Zagier's conjecture | We prove Zagier's conjecture regarding the 2-adic valuation of the
coefficients $\{b_m\}$ that appear in Ewing and Schober's series formula for
the area of the Mandelbrot set in the case where $m\equiv 2 \mod 4$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization | We develop an approximate formula for evaluating a cross-validation estimator
of predictive likelihood for multinomial logistic regression regularized by an
$\ell_1$-norm. This allows us to avoid repeated optimizations required for
literally conducting cross-validation; hence, the computational time can be
significantly reduced. The formula is derived through a perturbative approach
employing the largeness of the data size and the model dimensionality. An
extension to the elastic net regularization is also addressed. The usefulness
of the approximate formula is demonstrated on simulated data and the ISOLET
dataset from the UCI machine learning repository.
| 0 | 1 | 0 | 1 | 0 | 0 |
Realizing an optimization approach inspired from Piagets theory on cognitive development | The objective of this paper is to introduce an artificial intelligence based
optimization approach, which is inspired from Piagets theory on cognitive
development. The approach has been designed according to essential processes
that an individual may experience while learning something new or improving his
/ her knowledge. These processes are associated with the Piagets ideas on an
individuals cognitive development. The approach expressed in this paper is a
simple algorithm employing swarm intelligence oriented tasks in order to
overcome single-objective optimization problems. For evaluating effectiveness
of this early version of the algorithm, test operations have been done via some
benchmark functions. The obtained results show that the approach / algorithm
can be an alternative to the literature in terms of single-objective
optimization. The authors have suggested the name: Cognitive Development
Optimization Algorithm (CoDOA) for the related intelligent optimization
approach.
| 1 | 0 | 1 | 0 | 0 | 0 |
Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions | In conventional chemisorption model, the d-band center theory (augmented
sometimes with the upper edge of d-band for imporved accuarcy) plays a central
role in predicting adsorption energies and catalytic activity as a function of
d-band center of the solid surfaces, but it requires density functional
calculations that can be quite costly for large scale screening purposes of
materials. In this work, we propose to use the d-band width of the muffin-tin
orbital theory (to account for local coordination environment) plus
electronegativity (to account for adsorbate renormalization) as a simple set of
alternative descriptors for chemisorption, which do not demand the ab initio
calculations. This pair of descriptors are then combined with machine learning
methods, namely, artificial neural network (ANN) and kernel ridge regression
(KRR), to allow large scale materials screenings. We show, for a toy set of 263
alloy systems, that the CO adsorption energy can be predicted with a remarkably
small mean absolute deviation error of 0.05 eV, a significantly improved result
as compared to 0.13 eV obtained with descriptors including costly d-band center
calculations in literature. We achieved this high accuracy by utilizing an
active learning algorithm, without which the accuracy was 0.18 eV otherwise. As
a practical application of this machine, we identified Cu3Y@Cu as a highly
active and cost-effective electrochemical CO2 reduction catalyst to produce CO
with the overpotential 0.37 V lower than Au catalyst.
| 0 | 1 | 0 | 1 | 0 | 0 |
Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility | Extracting significant places or places of interest (POIs) using individuals'
spatio-temporal data is of fundamental importance for human mobility analysis.
Classical clustering methods have been used in prior work for detecting POIs,
but without considering temporal constraints. Usually, the involved parameters
for clustering are difficult to determine, e.g., the optimal cluster number in
hierarchical clustering. Currently, researchers either choose heuristic values
or use spatial distance-based optimization to determine an appropriate
parameter set. We argue that existing research does not optimally address
temporal information and thus leaves much room for improvement. Considering
temporal constraints in human mobility, we introduce an effective clustering
approach - namely POI clustering with temporal constraints (PC-TC) - to extract
POIs from spatio-temporal data of human mobility. Following human mobility
nature in modern society, our approach aims to extract both global POIs (e.g.,
workplace or university) and local POIs (e.g., library, lab, and canteen).
Based on two publicly available datasets including 193 individuals, our
evaluation results show that PC-TC has much potential for next place prediction
in terms of granularity (i.e., the number of extracted POIs) and
predictability.
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The maximum number of zeros of $r(z) - \overline{z}$ revisited | Generalizing several previous results in the literature on rational harmonic
functions, we derive bounds on the maximum number of zeros of functions $f(z) =
\frac{p(z)}{q(z)} - \overline{z}$, which depend on both $\mathrm{deg}(p)$ and
$\mathrm{deg}(q)$. Furthermore, we prove that any function that attains one of
these upper bounds is regular.
| 0 | 0 | 1 | 0 | 0 | 0 |
High Luminosity Large Hadron Collider HL-LHC | HL-LHC federates the efforts and R&D of a large international community
towards the ambitious HL- LHC objectives and contributes to establishing the
European Research Area (ERA) as a focal point of global research cooperation
and a leader in frontier knowledge and technologies. HL-LHC relies on strong
participation from various partners, in particular from leading US and Japanese
laboratories. This participation will be required for the execution of the
construction phase as a global project. In particular, the US LHC Accelerator
R&D Program (LARP) has developed some of the key technologies for the HL-LHC,
such as the large-aperture niobium-tin ($Nb_{3}Sn) quadrupoles and the crab
cavities. The proposed governance model is tailored accordingly and should pave
the way for the organization of the construction phase.
| 0 | 1 | 0 | 0 | 0 | 0 |
Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies | Various approaches have been proposed to learn visuo-motor policies for
real-world robotic applications. One solution is first learning in simulation
then transferring to the real world. In the transfer, most existing approaches
need real-world images with labels. However, the labelling process is often
expensive or even impractical in many robotic applications. In this paper, we
propose an adversarial discriminative sim-to-real transfer approach to reduce
the cost of labelling real data. The effectiveness of the approach is
demonstrated with modular networks in a table-top object reaching task where a
7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter
through visual observations. The adversarial transfer approach reduced the
labelled real data requirement by 50%. Policies can be transferred to real
environments with only 93 labelled and 186 unlabelled real images. The
transferred visuo-motor policies are robust to novel (not seen in training)
objects in clutter and even a moving target, achieving a 97.8% success rate and
1.8 cm control accuracy.
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Algebraic Bethe ansatz for the trigonometric sl(2) Gaudin model with triangular boundary | In the derivation of the generating function of the Gaudin Hamiltonians with
boundary terms, we follow the same approach used previously in the rational
case, which in turn was based on Sklyanin's method in the periodic case. Our
derivation is centered on the quasi-classical expansion of the linear
combination of the transfer matrix of the XXZ Heisenberg spin chain and the
central element, the so-called Sklyanin determinant. The corresponding Gaudin
Hamiltonians with boundary terms are obtained as the residues of the generating
function. By defining the appropriate Bethe vectors which yield strikingly
simple off-shell action of the generating function, we fully implement the
algebraic Bethe ansatz, obtaining the spectrum of the generating function and
the corresponding Bethe equations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Marked Temporal Dynamics Modeling based on Recurrent Neural Network | We are now witnessing the increasing availability of event stream data, i.e.,
a sequence of events with each event typically being denoted by the time it
occurs and its mark information (e.g., event type). A fundamental problem is to
model and predict such kind of marked temporal dynamics, i.e., when the next
event will take place and what its mark will be. Existing methods either
predict only the mark or the time of the next event, or predict both of them,
yet separately. Indeed, in marked temporal dynamics, the time and the mark of
the next event are highly dependent on each other, requiring a method that
could simultaneously predict both of them. To tackle this problem, in this
paper, we propose to model marked temporal dynamics by using a mark-specific
intensity function to explicitly capture the dependency between the mark and
the time of the next event. Extensive experiments on two datasets demonstrate
that the proposed method outperforms state-of-the-art methods at predicting
marked temporal dynamics.
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A Bayesian framework for distributed estimation of arrival rates in asynchronous networks | In this paper we consider a network of agents monitoring a spatially
distributed arrival process. Each node measures the number of arrivals seen at
its monitoring point in a given time-interval with the objective of estimating
the unknown local arrival rate. We propose an asynchronous distributed approach
based on a Bayesian model with unknown hyperparameter, where each node computes
the minimum mean square error (MMSE) estimator of its local arrival rate in a
distributed way. As a result, the estimation at each node "optimally" fuses the
information from the whole network through a distributed optimization
algorithm. Moreover, we propose an ad-hoc distributed estimator, based on a
consensus algorithm for time-varying and directed graphs, which exhibits
reduced complexity and exponential convergence. We analyze the performance of
the proposed distributed estimators, showing that they: (i) are reliable even
in presence of limited local data, and (ii) improve the estimation accuracy
compared to the purely decentralized setup. Finally, we provide a statistical
characterization of the proposed estimators. In particular, for the ad-hoc
estimator, we show that as the number of nodes goes to infinity its mean square
error converges to the optimal one. Numerical Monte Carlo simulations confirm
the theoretical characterization and highlight the appealing performances of
the estimators.
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Rigidity of branching microstructures in shape memory alloys | We analyze generic sequences for which the geometrically linear energy
\[E_\eta(u,\chi):= \eta^{-\frac{2}{3}}\int_{B_{0}(1)} \left| e(u)-
\sum_{i=1}^3 \chi_ie_i\right|^2 d x+\eta^\frac{1}{3} \sum_{i=1}^3
|D\chi_i|(B_{0}(1))\] remains bounded in the limit $\eta \to 0$. Here $ e(u)
:=1/2(Du + Du^T)$ is the (linearized) strain of the displacement $u$, the
strains $e_i$ correspond to the martensite strains of a shape memory alloy
undergoing cubic-to-tetragonal transformations and $\chi_i:B_{0}(1) \to
\{0,1\}$ is the partition into phases. In this regime it is known that in
addition to simple laminates also branched structures are possible, which if
austenite was present would enable the alloy to form habit planes.
