text
stringlengths 57
2.88k
| labels
sequencelengths 6
6
|
|---|---|
Title: Looping and Clustering model for the organization of protein-DNA complexes on the bacterial genome,
Abstract: The bacterial genome is organized in a structure called the nucleoid by a
variety of associated proteins. These proteins can form complexes on DNA that
play a central role in various biological processes, including chromosome
segregation. A prominent example is the large ParB-DNA complex, which forms an
essential component of the segregation machinery in many bacteria. ChIP-Seq
experiments show that ParB proteins localize around centromere-like parS sites
on the DNA to which ParB binds specifically, and spreads from there over large
sections of the chromosome. Recent theoretical and experimental studies suggest
that DNA-bound ParB proteins can interact with each other to condense into a
coherent 3D complex on the DNA. However, the structural organization of this
protein-DNA complex remains unclear, and a predictive quantitative theory for
the distribution of ParB proteins on DNA is lacking. Here, we propose the
Looping and Clustering (LC) model, which employs a statistical physics approach
to describe protein-DNA complexes. The LC model accounts for the extrusion of
DNA loops from a cluster of interacting DNA-bound proteins. Conceptually, the
structure of the protein-DNA complex is determined by a competition between
attractive protein interactions and the configurational and loop entropy of
this protein-DNA cluster. Indeed, we show that the protein interaction strength
determines the "tightness" of the loopy protein-DNA complex. With this approach
we consider the genomic organization of such a protein-DNA cluster around a
single high-affinity binding site. Thus, our model provides a theoretical
framework to quantitatively compute the binding profiles of ParB-like proteins
around a cognate (parS) binding site. | [
0,
1,
0,
0,
0,
0
] |
Title: Mean field limits for nonlinear spatially extended hawkes processes with exponential memory kernels,
Abstract: We consider spatially extended systems of interacting nonlinear Hawkes
processes modeling large systems of neurons placed in Rd and study the
associated mean field limits. As the total number of neurons tends to infinity,
we prove that the evolution of a typical neuron, attached to a given spatial
position, can be described by a nonlinear limit differential equation driven by
a Poisson random measure. The limit process is described by a neural field
equation. As a consequence, we provide a rigorous derivation of the neural
field equation based on a thorough mean field analysis. | [
0,
0,
1,
0,
0,
0
] |
Title: Bias correction in daily maximum and minimum temperature measurements through Gaussian process modeling,
Abstract: The Global Historical Climatology Network-Daily database contains, among
other variables, daily maximum and minimum temperatures from weather stations
around the globe. It is long known that climatological summary statistics based
on daily temperature minima and maxima will not be accurate, if the bias due to
the time at which the observations were collected is not accounted for. Despite
some previous work, to our knowledge, there does not exist a satisfactory
solution to this important problem. In this paper, we carefully detail the
problem and develop a novel approach to address it. Our idea is to impute the
hourly temperatures at the location of the measurements by borrowing
information from the nearby stations that record hourly temperatures, which
then can be used to create accurate summaries of temperature extremes. The key
difficulty is that these imputations of the temperature curves must satisfy the
constraint of falling between the observed daily minima and maxima, and
attaining those values at least once in a twenty-four hour period. We develop a
spatiotemporal Gaussian process model for imputing the hourly measurements from
the nearby stations, and then develop a novel and easy to implement Markov
Chain Monte Carlo technique to sample from the posterior distribution
satisfying the above constraints. We validate our imputation model using hourly
temperature data from four meteorological stations in Iowa, of which one is
hidden and the data replaced with daily minima and maxima, and show that the
imputed temperatures recover the hidden temperatures well. We also demonstrate
that our model can exploit information contained in the data to infer the time
of daily measurements. | [
0,
0,
0,
1,
0,
0
] |
Title: Wave-induced vortex recoil and nonlinear refraction,
Abstract: When a vortex refracts surface waves, the momentum flux carried by the waves
changes direction and the waves induce a reaction force on the vortex. We study
experimentally the resulting vortex distortion. Incoming surface gravity waves
impinge on a steady vortex of velocity $U_0$ driven magneto-hydrodynamically at
the bottom of a fluid layer. The waves induce a shift of the vortex center in
the direction transverse to wave propagation, together with a decrease in
surface vorticity. We interpret these two phenomena in the framework introduced
by Craik and Leibovich (1976): we identify the dimensionless Stokes drift
$S=U_s/U_0$ as the relevant control parameter, $U_s$ being the Stokes drift
velocity of the waves. We propose a simple vortex line model which indicates
that the shift of the vortex center originates from a balance between vorticity
advection by the Stokes drift and self-advection of the vortex. The decrease in
surface vorticity is interpreted as a consequence of vorticity expulsion by the
fast Stokes drift, which confines it at depth. This purely hydrodynamic process
is analogous to the magnetohydrodynamic expulsion of magnetic field by a
rapidly moving conductor through the electromagnetic skin effect. We study
vorticity expulsion in the limit of fast Stokes drift and deduce that the
surface vorticity decreases as $1/S$, a prediction which is compatible with the
experimental data. Such wave-induced vortex distortions have important
consequences for the nonlinear regime of wave refraction: the refraction angle
rapidly decreases with wave intensity. | [
0,
1,
0,
0,
0,
0
] |
Title: First-principles prediction of the stacking fault energy of gold at finite temperature,
Abstract: The intrinsic stacking fault energy (ISFE) $\gamma$ is a material parameter
fundamental to the discussion of plastic deformation mechanisms in metals.
Here, we scrutinize the temperature dependence of the ISFE of Au through
accurate first-principles derived Helmholtz free energies employing both the
super cell approach and the axial Ising model (AIM). A significant decrease of
the ISFE with temperature, $-(36$-$39)$\,\% from 0 to 890\,K depending on the
treatment of thermal expansion, is revealed, which matches the estimate based
on the experimental temperature coefficient $d \gamma / d T $ closely. We make
evident that this decrease predominantly originates from the excess vibrational
entropy at the stacking fault layer, although the contribution arising from the
static lattice expansion compensates it by approximately 60\,\%. Electronic
excitations are found to be of minor importance for the ISFE change with
temperature. We show that the Debye model in combination with the AIM captures
the correct sign but significantly underestimates the magnitude of the
vibrational contribution to $\gamma(T)$. The hexagonal close-packed (hcp) and
double hcp structures are established as metastable phases of Au. Our results
demonstrate that quantitative agreement with experiments can be obtained if all
relevant temperature-induced excitations are considered in first-principles
modeling and that the temperature dependence of the ISFE is substantial enough
to be taken into account in crystal plasticity modeling. | [
0,
1,
0,
0,
0,
0
] |
Title: Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions,
Abstract: We consider the problem of efficient packet dissemination in wireless
networks with point-to-multi-point wireless broadcast channels. We propose a
dynamic policy, which achieves the broadcast capacity of the network. This
policy is obtained by first transforming the original multi-hop network into a
precedence-relaxed virtual single-hop network and then finding an optimal
broadcast policy for the relaxed network. The resulting policy is shown to be
throughput-optimal for the original wireless network using a sample-path
argument. We also prove the NP-completeness of the finite-horizon broadcast
problem, which is in contrast with the polynomial time solvability of the
problem with point-to-point channels. Illustrative simulation results
demonstrate the efficacy of the proposed broadcast policy in achieving the full
broadcast capacity with low delay. | [
1,
0,
1,
0,
0,
0
] |
Title: Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning,
Abstract: Although aviation accidents are rare, safety incidents occur more frequently
and require a careful analysis to detect and mitigate risks in a timely manner.