In an ansatz-free manner we prove that the alignment of macroscopic
interfaces between martensite twins is as predicted by well-known rank-one
conditions. Our proof proceeds via the non-convex, non-discrete-valued
differential inclusion \[e(u) \in \bigcup_{1\leq i\neq j\leq 3}
\operatorname{conv} \{e_i,e_j\}\] satisfied by the weak limits of bounded
energy sequences and of which we classify all solutions. In particular, there
exist no convex integration solutions of the inclusion with complicated
geometric structures.
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Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository | Machine learning qualifies computers to assimilate with data, without being
solely programmed [1, 2]. Machine learning can be classified as supervised and
unsupervised learning. In supervised learning, computers learn an objective
that portrays an input to an output hinged on training input-output pairs [3].
Most efficient and widely used supervised learning algorithms are K-Nearest
Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor
(LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this
paper is to implement these elegant learning algorithms on eleven different
datasets from the UCI machine learning repository to observe the variation of
accuracies for each of the algorithms on all datasets. Analyzing the accuracy
of the algorithms will give us a brief idea about the relationship of the
machine learning algorithms and the data dimensionality. All the algorithms are
developed in Matlab. Upon such accuracy observation, the comparison can be
built among KNN, SVM, LMNN, and ENN regarding their performances on each
dataset.
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Network Classification in Temporal Networks Using Motifs | Network classification has a variety of applications, such as detecting
communities within networks and finding similarities between those representing
different aspects of the real world. However, most existing work in this area
focus on examining static undirected networks without considering directed
edges or temporality. In this paper, we propose a new methodology that utilizes
feature representation for network classification based on the temporal motif
distribution of the network and a null model for comparing against random
graphs. Experimental results show that our method improves accuracy by up
$10\%$ compared to the state-of-the-art embedding method in network
classification, for tasks such as classifying network type, identifying
communities in email exchange network, and identifying users given their
app-switching behaviors.
| 1 | 0 | 0 | 0 | 0 | 0 |
The strength of Ramsey's theorem for pairs and arbitrarily many colors | In this paper, we show that $\mathrm{RT}^{2}+\mathsf{WKL}_0$ is a
$\Pi^{1}_{1}$-conservative extension of $\mathrm{B}\Sigma^0_3$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Joint Power Allocation and Beamforming for Energy-Efficient Two-Way Multi-Relay Communications | This paper considers the joint design of user power allocation and relay
beamforming in relaying communications, in which multiple pairs of
single-antenna users exchange information with each other via multiple-antenna
relays in two time slots. All users transmit their signals to the relays in the
first time slot while the relays broadcast the beamformed signals to all users
in the second time slot. The aim is to maximize the system's energy efficiency
(EE) subject to quality-of-service (QoS) constraints in terms of exchange
throughput requirements. The QoS constraints are nonconvex with many nonlinear
cross-terms, so finding a feasible point is already computationally
challenging. The sum throughput appears in the numerator while the total
consumption power appears in the denominator of the EE objective function. The
former is a nonconcave function and the latter is a nonconvex function, making
fractional programming useless for EE optimization. Nevertheless, efficient
iterations of low complexity to obtain its optimized solutions are developed.
The performances of the multiple-user and multiple-relay networks under various
scenarios are evaluated to show the merit of the paper development.
| 1 | 0 | 0 | 0 | 0 | 0 |
Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen | Armed conflict has led to an unprecedented number of internally displaced
persons (IDPs) - individuals who are forced out of their homes but remain
within their country. IDPs often urgently require shelter, food, and
healthcare, yet prediction of when large fluxes of IDPs will cross into an area
remains a major challenge for aid delivery organizations. Accurate forecasting
of IDP migration would empower humanitarian aid groups to more effectively
allocate resources during conflicts. We show that monthly flow of IDPs from
province to province in both Syria and Yemen can be accurately forecasted one
month in advance, using publicly available data. We model monthly IDP flow
using data on food price, fuel price, wage, geospatial, and news data. We find
that machine learning approaches can more accurately forecast migration trends
than baseline persistence models. Our findings thus potentially enable
proactive aid allocation for IDPs in anticipation of forecasted arrivals.
| 0 | 0 | 0 | 1 | 0 | 0 |
Entropy? Honest! | Here we deconstruct, and then in a reasoned way reconstruct, the concept of
"entropy of a system," paying particular attention to where the randomness may
be coming from. We start with the core concept of entropy as a COUNT associated
with a DESCRIPTION; this count (traditionally expressed in logarithmic form for
a number of good reasons) is in essence the number of possibilities---specific
instances or "scenarios," that MATCH that description. Very natural (and
virtually inescapable) generalizations of the idea of description are the
probability distribution and of its quantum mechanical counterpart, the density
operator.
We track the process of dynamically updating entropy as a system evolves.
Three factors may cause entropy to change: (1) the system's INTERNAL DYNAMICS;
(2) unsolicited EXTERNAL INFLUENCES on it; and (3) the approximations one has
to make when one tries to predict the system's future state. The latter task is
usually hampered by hard-to-quantify aspects of the original description,
limited data storage and processing resource, and possibly algorithmic
inadequacy. Factors 2 and 3 introduce randomness into one's predictions and
accordingly degrade them. When forecasting, as long as the entropy bookkeping
is conducted in an HONEST fashion, this degradation will ALWAYS lead to an
entropy increase.
To clarify the above point we introduce the notion of HONEST ENTROPY, which
coalesces much of what is of course already done, often tacitly, in responsible
entropy-bookkeping practice. This notion, we believe, will help to fill an
expressivity gap in scientific discourse. With its help we shall prove that ANY
dynamical system---not just our physical universe---strictly obeys Clausius's
original formulation of the second law of thermodynamics IF AND ONLY IF it is
invertible. Thus this law is a TAUTOLOGICAL PROPERTY of invertible systems!
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Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors | We study the problem of generating adversarial examples in a black-box
setting in which only loss-oracle access to a model is available. We introduce
a framework that conceptually unifies much of the existing work on black-box
attacks, and we demonstrate that the current state-of-the-art methods are
optimal in a natural sense. Despite this optimality, we show how to improve
black-box attacks by bringing a new element into the problem: gradient priors.
We give a bandit optimization-based algorithm that allows us to seamlessly
integrate any such priors, and we explicitly identify and incorporate two
examples. The resulting methods use two to four times fewer queries and fail
two to five times less often than the current state-of-the-art.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Dynamic Model of Central Counterparty Risk | We introduce a dynamic model of the default waterfall of derivatives CCPs and
propose a risk sensitive method for sizing the initial margin (IM), and the
default fund (DF) and its allocation among clearing members. Using a Markovian
structure model of joint credit migrations, our evaluation of DF takes into
account the joint credit quality of clearing members as they evolve over time.
Another important aspect of the proposed methodology is the use of the time
consistent dynamic risk measures for computation of IM and DF. We carry out a
comprehensive numerical study, where, in particular, we analyze the advantages
of the proposed methodology and its comparison with the currently prevailing
methods used in industry.
| 0 | 0 | 0 | 0 | 0 | 1 |
Half-Duplex Base Station with Adaptive Scheduling of the in-Band Uplink-Receptions and Downlink-Transmissions | In this paper, we propose a novel reception/transmission scheme for
half-duplex base stations (BSs). In particular, we propose a half-duplex BS
that employes in-band uplink-receptions from user 1 and downlink-transmissions
to user 2, which occur in different time slots. Furthermore, we propose optimal
adaptive scheduling of the in-band uplink-receptions and downlink-transmissions
of the BS such that the uplink-downlink rate/throughput region is maximized and
the outage probabilities of the uplink and downlink channels are minimized.
Practically, this results in selecting whether in a given time slot the BS
should receive from user 1 or transmit to user 2 based on the qualities of the
in-band uplink-reception and downlink-transmission channels. Compared to the
performance achieved with a conventional full-duplex division (FDD) base
station, two main gains can be highlighted: 1) Increased uplink-downlink
rate/throughput region; 2) Doubling of the diversity gain of both the uplink
and downlink channels.
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HAT-P-26b: A Neptune-Mass Exoplanet with a Well Constrained Heavy Element Abundance | A correlation between giant-planet mass and atmospheric heavy elemental
abundance was first noted in the past century from observations of planets in
our own Solar System, and has served as a cornerstone of planet formation
theory. Using data from the Hubble and Spitzer Space Telescopes from 0.5 to 5
microns, we conducted a detailed atmospheric study of the transiting
Neptune-mass exoplanet HAT-P-26b. We detected prominent H2O absorption bands
with a maximum base-to-peak amplitude of 525ppm in the transmission spectrum.
Using the water abundance as a proxy for metallicity, we measured HAT-P-26b's
atmospheric heavy element content [4.8 (-4.0 +21.5) times solar]. This likely
indicates that HAT-P-26b's atmosphere is primordial and obtained its gaseous
envelope late in its disk lifetime, with little contamination from metal-rich
planetesimals.
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Robust Localization Using Range Measurements with Unknown and Bounded Errors | Cooperative geolocation has attracted significant research interests in
recent years. A large number of localization algorithms rely on the
availability of statistical knowledge of measurement errors, which is often
difficult to obtain in practice. Compared with the statistical knowledge of
measurement errors, it can often be easier to obtain the measurement error
bound. This work investigates a localization problem assuming unknown
measurement error distribution except for a bound on the error. We first
formulate this localization problem as an optimization problem to minimize the
worst-case estimation error, which is shown to be a non-convex optimization
problem. Then, relaxation is applied to transform it into a convex one.
Furthermore, we propose a distributed algorithm to solve the problem, which
will converge in a few iterations. Simulation results show that the proposed
algorithms are more robust to large measurement errors than existing algorithms
in the literature. Geometrical analysis providing additional insights is also
provided.