Analyzing safety incidents using operational data and producing event-based
explanations is invaluable to airline companies as well as to governing
organizations such as the Federal Aviation Administration (FAA) in the United
States. However, this task is challenging because of the complexity involved in
mining multi-dimensional heterogeneous time series data, the lack of
time-step-wise annotation of events in a flight, and the lack of scalable tools
to perform analysis over a large number of events. In this work, we propose a
precursor mining algorithm that identifies events in the multidimensional time
series that are correlated with the safety incident. Precursors are valuable to
systems health and safety monitoring and in explaining and forecasting safety
incidents. Current methods suffer from poor scalability to high dimensional
time series data and are inefficient in capturing temporal behavior. We propose
an approach by combining multiple-instance learning (MIL) and deep recurrent
neural networks (DRNN) to take advantage of MIL's ability to learn using weakly
supervised data and DRNN's ability to model temporal behavior. We describe the
algorithm, the data, the intuition behind taking a MIL approach, and a
comparative analysis of the proposed algorithm with baseline models. We also
discuss the application to a real-world aviation safety problem using data from
a commercial airline company and discuss the model's abilities and
shortcomings, with some final remarks about possible deployment directions. | [
1,
0,
0,
1,
0,
0
] |
Title: The Challenge of Spin-Orbit-Tuned Ground States in Iridates,
Abstract: Effects of spin-orbit interactions in condensed matter are an important and
rapidly evolving topic. Strong competition between spin-orbit, on-site Coulomb
and crystalline electric field interactions in iridates drives exotic quantum
states that are unique to this group of materials. In particular, the Jeff =
1/2 Mott state served as an early signal that the combined effect of strong
spin-orbit and Coulomb interactions in iridates has unique, intriguing
consequences. In this Key Issues Review, we survey some current experimental
studies of iridates. In essence, these materials tend to defy conventional
wisdom: absence of conventional correlations between magnetic and insulating
states, avoidance of metallization at high pressures, S-shaped I-V
characteristic, emergence of an odd-parity hidden order, etc. It is
particularly intriguing that there exist conspicuous discrepancies between
current experimental results and theoretical proposals that address
superconducting, topological and quantum spin liquid phases. This class of
materials, in which the lattice degrees of freedom play a critical role seldom
seen in other materials, evidently presents some profound intellectual
challenges that call for more investigations both experimentally and
theoretically. Physical properties unique to these materials may help unlock a
world of possibilities for functional materials and devices. We emphasize that,
given the rapidly developing nature of this field, this Key Issues Review is by
no means an exhaustive report of the current state of experimental studies of
iridates. | [
0,
1,
0,
0,
0,
0
] |
Title: Derived Picard groups of preprojective algebras of Dynkin type,
Abstract: In this paper, we study two-sided tilting complexes of preprojective algebras
of Dynkin type. We construct the most fundamental class of two-sided tilting
complexes, which has a group structure by derived tensor products and induces a
group of auto-equivalences of the derived category. We show that the group
structure of the two-sided tilting complexes is isomorphic to the braid group
of the corresponding folded graph. Moreover we show that these two-sided
tilting complexes induce tilting mutation and any tilting complex is given as
the derived tensor products of them. Using these results, we determine the
derived Picard group of preprojective algebras for type $A$ and $D$. | [
0,
0,
1,
0,
0,
0
] |
Title: Semi-supervised Learning in Network-Structured Data via Total Variation Minimization,
Abstract: We propose and analyze a method for semi-supervised learning from
partially-labeled network-structured data. Our approach is based on a graph
signal recovery interpretation under a clustering hypothesis that labels of
data points belonging to the same well-connected subset (cluster) are similar
valued. This lends naturally to learning the labels by total variation (TV)
minimization, which we solve by applying a recently proposed primal-dual method
for non-smooth convex optimization. The resulting algorithm allows for a highly
scalable implementation using message passing over the underlying empirical
graph, which renders the algorithm suitable for big data applications. By
applying tools of compressed sensing, we derive a sufficient condition on the
underlying network structure such that TV minimization recovers clusters in the