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Intuitionistic Non-Normal Modal Logics: A general framework | We define a family of intuitionistic non-normal modal logics; they can bee
seen as intuitionistic counterparts of classical ones. We first consider
monomodal logics, which contain only one between Necessity and Possibility. We
then consider the more important case of bimodal logics, which contain both
modal operators. In this case we define several interactions between Necessity
and Possibility of increasing strength, although weaker than duality. For all
logics we provide both a Hilbert axiomatisation and a cut-free sequent
calculus, on its basis we also prove their decidability. We then give a
semantic characterisation of our logics in terms of neighbourhood models. Our
semantic framework captures modularly not only our systems but also already
known intuitionistic non-normal modal logics such as Constructive K (CK) and
the propositional fragment of Wijesekera's Constructive Concurrent Dynamic
Logic.
| 1 | 0 | 0 | 0 | 0 | 0 |
Externalities in Socially-Based Resource Sharing Network | This paper investigates the impact of link formation between a pair of agents
on resource availability of other agents in a social cloud network, which is a
special case of socially-based resource sharing systems. Specifically, we study
the correlation between externalities, network size, and network density.
We first conjecture and experimentally support that if an agent experiences
positive externalities, then its closeness (harmonic centrality measure) should
increase. Next, we show the following for ring networks: in less populated
networks no agent experiences positive externalities; in more populated
networks a set of agents experience positive externalities, and larger the
distance between agents forming a link, more the number of beneficiaries; and
the number of beneficiaries is always less than the number of
non-beneficiaries. Finally, we show that network density is inversely
proportional to positive externalities, and further, it plays a crucial role in
determining the kind of externalities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits | Consider the problem: given data pair $(\mathbf{x}, \mathbf{y})$ drawn from a
population with $f_*(x) = \mathbf{E}[\mathbf{y} | \mathbf{x} = x]$, specify a
neural network and run gradient flow on the weights over time until reaching
any stationarity. How does $f_t$, the function computed by the neural network
at time $t$, relate to $f_*$, in terms of approximation and representation?
What are the provable benefits of the adaptive representation by neural
networks compared to the pre-specified fixed basis representation in the
classical nonparametric literature? We answer the above questions via a dynamic
reproducing kernel Hilbert space (RKHS) approach indexed by the training
process of neural networks. We show that when reaching any local stationarity,
gradient flow learns an adaptive RKHS representation, and performs the global
least squares projection onto the adaptive RKHS, simultaneously. In addition,
we prove that as the RKHS is data-adaptive and task-specific, the residual for
$f_*$ lies in a subspace that is smaller than the orthogonal complement of the
RKHS, formalizing the representation and approximation benefits of neural
networks.
| 1 | 0 | 1 | 1 | 0 | 0 |
On utility maximization without passing by the dual problem | We treat utility maximization from terminal wealth for an agent with utility
function $U:\mathbb{R}\to\mathbb{R}$ who dynamically invests in a
continuous-time financial market and receives a possibly unbounded random
endowment. We prove the existence of an optimal investment without introducing
the associated dual problem. We rely on a recent result of Orlicz space theory,
due to Delbaen and Owari which leads to a simple and transparent proof.
Our results apply to non-smooth utilities and even strict concavity can be
relaxed. We can handle certain random endowments with non-hedgeable risks,
complementing earlier papers. Constraints on the terminal wealth can also be
incorporated.
As examples, we treat frictionless markets with finitely many assets and
large financial markets.
| 0 | 0 | 1 | 0 | 0 | 0 |
Stochastic Generative Hashing | Learning-based binary hashing has become a powerful paradigm for fast search
and retrieval in massive databases. However, due to the requirement of discrete
outputs for the hash functions, learning such functions is known to be very
challenging. In addition, the objective functions adopted by existing hashing
techniques are mostly chosen heuristically. In this paper, we propose a novel
generative approach to learn hash functions through Minimum Description Length
principle such that the learned hash codes maximally compress the dataset and
can also be used to regenerate the inputs. We also develop an efficient
learning algorithm based on the stochastic distributional gradient, which
avoids the notorious difficulty caused by binary output constraints, to jointly
optimize the parameters of the hash function and the associated generative
model. Extensive experiments on a variety of large-scale datasets show that the
proposed method achieves better retrieval results than the existing
state-of-the-art methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Nonlinear learning and learning advantages in evolutionary games | The idea of incompetence as a learning or adaptation function was introduced
in the context of evolutionary games as a fixed parameter. However, live
organisms usually perform different nonlinear adaptation functions such as a
power law or exponential fitness growth. Here, we examine how the functional
form of the learning process may affect the social competition between
different behavioral types. Further, we extend our results for the evolutionary
games where fluctuations in the environment affect the behavioral adaptation of
competing species and demonstrate importance of the starting level of
incompetence for survival. Hence, we define a new concept of learning
advantages that becomes crucial when environments are constantly changing and
requiring rapid adaptation from species. This may lead to the evolutionarily
weak phase when even evolutionary stable populations become vulnerable to
invasions.
| 0 | 0 | 0 | 0 | 1 | 0 |
Information-theoretic Limits for Community Detection in Network Models | We analyze the information-theoretic limits for the recovery of node labels
in several network models. This includes the Stochastic Block Model, the
Exponential Random Graph Model, the Latent Space Model, the Directed
Preferential Attachment Model, and the Directed Small-world Model. For the
Stochastic Block Model, the non-recoverability condition depends on the
probabilities of having edges inside a community, and between different
communities. For the Latent Space Model, the non-recoverability condition
depends on the dimension of the latent space, and how far and spread are the
communities in the latent space. For the Directed Preferential Attachment Model
and the Directed Small-world Model, the non-recoverability condition depends on
the ratio between homophily and neighborhood size. We also consider dynamic
versions of the Stochastic Block Model and the Latent Space Model.
| 1 | 0 | 0 | 1 | 0 | 0 |
Proofs of life: molecular-biology reasoning simulates cell behaviors from first principles | We axiomatize the molecular-biology reasoning style, verify compliance of the
standard reference: Ptashne, A Genetic Switch, and present proof-theory-induced
technologies to predict phenotypes and life cycles from genotypes. The key is
to note that `reductionist discipline' entails constructive reasoning, i.e.,
that any argument for a compound property is constructed from more basic
arguments. Proof theory makes explicit the inner structure of the axiomatized
reasoning style and allows the permissible dynamics to be presented as a mode
of computation that can be executed and analyzed. Constructivity and
executability guarantee simulation when working over domain-specific languages.
Here, we exhibit phenotype properties for genotype reasons: a molecular-biology
argument is an open-system concurrent computation that results in compartment
changes and is performed among processes of physiology change as determined
from the molecular programming of given DNA. Life cycles are the possible
sequentializations of the processes. A main implication of our construction is
that technical correctness provides a complementary perspective on science that
is as fundamental there as it is for pure mathematics, provided mature
reductionism exists.
| 0 | 0 | 0 | 0 | 1 | 0 |
Beyond recursion operators | We briefly recall the history of the Nijenhuis torsion of (1,1)-tensors on
manifolds and of the lesser-known Haantjes torsion. We then show how the
Haantjes manifolds of Magri and the symplectic-Haantjes structures of Tempesta
and Tondo generalize the classical approach to integrable systems in the
bi-hamiltonian and symplectic-Nijenhuis formalisms, the sequence of powers of
the recursion operator being replaced by a family of commuting Haantjes
operators.
| 0 | 0 | 1 | 0 | 0 | 0 |
On Classical Integrability of the Hydrodynamics of Quantum Integrable Systems | Recently, a hydrodynamic description of local equilibrium dynamics in quantum
integrable systems was discovered. In the diffusionless limit, this is
equivalent to a certain "Bethe-Boltzmann" kinetic equation, which has the form
of an integro-differential conservation law in $(1+1)$D. The purpose of the
present work is to investigate the sense in which the Bethe-Boltzmann equation
defines an "integrable kinetic equation". To this end, we study a class of $N$
dimensional systems of evolution equations that arise naturally as
finite-dimensional approximations to the Bethe-Boltzmann equation. We obtain
non-local Poisson brackets and Hamiltonian densities for these equations and
derive an infinite family of first integrals, parameterized by $N$ functional
degrees of freedom. We find that the conserved charges arising from quantum
integrability map to Casimir invariants of the hydrodynamic bracket and their
group velocities map to Hamiltonian flows. Some results from the
finite-dimensional setting extend to the underlying integro-differential
equation, providing evidence for its integrability in the hydrodynamic sense.
| 0 | 1 | 0 | 0 | 0 | 0 |
Transition to turbulence when the Tollmien-Schlichting and bypass routes coexist | Plane Poiseuille flow, the pressure driven flow between parallel plates,
shows a route to turbulence connected with a linear instability to
Tollmien-Schlichting (TS) waves, and another one, the bypass transition, that
is triggered with finite amplitude perturbation. We use direct numerical
simulations to explore the arrangement of the different routes to turbulence
among the set of initial conditions. For plates that are a distance $2H$ apart
and in a domain of width $2\pi H$ and length $2\pi H$ the subcritical
instability to TS waves sets in at $Re_{c}=5815$ that extends down to
$Re_{TS}\approx4884$. The bypass route becomes available above $Re_E=459$ with
the appearance of three-dimensional finite-amplitude traveling waves. The
bypass transition covers a large set of finite amplitude perturbations. Below
$Re_c$, TS appear for a tiny set of initial conditions that grows with
increasing Reynolds number. Above $Re_c$ the previously stable region becomes
unstable via TS waves, but a sharp transition to the bypass route can still be
identified. Both routes lead to the same turbulent in the final stage of the
transition, but on different time scales. Similar phenomena can be expected in
other flows where two or more routes to turbulence compete.
| 0 | 1 | 0 | 0 | 0 | 0 |
Interactive Exploration and Discovery of Scientific Publications with PubVis | With an exponentially growing number of scientific papers published each
year, advanced tools for exploring and discovering publications of interest are
becoming indispensable. To empower users beyond a simple keyword search
provided e.g. by Google Scholar, we present the novel web application PubVis.
Powered by a variety of machine learning techniques, it combines essential
features to help researchers find the content most relevant to them. An
interactive visualization of a large collection of scientific publications
provides an overview of the field and encourages the user to explore articles
beyond a narrow research focus. This is augmented by personalized content based
article recommendations as well as an advanced full text search to discover
relevant references. The open sourced implementation of the app can be easily
set up and run locally on a desktop computer to provide access to content
tailored to the specific needs of individual users. Additionally, a PubVis demo
with access to a collection of 10,000 papers can be tested online.
| 1 | 0 | 0 | 0 | 0 | 0 |
Improving phase II oncology trials using best observed RECIST response as an endpoint by modelling continuous tumour measurements | In many phase II trials in solid tumours, patients are assessed using
endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST)
scale. Often, analyses are based on the response rate. This is the proportion
of patients who have an observed tumour shrinkage above a pre-defined level and
no new tumour lesions. The augmented binary method has been proposed to improve
the precision of the estimator of the response rate. The method involves
modelling the tumour shrinkage to avoid dichotomising it. However, in many
trials the best observed response is used as the primary outcome. In such
trials, patients are followed until progression, and their best observed RECIST
outcome is used as the primary endpoint. In this paper, we propose a method
that extends the augmented binary method so that it can be used when the
outcome is best observed response. We show through simulated data and data from
a real phase II cancer trial that this method improves power in both single-arm
and randomised trials. The average gain in power compared to the traditional
analysis is equivalent to approximately a 35% increase in sample size. A
modified version of the method is proposed to reduce the computational effort
required. We show this modified method maintains much of the efficiency
advantages.
| 0 | 0 | 0 | 1 | 0 | 0 |
Evans-Selberg potential on planar domains | We provide explicit formulas of Evans kernels, Evans-Selberg potentials and
fundamental metrics on potential-theoretically parabolic planar domains.
| 0 | 0 | 1 | 0 | 0 | 0 |
Bridging the Gap Between Computational Photography and Visual Recognition | What is the current state-of-the-art for image restoration and enhancement
applied to degraded images acquired under less than ideal circumstances? Can
the application of such algorithms as a pre-processing step to improve image
interpretability for manual analysis or automatic visual recognition to
classify scene content? While there have been important advances in the area of
computational photography to restore or enhance the visual quality of an image,
the capabilities of such techniques have not always translated in a useful way
to visual recognition tasks. Consequently, there is a pressing need for the
development of algorithms that are designed for the joint problem of improving
visual appearance and recognition, which will be an enabling factor for the
deployment of visual recognition tools in many real-world scenarios. To address
this, we introduce the UG^2 dataset as a large-scale benchmark composed of
video imagery captured under challenging conditions, and two enhancement tasks
designed to test algorithmic impact on visual quality and automatic object
recognition. Furthermore, we propose a set of metrics to evaluate the joint
improvement of such tasks as well as individual algorithmic advances, including
a novel psychophysics-based evaluation regime for human assessment and a
realistic set of quantitative measures for object recognition performance. We
introduce six new algorithms for image restoration or enhancement, which were
created as part of the IARPA sponsored UG^2 Challenge workshop held at CVPR
2018. Under the proposed evaluation regime, we present an in-depth analysis of
these algorithms and a host of deep learning-based and classic baseline
approaches. From the observed results, it is evident that we are in the early
days of building a bridge between computational photography and visual
recognition, leaving many opportunities for innovation in this area.
| 1 | 0 | 0 | 0 | 0 | 0 |
On some conjectures of Samuels and Feige | Let $\mu_1 \ge \dotsc \ge \mu_n > 0$ and $\mu_1 + \dotsm + \mu_n = 1$. Let
$X_1, \dotsc, X_n$ be independent non-negative random variables with $EX_1 =
\dotsc = EX_n = 1$, and let $Z = \sum_{i=1}^n \mu_i X_i$. Let $M = \max_{1 \le
i \le n} \mu_i = \mu_1$, and let $\delta > 0$ and $T = 1 + \delta$. Both
Samuels and Feige formulated conjectures bounding the probability $P(Z < T)$
from above. We prove that Samuels' conjecture implies a conjecture of Feige.
| 0 | 0 | 1 | 0 | 0 | 0 |
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States | Making the right decision in traffic is a challenging task that is highly
dependent on individual preferences as well as the surrounding environment.
Therefore it is hard to model solely based on expert knowledge. In this work we
use Deep Reinforcement Learning to learn maneuver decisions based on a compact
semantic state representation. This ensures a consistent model of the
environment across scenarios as well as a behavior adaptation function,
enabling on-line changes of desired behaviors without re-training. The input
for the neural network is a simulated object list similar to that of Radar or
Lidar sensors, superimposed by a relational semantic scene description. The
state as well as the reward are extended by a behavior adaptation function and
a parameterization respectively. With little expert knowledge and a set of
mid-level actions, it can be seen that the agent is capable to adhere to
traffic rules and learns to drive safely in a variety of situations.
| 1 | 0 | 0 | 1 | 0 | 0 |
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows | We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but
simulating data from the model is possible. SNL trains an autoregressive flow
on simulated data in order to learn a model of the likelihood in the region of
high posterior density. A sequential training procedure guides simulations and
reduces simulation cost by orders of magnitude. We show that SNL is more
robust, more accurate and requires less tuning than related neural-based
methods, and we discuss diagnostics for assessing calibration, convergence and
goodness-of-fit.
| 0 | 0 | 0 | 1 | 0 | 0 |
Creativity: Generating Diverse Questions using Variational Autoencoders | Generating diverse questions for given images is an important task for
computational education, entertainment and AI assistants. Different from many
conventional prediction techniques is the need for algorithms to generate a
diverse set of plausible questions, which we refer to as "creativity". In this
paper we propose a creative algorithm for visual question generation which
combines the advantages of variational autoencoders with long short-term memory
networks. We demonstrate that our framework is able to generate a large set of
varying questions given a single input image.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cooling dynamics of a single trapped ion via elastic collisions with small-mass atoms | We demonstrated sympathetic cooling of a single ion in a buffer gas of
ultracold atoms with small mass. Efficient collisional cooling was realized by
suppressing collision-induced heating. We attempt to explain the experimental
results with a simple rate equation model and provide a quantitative discussion
of the cooling efficiency per collision. The knowledge we obtained in this work
is an important ingredient for advancing the technique of sympathetic cooling
of ions with neutral atoms.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modulational instability in the full-dispersion Camassa-Holm equation | We determine the stability and instability of a sufficiently small and
periodic traveling wave to long wavelength perturbations, for a nonlinear
dispersive equation which extends a Camassa-Holm equation to include all the
dispersion of water waves and the Whitham equation to include nonlinearities of
medium amplitude waves. In the absence of the effects of surface tension, the
result qualitatively agrees with the Benjamin-Feir instability of a Stokes
wave. In the presence of the effects of surface tension, it qualitatively
agrees with those from formal asymptotic expansions of the physical problem and
it improves upon that for the Whitham equation, correctly predicting the limit
of strong surface tension. We discuss the modulational stability and
instability in the Camassa-Holm equation and related models.
| 0 | 1 | 1 | 0 | 0 | 0 |
Evaluating and Modelling Hanabi-Playing Agents | Agent modelling involves considering how other agents will behave, in order
to influence your own actions. In this paper, we explore the use of agent
modelling in the hidden-information, collaborative card game Hanabi. We
implement a number of rule-based agents, both from the literature and of our
own devising, in addition to an Information Set Monte Carlo Tree Search
(IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new,
predictor version that uses a model of the agents with which it is paired. We
observe a significant improvement in game-playing strength from this agent in
comparison to IS-MCTS, resulting from its consideration of what the other
agents in a game would do. In addition, we create a flawed rule-based agent to
highlight the predictor's capabilities with such an agent.
| 1 | 0 | 0 | 0 | 0 | 0 |
Rank-related dimension bounds for subspaces of bilinear forms over finite fields | Let q be a power of a prime and let V be a vector space of finite dimension n
over the field of order q. Let Bil(V) denote the set of all bilinear forms
defined on V x V, let Symm(V) denote the subspace of Bil(V) consisting of
symmetric bilinear forms, and Alt(V) denote the subspace of alternating
bilinear forms. Let M denote a subspace of any of the spaces Bil(V), Symm(V),
or Alt(V). In this paper we investigate hypotheses on the rank of the non-zero
elements of M which lead to reasonable bounds for dim M. Typically, we look at
the case where exactly two or three non-zero ranks occur, one of which is
usually n. In the case that M achieves the maximal dimension predicted by the
dimension bound, we try to enumerate the number of forms of a given rank in M
and describe geometric properties of the radicals of the degenerate elements of
M.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks | Bayesian Networks have been widely used in the last decades in many fields,
to describe statistical dependencies among random variables. In general,
learning the structure of such models is a problem with considerable
theoretical interest that still poses many challenges. On the one hand, this is
a well-known NP-complete problem, which is practically hardened by the huge
search space of possible solutions. On the other hand, the phenomenon of
I-equivalence, i.e., different graphical structures underpinning the same set
of statistical dependencies, may lead to multimodal fitness landscapes further
hindering maximum likelihood approaches to solve the task. Despite all these
difficulties, greedy search methods based on a likelihood score coupled with a
regularization term to account for model complexity, have been shown to be
surprisingly effective in practice. In this paper, we consider the formulation
of the task of learning the structure of Bayesian Networks as an optimization
problem based on a likelihood score. Nevertheless, our approach do not adjust
this score by means of any of the complexity terms proposed in the literature;
instead, it accounts directly for the complexity of the discovered solutions by
exploiting a multi-objective optimization procedure. To this extent, we adopt
NSGA-II and define the first objective function to be the likelihood of a
solution and the second to be the number of selected arcs. We thoroughly
analyze the behavior of our method on a wide set of simulated data, and we
discuss the performance considering the goodness of the inferred solutions both
in terms of their objective functions and with respect to the retrieved
structure. Our results show that NSGA-II can converge to solutions
characterized by better likelihood and less arcs than classic approaches,
although paradoxically frequently characterized by a lower similarity to the
target network.
| 0 | 0 | 0 | 1 | 0 | 0 |
Simultaneous Localization and Layout Model Selection in Manhattan Worlds | In this paper, we will demonstrate how Manhattan structure can be exploited
to transform the Simultaneous Localization and Mapping (SLAM) problem, which is
typically solved by a nonlinear optimization over feature positions, into a
model selection problem solved by a convex optimization over higher order
layout structures, namely walls, floors, and ceilings. Furthermore, we show how
our novel formulation leads to an optimization procedure that automatically
performs data association and loop closure and which ultimately produces the
simplest model of the environment that is consistent with the available
measurements. We verify our method on real world data sets collected with
various sensing modalities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Robust method for finding sparse solutions to linear inverse problems using an L2 regularization | We analyzed the performance of a biologically inspired algorithm called the
Corrected Projections Algorithm (CPA) when a sparseness constraint is required
to unambiguously reconstruct an observed signal using atoms from an
overcomplete dictionary. By changing the geometry of the estimation problem,
CPA gives an analytical expression for a binary variable that indicates the
presence or absence of a dictionary atom using an L2 regularizer. The
regularized solution can be implemented using an efficient real-time
Kalman-filter type of algorithm. The smoother L2 regularization of CPA makes it
very robust to noise, and CPA outperforms other methods in identifying known
atoms in the presence of strong novel atoms in the signal.
| 1 | 0 | 0 | 1 | 0 | 0 |
Humanoid Robot-Application and Influence | Application of humanoid robots has been common in the field of healthcare and
education. It has been recurrently used to improve social behavior and mollify
distress level among children with autism, cancer and cerebral palsy. This
article discusses the same from a human factors perspective. It shows how
people of different age and gender have a different opinion towards the
application and acceptance of humanoid robots. Additionally, this article
highlights the influence of cerebral condition and social interaction on a user
behavior and attitude towards humanoid robots. Our study performed a literature
review and found that (a) children and elderly individuals prefer humanoid
robots due to inactive social interaction, (b) The deterministic behavior of
humanoid robots can be acknowledged to improve social behavior of autistic
children, (c) Trust on humanoid robots is highly driven by its application and
a user age, gender, and social life.
| 1 | 0 | 0 | 0 | 0 | 0 |
Onsager's Conjecture for the Incompressible Euler Equations in Bounded Domains | The goal of this note is to show that, also in a bounded domain $\Omega
\subset \mathbb{R}^n$, with $\partial \Omega\in C^2$, any weak solution,
$(u(x,t),p(x,t))$, of the Euler equations of ideal incompressible fluid in
$\Omega\times (0,T) \subset \mathbb{R}^n\times\mathbb{R}_t$, with the
impermeability boundary condition: $u\cdot \vec n =0$ on
$\partial\Omega\times(0,T)$, is of constant energy on the interval $(0,T)$
provided the velocity field $u \in L^3((0,T);
C^{0,\alpha}(\overline{\Omega}))$, with $\alpha>\frac13\,.$
| 0 | 1 | 1 | 0 | 0 | 0 |
Fuzzy logic based approaches for gene regulatory network inference | The rapid advancement in high-throughput techniques has fueled the generation
of large volume of biological data rapidly with low cost. Some of these
techniques are microarray and next generation sequencing which provides genome
level insight of living cells. As a result, the size of most of the biological
databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These
biological data are analyzed using computational techniques for knowledge
discovery - which is one of the objectives of bioinformatics research. Gene
regulatory network (GRN) is a gene-gene interaction network which plays pivotal
role in understanding gene regulation process and disease studies. From the
last couple of decades, the researchers are interested in developing
computational algorithms for GRN inference (GRNI) using high-throughput
experimental data. Several computational approaches have been applied for
inferring GRN from gene expression data including statistical techniques
(correlation coefficient), information theory (mutual information), regression
based approaches, probabilistic approaches (Bayesian networks, naive byes),
artificial neural networks, and fuzzy logic. The fuzzy logic, along with its
hybridization with other intelligent approach, is well studied in GRNI due to
its several advantages. In this paper, we present a consolidated review on
fuzzy logic and its hybrid approaches for GRNI developed during last two
decades.
| 0 | 0 | 0 | 0 | 1 | 0 |
Instrumentation for nuclear magnetic resonance in zero and ultralow magnetic field | We review instrumentation for nuclear magnetic resonance (NMR) in zero and
ultra-low magnetic field (ZULF, below 0.1 $\mu$T) where detection is based on a
low-cost, non-cryogenic, spin-exchange relaxation free (SERF) $^{87}$Rb atomic
magnetometer. The typical sensitivity is 20-30 fT/Hz$^{1/2}$ for signal
frequencies below 1 kHz and NMR linewidths range from Hz all the way down to
tens of mHz. These features enable precision measurements of chemically
informative nuclear spin-spin couplings as well as nuclear spin precession in
ultra-low magnetic fields.
| 0 | 1 | 0 | 0 | 0 | 0 |
Countable dense homogeneity and the Cantor set | It is shown that CH implies the existence of a compact Hausdorff space that
is countable dense homogeneous, crowded and does not contain topological copies
of the Cantor set. This contrasts with a previous result by the author which
says that for any crowded Hausdorff space $X$ of countable $\pi$-weight, if
${}^\omega{X}$ is countable dense homogeneous, then $X$ must contain a
topological copy of the Cantor set.
| 0 | 0 | 1 | 0 | 0 | 0 |
Dimension Estimation Using Random Connection Models | Information about intrinsic dimension is crucial to perform dimensionality
reduction, compress information, design efficient algorithms, and do
statistical adaptation. In this paper we propose an estimator for the intrinsic
dimension of a data set. The estimator is based on binary neighbourhood
information about the observations in the form of two adjacency matrices, and
does not require any explicit distance information. The underlying graph is
modelled according to a subset of a specific random connection model, sometimes
referred to as the Poisson blob model. Computationally the estimator scales
like n log n, and we specify its asymptotic distribution and rate of
convergence. A simulation study on both real and simulated data shows that our
approach compares favourably with some competing methods from the literature,
including approaches that rely on distance information.
| 0 | 0 | 1 | 1 | 0 | 0 |
Optimal projection of observations in a Bayesian setting | Optimal dimensionality reduction methods are proposed for the Bayesian
inference of a Gaussian linear model with additive noise in presence of
overabundant data. Three different optimal projections of the observations are
proposed based on information theory: the projection that minimizes the
Kullback-Leibler divergence between the posterior distributions of the original
and the projected models, the one that minimizes the expected Kullback-Leibler
divergence between the same distributions, and the one that maximizes the
mutual information between the parameter of interest and the projected
observations. The first two optimization problems are formulated as the
determination of an optimal subspace and therefore the solution is computed
using Riemannian optimization algorithms on the Grassmann manifold. Regarding
the maximization of the mutual information, it is shown that there exists an
optimal subspace that minimizes the entropy of the posterior distribution of
the reduced model; a basis of the subspace can be computed as the solution to a
generalized eigenvalue problem; an a priori error estimate on the mutual
information is available for this particular solution; and that the
dimensionality of the subspace to exactly conserve the mutual information
between the input and the output of the models is less than the number of
parameters to be inferred. Numerical applications to linear and nonlinear
models are used to assess the efficiency of the proposed approaches, and to
highlight their advantages compared to standard approaches based on the
principal component analysis of the observations.
| 0 | 0 | 1 | 1 | 0 | 0 |
Notes on the Multiplicative Ergodic Theorem | The Oseledets Multiplicative Ergodic theorem is a basic result with numerous
applications throughout dynamical systems. These notes provide an introduction
to this theorem, as well as subsequent generalizations. They are based on
lectures at summer schools in Brazil, France, and Russia.
| 0 | 0 | 1 | 0 | 0 | 0 |
Targeted Learning with Daily EHR Data | Electronic health records (EHR) data provide a cost and time-effective
opportunity to conduct cohort studies of the effects of multiple time-point
interventions in the diverse patient population found in real-world clinical
settings. Because the computational cost of analyzing EHR data at daily (or
more granular) scale can be quite high, a pragmatic approach has been to
partition the follow-up into coarser intervals of pre-specified length. Current
guidelines suggest employing a 'small' interval, but the feasibility and
practical impact of this recommendation has not been evaluated and no formal
methodology to inform this choice has been developed. We start filling these
gaps by leveraging large-scale EHR data from a diabetes study to develop and
illustrate a fast and scalable targeted learning approach that allows to follow
the current recommendation and study its practical impact on inference. More
specifically, we map daily EHR data into four analytic datasets using 90, 30,
15 and 5-day intervals. We apply a semi-parametric and doubly robust estimation
approach, the longitudinal TMLE, to estimate the causal effects of four dynamic
treatment rules with each dataset, and compare the resulting inferences. To
overcome the computational challenges presented by the size of these data, we
propose a novel TMLE implementation, the 'long-format TMLE', and rely on the
latest advances in scalable data-adaptive machine-learning software, xgboost
and h2o, for estimation of the TMLE nuisance parameters.
| 0 | 0 | 0 | 1 | 0 | 0 |
Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques | A framework for the generation of bridge-specific fragility utilizing the
capabilities of machine learning and stripe-based approach is presented in this
paper. The proposed methodology using random forests helps to generate or
update fragility curves for a new set of input parameters with less
computational effort and expensive re-simulation. The methodology does not
place any assumptions on the demand model of various components and helps to
identify the relative importance of each uncertain variable in their seismic
demand model. The methodology is demonstrated through the case studies of
multi-span concrete bridges in California. Geometric, material and structural
uncertainties are accounted for in the generation of bridge models and
fragility curves. It is also noted that the traditional lognormality assumption
on the demand model leads to unrealistic fragility estimates. Fragility results
obtained the proposed methodology curves can be deployed in risk assessment
platform such as HAZUS for regional loss estimation.
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Accountability of AI Under the Law: The Role of Explanation | The ubiquity of systems using artificial intelligence or "AI" has brought
increasing attention to how those systems should be regulated. The choice of
how to regulate AI systems will require care. AI systems have the potential to
synthesize large amounts of data, allowing for greater levels of
personalization and precision than ever before---applications range from
clinical decision support to autonomous driving and predictive policing. That
said, there exist legitimate concerns about the intentional and unintentional
negative consequences of AI systems. There are many ways to hold AI systems
accountable. In this work, we focus on one: explanation. Questions about a
legal right to explanation from AI systems was recently debated in the EU
General Data Protection Regulation, and thus thinking carefully about when and
how explanation from AI systems might improve accountability is timely. In this
work, we review contexts in which explanation is currently required under the
law, and then list the technical considerations that must be considered if we
desired AI systems that could provide kinds of explanations that are currently
required of humans.
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Frequency-oriented sub-sampling by photonic Fourier transform and I/Q demodulation | Sub-sampling can acquire directly a passband within a broad radio frequency
(RF) range, avoiding down-conversion and low-phase-noise tunable local
oscillation (LO). However, sub-sampling suffers from band folding and
self-image interference. In this paper we propose a frequency-oriented
sub-sampling to solve the two problems. With ultrashort optical pulse and a
pair of chromatic dispersions, the broadband RF signal is firstly short-time
Fourier-transformed to a spectrum-spread pulse. Then a time slot, corresponding
to the target spectrum slice, is coherently optical-sampled with
in-phase/quadrature (I/Q) demodulation. We demonstrate the novel bandpass
sampling by a numerical example, which shows the desired uneven intensity
response, i.e. pre-filtering. We show in theory that appropriate time-stretch
capacity from dispersion can result in pre-filtering bandwidth less than
sampling rate. Image rejection due to I/Q sampling is also analyzed. A
proof-of-concept experiment, which is based on a time-lens sampling source and
chirped fiber Bragg gratings (CFBGs), shows the center-frequency-tunable
pre-filtered sub-sampling with bandwidth of 6 GHz around, as well as imaging
rejection larger than 26 dB. Our technique may benefit future broadband RF
receivers for frequency-agile Radar or channelization.
| 0 | 1 | 0 | 0 | 0 | 0 |
A moment map picture of relative balanced metrics on extremal Kähler manifolds | We give a moment map interpretation of some relatively balanced metrics. As
an application, we extend a result of S. K. Donaldson on constant scalar
curvature Kähler metrics to the case of extremal metrics. Namely, we show
that a given extremal metric is the limit of some specific relatively balanced
metrics. As a corollary, we recover uniqueness and splitting results for
extremal metrics in the polarized case.
| 0 | 0 | 1 | 0 | 0 | 0 |
Traffic models with adversarial vehicle behaviour | We examine the impact of adversarial actions on vehicles in traffic. Current
advances in assisted/autonomous driving technologies are supposed to reduce the
number of casualties, but this seems to be desired despite the recently proved
insecurity of in-vehicle communication buses or components. Fortunately to some
extent, while compromised cars have become a reality, the numerous attacks
reported so far on in-vehicle electronics are exclusively concerned with
impairments of a single target. In this work we put adversarial behavior under
a more complex scenario where driving decisions deluded by corrupted
electronics can affect more than one vehicle. Particularly, we focus our
attention on chain collisions involving multiple vehicles that can be amplified
by simple adversarial interventions, e.g., delaying taillights or falsifying
speedometer readings. We provide metrics for assessing adversarial impact and
consider safety margins against adversarial actions. Moreover, we discuss
intelligent adversarial behaviour by which the creation of rogue platoons is
possible and speed manipulations become stealthy to human drivers. We emphasize
that our work does not try to show the mere fact that imprudent speeds and
headways lead to chain-collisions, but points out that an adversary may favour
such scenarios (eventually keeping his actions stealthy for human drivers) and
further asks for quantifying the impact of adversarial activity or whether
existing traffic regulations are prepared for such situations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments | This paper presents two novel control methodologies for the cooperative
manipulation of an object by N robotic agents. Firstly, we design an adaptive
control protocol which employs quaternion feedback for the object orientation
to avoid potential representation singularities. Secondly, we propose a control
protocol that guarantees predefined transient and steady-state performance for
the object trajectory. Both methodologies are decentralized, since the agents
calculate their own signals without communicating with each other, as well as
robust to external disturbances and model uncertainties. Moreover, we consider
that the grasping points are rigid, and avoid the need for force/torque
measurements. Load distribution is also included via a grasp matrix
pseudo-inverse to account for potential differences in the agents' power
capabilities. Finally, simulation and experimental results with two robotic
arms verify the theoretical findings.
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Conditionally conjugate mean-field variational Bayes for logistic models | Variational Bayes (VB) is a common strategy for approximate Bayesian
inference, but simple methods are only available for specific classes of models
including, in particular, representations having conditionally conjugate
constructions within an exponential family. Models with logit components are an
apparently notable exception to this class, due to the absence of conjugacy
between the logistic likelihood and the Gaussian priors for the coefficients in
the linear predictor. To facilitate approximate inference within this widely
used class of models, Jaakkola and Jordan (2000) proposed a simple variational
approach which relies on a family of tangent quadratic lower bounds of logistic
log-likelihoods, thus restoring conjugacy between these approximate bounds and
the Gaussian priors. This strategy is still implemented successfully, but less
attempts have been made to formally understand the reasons underlying its
excellent performance. To cover this key gap, we provide a formal connection
between the above bound and a recent Pólya-gamma data augmentation for
logistic regression. Such a result places the computational methods associated
with the aforementioned bounds within the framework of variational inference
for conditionally conjugate exponential family models, thereby allowing recent
advances for this class to be inherited also by the methods relying on Jaakkola
and Jordan (2000).
| 0 | 0 | 1 | 1 | 0 | 0 |
A $\frac{3}{2}$-Approximation Algorithm for Tree Augmentation via Chvátal-Gomory Cuts | The weighted tree augmentation problem (WTAP) is a fundamental network design
problem. We are given an undirected tree $G = (V,E)$, an additional set of
edges $L$ called links and a cost vector $c \in \mathbb{R}^L_{\geq 1}$. The
goal is to choose a minimum cost subset $S \subseteq L$ such that $G = (V, E
\cup S)$ is $2$-edge-connected. In the unweighted case, that is, when we have
$c_\ell = 1$ for all $\ell \in L$, the problem is called the tree augmentation
problem (TAP).
Both problems are known to be APX-hard, and the best known approximation
factors are $2$ for WTAP by (Frederickson and JáJá, '81) and $\tfrac{3}{2}$
for TAP due to (Kortsarz and Nutov, TALG '16). In the case where all link costs
are bounded by a constant $M$, (Adjiashvili, SODA '17) recently gave a $\approx
1.96418+\varepsilon$-approximation algorithm for WTAP under this assumption.
This is the first approximation with a better guarantee than $2$ that does not
require restrictions on the structure of the tree or the links.
In this paper, we improve Adjiashvili's approximation to a
$\frac{3}{2}+\varepsilon$-approximation for WTAP under the bounded cost
assumption. We achieve this by introducing a strong LP that combines
$\{0,\frac{1}{2}\}$-Chvátal-Gomory cuts for the standard LP for the problem
with bundle constraints from Adjiashvili. We show that our LP can be solved
efficiently and that it is exact for some instances that arise at the core of
Adjiashvili's approach. This results in the improved guarantee of
$\frac{3}{2}+\varepsilon$. For TAP, this is the best known LP-based result, and
matches the bound of $\frac{3}{2}+\varepsilon$ achieved by the best SDP-based
algorithm due to (Cheriyan and Gao, arXiv '15).
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On Identifying Disaster-Related Tweets: Matching-based or Learning-based? | Social media such as tweets are emerging as platforms contributing to
situational awareness during disasters. Information shared on Twitter by both
affected population (e.g., requesting assistance, warning) and those outside
the impact zone (e.g., providing assistance) would help first responders,
decision makers, and the public to understand the situation first-hand.
Effective use of such information requires timely selection and analysis of
tweets that are relevant to a particular disaster. Even though abundant tweets
are promising as a data source, it is challenging to automatically identify
relevant messages since tweet are short and unstructured, resulting to
unsatisfactory classification performance of conventional learning-based
approaches. Thus, we propose a simple yet effective algorithm to identify
relevant messages based on matching keywords and hashtags, and provide a
comparison between matching-based and learning-based approaches. To evaluate
the two approaches, we put them into a framework specifically proposed for
analyzing disaster-related tweets. Analysis results on eleven datasets with
various disaster types show that our technique provides relevant tweets of
higher quality and more interpretable results of sentiment analysis tasks when
compared to learning approach.
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Self-Stabilizing Disconnected Components Detection and Rooted Shortest-Path Tree Maintenance in Polynomial Steps | We deal with the problem of maintaining a shortest-path tree rooted at some
process r in a network that may be disconnected after topological changes. The
goal is then to maintain a shortest-path tree rooted at r in its connected
component, V\_r, and make all processes of other components detecting that r is
not part of their connected component. We propose, in the composite atomicity
model, a silent self-stabilizing algorithm for this problem working in
semi-anonymous networks, where edges have strictly positive weights. This
algorithm does not require any a priori knowledge about global parameters of
the network. We prove its correctness assuming the distributed unfair daemon,
the most general daemon. Its stabilization time in rounds is at most 3nmax+D,
where nmax is the maximum number of non-root processes in a connected component
and D is the hop-diameter of V\_r. Furthermore, if we additionally assume that
edge weights are positive integers, then it stabilizes in a polynomial number
of steps: namely, we exhibit a bound in O(maxi nmax^3 n), where maxi is the
maximum weight of an edge and n is the number of processes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Optimizing wearable assistive devices with neuromuscular models and optimal control | The coupling of human movement dynamics with the function and design of
wearable assistive devices is vital to better understand the interaction
between the two. Advanced neuromuscular models and optimal control formulations
provide the possibility to study and improve this interaction. In addition,
optimal control can also be used to generate predictive simulations that
generate novel movements for the human model under varying optimization
criterion.
| 1 | 0 | 0 | 0 | 0 | 0 |
Preferential placement for community structure formation | Various models have been recently proposed to reflect and predict different
properties of complex networks. However, the community structure, which is one
of the most important properties, is not well studied and modeled. In this
paper, we suggest a principle called "preferential placement", which allows to
model a realistic clustering structure. We provide an extensive empirical
analysis of the obtained structure as well as some theoretical results.
| 1 | 1 | 0 | 0 | 0 | 0 |
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization | In this paper, we consider the problem of sequentially optimizing a black-box
function $f$ based on noisy samples and bandit feedback. We assume that $f$ is
smooth in the sense of having a bounded norm in some reproducing kernel Hilbert
space (RKHS), yielding a commonly-considered non-Bayesian form of Gaussian
process bandit optimization. We provide algorithm-independent lower bounds on
the simple regret, measuring the suboptimality of a single point reported after
$T$ rounds, and on the cumulative regret, measuring the sum of regrets over the
$T$ chosen points. For the isotropic squared-exponential kernel in $d$
dimensions, we find that an average simple regret of $\epsilon$ requires $T =
\Omega\big(\frac{1}{\epsilon^2} (\log\frac{1}{\epsilon})^{d/2}\big)$, and the
average cumulative regret is at least $\Omega\big( \sqrt{T(\log T)^{d/2}}
\big)$, thus matching existing upper bounds up to the replacement of $d/2$ by
$2d+O(1)$ in both cases. For the Matérn-$\nu$ kernel, we give analogous
bounds of the form $\Omega\big( (\frac{1}{\epsilon})^{2+d/\nu}\big)$ and
$\Omega\big( T^{\frac{\nu + d}{2\nu + d}} \big)$, and discuss the resulting
gaps to the existing upper bounds.
| 1 | 0 | 0 | 1 | 0 | 0 |
Hirota bilinear equations for Painlevé transcendents | We present some observations on the tau-function for the fourth Painlevé
equation. By considering a Hirota bilinear equation of order four for this
tau-function, we describe the general form of the Taylor expansion around an
arbitrary movable zero. The corresponding Taylor series for the tau-functions
of the first and second Painlevé equations, as well as that for the
Weierstrass sigma function, arise naturally as special cases, by setting
certain parameters to zero.
| 0 | 1 | 1 | 0 | 0 | 0 |
Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data | Representational Similarity Analysis (RSA) aims to explore similarities
between neural activities of different stimuli. Classical RSA techniques employ
the inverse of the covariance matrix to explore a linear model between the
neural activities and task events. However, calculating the inverse of a
large-scale covariance matrix is time-consuming and can reduce the stability
and robustness of the final analysis. Notably, it becomes severe when the
number of samples is too large. For facing this shortcoming, this paper
proposes a novel RSA method called gradient-based RSA (GRSA). Moreover, the
proposed method is not restricted to a linear model. In fact, there is a
growing interest in finding more effective ways of using multi-subject and
whole-brain fMRI data. Searchlight technique can extend RSA from the localized
brain regions to the whole-brain regions with smaller memory footprint in each
process. Based on Searchlight, we propose a new method called Spatiotemporal
Searchlight GRSA (SSL-GRSA) that generalizes our ROI-based GRSA algorithm to
the whole-brain data. Further, our approach can handle some computational
challenges while dealing with large-scale, multi-subject fMRI data.
Experimental studies on multi-subject datasets confirm that both proposed
approaches achieve superior performance to other state-of-the-art RSA
algorithms.
| 0 | 0 | 0 | 1 | 1 | 0 |
Diffeomorphic random sampling using optimal information transport | In this article we explore an algorithm for diffeomorphic random sampling of
nonuniform probability distributions on Riemannian manifolds. The algorithm is
based on optimal information transport (OIT)---an analogue of optimal mass
transport (OMT). Our framework uses the deep geometric connections between the
Fisher-Rao metric on the space of probability densities and the right-invariant
information metric on the group of diffeomorphisms. The resulting sampling
algorithm is a promising alternative to OMT, in particular as our formulation
is semi-explicit, free of the nonlinear Monge--Ampere equation. Compared to
Markov Chain Monte Carlo methods, we expect our algorithm to stand up well when
a large number of samples from a low dimensional nonuniform distribution is
needed.
| 0 | 0 | 1 | 1 | 0 | 0 |
On approximations by trigonometric polynomials of classes of functions defined by moduli of smoothness | In this paper, we give a characterization of Nikol'ski\u{\i}-Besov type
classes of functions, given by integral representations of moduli of
smoothness, in terms of series over the moduli of smoothness. Also, necessary
and sufficient conditions in terms of monotone or lacunary Fourier coefficients
for a function to belong to a such a class are given. In order to prove our
results, we make use of certain recent reverse Copson- and Leindler-type
inequalities.
| 0 | 0 | 1 | 0 | 0 | 0 |
Topologically protected Dirac plasmons in graphene | Topological optical states exhibit unique immunity to defects and the ability
to propagate without losses rendering them ideal for photonic applications.A
powerful class of such states is based on time-reversal symmetry breaking of
the optical response.However, existing proposals either involve sophisticated
and bulky structural designs or can only operate in the microwave regime. Here,
we propose and provide a theoretical proof-of-principle demonstration for
highly confined topologically protected optical states to be realized at
infrared frequencies in a simple 2D material structure-a periodically patterned
graphene monolayer-subject to a magnetic field below 1 tesla. In our graphene
honeycomb superlattice structures plasmons exhibit substantial nonreciprocal
behavior at the superlattice junctions, leading to the emergence of
topologically protected edge states and localized bulk modes enabled by the
strong magneto-optical response of this material, which leads to
time-reversal-symmetry breaking already at moderate static magnetic fields. The
proposed approach is simple and robust for realizing topologically nontrivial
2D optical states, not only in graphene, but also in other 2D atomic layers,
and could pave the way for realizing fast, nanoscale, defect-immune devices for
integrated photonics applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates | In this paper we propose and explore the k-Nearest Neighbour UCB algorithm
for multi-armed bandits with covariates. We focus on a setting where the
covariates are supported on a metric space of low intrinsic dimension, such as
a manifold embedded within a high dimensional ambient feature space. The
algorithm is conceptually simple and straightforward to implement. The
k-Nearest Neighbour UCB algorithm does not require prior knowledge of the
either the intrinsic dimension of the marginal distribution or the time
horizon. We prove a regret bound for the k-Nearest Neighbour UCB algorithm
which is minimax optimal up to logarithmic factors. In particular, the
algorithm automatically takes advantage of both low intrinsic dimensionality of
the marginal distribution over the covariates and low noise in the data,
expressed as a margin condition. In addition, focusing on the case of bounded
rewards, we give corresponding regret bounds for the k-Nearest Neighbour KL-UCB
algorithm, which is an analogue of the KL-UCB algorithm adapted to the setting
of multi-armed bandits with covariates. Finally, we present empirical results
which demonstrate the ability of both the k-Nearest Neighbour UCB and k-Nearest
Neighbour KL-UCB to take advantage of situations where the data is supported on
an unknown sub-manifold of a high-dimensional feature space.
| 0 | 0 | 0 | 1 | 0 | 0 |
Proportional Mean Residual Life Model with Censored Survival Data under Case-cohort Design | Proportional mean residual life model is studied for analysing survival data
from the case-cohort design. To simultaneously estimate the regression
parameters and the baseline mean residual life function, weighted estimating
equations based on an inverse selection probability are proposed. The resulting
regression coefficients estimates are shown to be consistent and asymptotic
normal with easily estimated variance-covariance. Simulation studies show that
the proposed estimators perform very well. An application to a real dataset
from the South Welsh nickel refiners study is also given to illustrate the
methodology.
| 0 | 0 | 1 | 1 | 0 | 0 |
Shape differentiation of a steady-state reaction-diffusion problem arising in Chemical Engineering: the case of non-smooth kinetic with dead core | In this paper we consider an extension of the results in shape
differentiation of semilinear equations with smooth nonlinearity presented in
J.I. Díaz and D. Gómez-Castro: An Application of Shape Differentiation to
the Effectiveness of a Steady State Reaction-Diffusion Problem Arising in
Chemical Engineering. Electron. J. Differ. Equations in 2015 to the case in
which the nonlinearities might be less smooth. Namely we will show that Gateaux
shape derivatives exists when the nonlinearity is only Lipschitz continuous,
and we will give a definition of the derivative when the nonlinearity has a
blow up. In this direction, we will study the case of root-type nonlinearities.
| 0 | 0 | 1 | 0 | 0 | 0 |
Analytic evaluation of Coulomb integrals for one, two and three-electron distance operators, $R_{C1}^{-n}R_{D1}^{-m}$, $R_{C1}^{-n}r_{12}^{-m}$ and $r_{12}^{-n}r_{13}^{-m}$ with $n, m=0,1,2$ | The state of the art for integral evaluation is that analytical solutions to
integrals are far more useful than numerical solutions. We evaluate certain
integrals analytically that are necessary in some approaches in quantum
chemistry. In the title, where R stands for nucleus-electron and r for
electron-electron distances, the $(n,m)=(0,0)$ case is trivial, the
$(n,m)=(1,0)$ and (0,1) cases are well known, fundamental milestone in
integration and widely used in computation chemistry, as well as based on
Laplace transformation with integrand exp(-$a^2t^2$). The rest of the cases are
new and need the other Laplace transformation with integrand exp(-$a^2t$) also,
as well as the necessity of a two dimensional version of Boys function comes up
in case. These analytic expressions (up to Gaussian function integrand) are
useful for manipulation with higher moments of inter-electronic distances, for
example in correlation calculations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Herschel-PACS photometry of faint stars | Our aims are to determine flux densities and their photometric accuracy for a
set of seventeen stars that range in flux from intermediately bright (<2.5 Jy)
to faint (>5 mJy) in the far-infrared (FIR). We also aim to derive
signal-to-noise dependence with flux and time, and compare the results with
predictions from the Herschel exposure-time calculation tool. The PACS faint
star sample has allowed a comprehensive sensitivity assessment of the PACS
photometer. Accurate photometry allows us to establish a set of five FIR
primary standard candidates, namely alpha Ari, epsilon Lep, omega,Cap, HD41047
and 42Dra, which are 2 -- 20 times fainter than the faintest PACS fiducial
standard (gamma Dra) with absolute accuracy of <6%. For three of these primary
standard candidates, essential stellar parameters are known, meaning that a
dedicated flux model code may be run.
| 0 | 1 | 0 | 0 | 0 | 0 |
Role of zero synapses in unsupervised feature learning | Synapses in real neural circuits can take discrete values, including zero
(silent or potential) synapses. The computational role of zero synapses in
unsupervised feature learning of unlabeled noisy data is still unclear, thus it
is important to understand how the sparseness of synaptic activity is shaped
during learning and its relationship with receptive field formation. Here, we
formulate this kind of sparse feature learning by a statistical mechanics
approach. We find that learning decreases the fraction of zero synapses, and
when the fraction decreases rapidly around a critical data size, an
intrinsically structured receptive field starts to develop. Further increasing
the data size refines the receptive field, while a very small fraction of zero
synapses remain to act as contour detectors. This phenomenon is discovered not
only in learning a handwritten digits dataset, but also in learning retinal
neural activity measured in a natural-movie-stimuli experiment.
| 1 | 1 | 0 | 0 | 0 | 0 |
On the Pervasiveness of Difference-Convexity in Optimization and Statistics | With the increasing interest in applying the methodology of
difference-of-convex (dc) optimization to diverse problems in engineering and
statistics, this paper establishes the dc property of many well-known functions
not previously known to be of this class. Motivated by a quadratic programming
based recourse function in two-stage stochastic programming, we show that the
(optimal) value function of a copositive (thus not necessarily convex)
quadratic program is dc on the domain of finiteness of the program when the
matrix in the objective function's quadratic term and the constraint matrix are
fixed. The proof of this result is based on a dc decomposition of a piecewise
LC1 function (i.e., functions with Lipschitz gradients). Armed with these new
results and known properties of dc functions existed in the literature, we show
that many composite statistical functions in risk analysis, including the
value-at-risk (VaR), conditional value-at-risk (CVaR), expectation-based,
VaR-based, and CVaR-based random deviation functions are all dc. Adding the
known class of dc surrogate sparsity functions that are employed as
approximations of the l_0 function in statistical learning, our work
significantly expands the family of dc functions and positions them for
fruitful applications.
| 0 | 0 | 1 | 0 | 0 | 0 |
Stochastic Dynamic Optimal Power Flow in Distribution Network with Distributed Renewable Energy and Battery Energy Storage | The penetration of distributed renewable energy (DRE) greatly raises the risk
of distribution network operation such as peak shaving and voltage stability.
Battery energy storage (BES) has been widely accepted as the most potential
application to cope with the challenge of high penetration of DRE. To cope with
the uncertainties and variability of DRE, a stochastic day-ahead dynamic
optimal power flow (DOPF) and its algorithm are proposed. The overall economy
is achieved by fully considering the DRE, BES, electricity purchasing and
active power losses. The rainflow algorithm-based cycle counting method of BES
is incorporated in the DOPF model to capture the cell degradation, greatly
extending the expected BES lifetime and achieving a better economy. DRE
scenarios are generated to consider the uncertainties and correlations based on
the Copula theory. To solve the DOPF model, we propose a Lagrange
relaxation-based algorithm, which has a significantly reduced complexity with
respect to the existing techniques. For this reason, the proposed algorithm
enables much more scenarios incorporated in the DOPF model and better captures
the DRE uncertainties and correlations. Finally, numerical studies for the
day-ahead DOPF in the IEEE 123-node test feeder are presented to demonstrate
the merits of the proposed method. Results show that the actual BES life
expectancy of the proposed model has increased to 4.89 times compared with the
traditional ones. The problems caused by DRE are greatly alleviated by fully
capturing the uncertainties and correlations with the proposed method.
| 0 | 0 | 1 | 0 | 0 | 0 |
Achievable Rate Region of the Zero-Forcing Precoder in a 2 X 2 MU-MISO Broadcast VLC Channel with Per-LED Peak Power Constraint and Dimming Control | In this paper, we consider the 2 X 2 multi-user multiple-input-single-output
(MU-MISO) broadcast visible light communication (VLC) channel with two light
emitting diodes (LEDs) at the transmitter and a single photo diode (PD) at each
of the two users. We propose an achievable rate region of the Zero-Forcing (ZF)
precoder in this 2 X 2 MU-MISO VLC channel under a per-LED peak and average
power constraint, where the average optical power emitted from each LED is
fixed for constant lighting, but is controllable (referred to as dimming
control in IEEE 802.15.7 standard on VLC). We analytically characterize the
proposed rate region boundary and show that it is Pareto-optimal. Further
analysis reveals that the largest rate region is achieved when the fixed
per-LED average optical power is half of the allowed per-LED peak optical
power. We also propose a novel transceiver architecture where the channel
encoder and dimming control are separated which greatly simplifies the
complexity of the transceiver. A case study of an indoor VLC channel with the
proposed transceiver reveals that the achievable information rates are
sensitive to the placement of the LEDs and the PDs. An interesting observation
is that for a given placement of LEDs in a 5 m X 5 m X 3 m room, even with a
substantial displacement of the users from their optimum placement, reduction
in the achievable rates is not significant. This observation could therefore be
used to define "coverage zones" within a room where the reduction in the
information rates to the two users is within an acceptable tolerance limit.
| 1 | 0 | 1 | 0 | 0 | 0 |
Autonomous Electric Race Car Design | Autonomous driving and electric vehicles are nowadays very active research
and development areas. In this paper we present the conversion of a standard
Kyburz eRod into an autonomous vehicle that can be operated in challenging
environments such as Swiss mountain passes. The overall hardware and software
architectures are described in detail with a special emphasis on the sensor
requirements for autonomous vehicles operating in partially structured
environments. Furthermore, the design process itself and the finalized system
architecture are presented. The work shows state of the art results in
localization and controls for self-driving high-performance electric vehicles.
Test results of the overall system are presented, which show the importance of
generalizable state estimation algorithms to handle a plethora of conditions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Temporal Stable Community in Time-Varying Networks | Identifying community structure of a complex network provides insight to the
interdependence between the network topology and emergent collective behaviors
of networks, while detecting such invariant communities in a time-varying
network is more challenging. In this paper, we define the temporal stable
community and newly propose the concept of dynamic modularity to evaluate the
stable community structures in time-varying networks, which is robust against
small changes as verified by several empirical time-varying network datasets.
Besides, using the volatility features of temporal stable communities in
functional brain networks, we successfully differentiate the ADHD (Attention
Deficit Hyperactivity Disorder) patients and healthy controls efficiently.
| 0 | 0 | 0 | 0 | 1 | 0 |
The shape of a rapidly rotating polytrope with index unity | We show that the solutions obtained in the paper `An exact solution for
arbitrarily rotating gaseous polytropes with index unity' by Kong, Zhang, and
Schubert represent only approximate solutions of the free-boundary
Euler-Poisson system of equations describing uniformly rotating,
self-gravitating polytropes with index unity. We discuss the quality of such
solutions as approximations to the rigidly rotating equilibrium polytropic
configurations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measurement of the Planck constant at the National Institute of Standards and Technology from 2015 to 2017 | Researchers at the National Institute of Standards and Technology(NIST) have
measured the value of the Planck constant to be $h =6.626\,069\,934(89)\times
10^{-34}\,$J$\,$s (relative standard uncertainty $13\times 10^{-9}$). The
result is based on over 10$\,$000 weighings of masses with nominal values
ranging from 0.5$\,$kg to 2$\,$kg with the Kibble balance NIST-4. The
uncertainty has been reduced by more than twofold relative to a previous
determination because of three factors: (1) a much larger data set than
previously available, allowing a more realistic, and smaller, Type A
evaluation; (2) a more comprehensive measurement of the back action of the
weighing current on the magnet by weighing masses up to 2$\,$kg, decreasing the
uncertainty associated with magnet non-linearity; (3) a rigorous investigation
of the dependence of the geometric factor on the coil velocity reducing the
uncertainty assigned to time-dependent leakage of current in the coil.
| 0 | 1 | 0 | 0 | 0 | 0 |
Event Stream-Based Process Discovery using Abstract Representations | The aim of process discovery, originating from the area of process mining, is
to discover a process model based on business process execution data. A
majority of process discovery techniques relies on an event log as an input. An
event log is a static source of historical data capturing the execution of a
business process. In this paper we focus on process discovery relying on online
streams of business process execution events. Learning process models from
event streams poses both challenges and opportunities, i.e. we need to handle
unlimited amounts of data using finite memory and, preferably, constant time.
We propose a generic architecture that allows for adopting several classes of
existing process discovery techniques in context of event streams. Moreover, we
provide several instantiations of the architecture, accompanied by
implementations in the process mining tool-kit ProM (this http URL).
Using these instantiations, we evaluate several dimensions of stream-based
process discovery. The evaluation shows that the proposed architecture allows
us to lift process discovery to the streaming domain.
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On $C$-bases, partition pairs and filtrations for induced or restricted Specht modules | We obtain alternative explicit Specht filtrations for the induced and the
restricted Specht modules in the Hecke algebra of the symmetric group (defined
over the ring $A=\mathbb Z[q^{1/2},q^{-1/2}]$ where $q$ is an indeterminate)
using $C$-bases for these modules. Moreover, we provide a link between a
certain $C$-basis for the induced Specht module and the notion of pairs of
partitions.
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