empirical graph of the data. In particular, we show that the proposed
primal-dual method amounts to maximizing network flows over the empirical graph
of the dataset. Moreover, the learning accuracy of the proposed algorithm is
linked to the set of network flows between data points having known labels. The
effectiveness and scalability of our approach is verified by numerical
experiments. | [
1,
0,
0,
1,
0,
0
] |
Title: Susceptibility of Methicillin Resistant Staphylococcus aureus to Vancomycin using Liposomal Drug Delivery System,
Abstract: Staphylococcus aureus responsible for nosocomial infections is a significant
threat to the public health. The increasing resistance of S.aureus to various
antibiotics has drawn it to a prime focus for research on designing an
appropriate drug delivery system. Emergence of Methicillin Resistant
Staphylococcus aureus (MRSA) in 1961, necessitated the use of vancomycin "the
drug of last resort" to treat these infections. Unfortunately, S.aureus has
already started gaining resistances to vancomycin. Liposome encapsulation of
drugs have been earlier shown to provide an efficient method of microbial
inhibition in many cases. We have studied the effect of liposome encapsulated
vancomycin on MRSA and evaluated the antibacterial activity of the
liposome-entrapped drug in comparison to that of the free drug based on the
minimum inhibitory concentration (MIC) of the drug. The MIC for liposomal
vancomycin was found to be about half of that of free vancomycin. The growth
response of MRSA showed that the liposomal vancomycin induced the culture to go
into bacteriostatic state and phagocytic killing was enhanced. Administration
of the antibiotic encapsulated in liposome thus was shown to greatly improve
the drug delivery as well as the drug resistance caused by MRSA. | [
0,
0,
0,
0,
1,
0
] |
Title: Pseudospectral Model Predictive Control under Partially Learned Dynamics,
Abstract: Trajectory optimization of a controlled dynamical system is an essential part
of autonomy, however many trajectory optimization techniques are limited by the
fidelity of the underlying parametric model. In the field of robotics, a lack
of model knowledge can be overcome with machine learning techniques, utilizing
measurements to build a dynamical model from the data. This paper aims to take
the middle ground between these two approaches by introducing a semi-parametric
representation of the underlying system dynamics. Our goal is to leverage the
considerable information contained in a traditional physics based model and
combine it with a data-driven, non-parametric regression technique known as a
Gaussian Process. Integrating this semi-parametric model with model predictive
pseudospectral control, we demonstrate this technique on both a cart pole and
quadrotor simulation with unmodeled damping and parametric error. In order to
manage parametric uncertainty, we introduce an algorithm that utilizes Sparse
Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We
implement this online learning technique on a cart pole and quadrator, then
demonstrate the use of online learning and obstacle avoidance for the dubin
vehicle dynamics. | [
1,
0,
0,
0,
0,
0
] |
Title: Manifold Regularization for Kernelized LSTD,
Abstract: Policy evaluation or value function or Q-function approximation is a key
procedure in reinforcement learning (RL). It is a necessary component of policy
iteration and can be used for variance reduction in policy gradient methods.
Therefore its quality has a significant impact on most RL algorithms. Motivated
by manifold regularized learning, we propose a novel kernelized policy
evaluation method that takes advantage of the intrinsic geometry of the state
space learned from data, in order to achieve better sample efficiency and
higher accuracy in Q-function approximation. Applying the proposed method in
the Least-Squares Policy Iteration (LSPI) framework, we observe superior
performance compared to widely used parametric basis functions on two standard
benchmarks in terms of policy quality. | [
1,
0,
0,
1,
0,
0
] |
Title: Radio Weak Lensing Shear Measurement in the Visibility Domain - II. Source Extraction,
Abstract: This paper extends the method introduced in Rivi et al. (2016b) to measure
galaxy ellipticities in the visibility domain for radio weak lensing surveys.
In that paper we focused on the development and testing of the method for the
simple case of individual galaxies located at the phase centre, and proposed to
extend it to the realistic case of many sources in the field of view by
isolating visibilities of each source with a faceting technique. In this second
paper we present a detailed algorithm for source extraction in the visibility
domain and show its effectiveness as a function of the source number density by
running simulations of SKA1-MID observations in the band 950-1150 MHz and
comparing original and measured values of galaxies' ellipticities. Shear
measurements from a realistic population of 10^4 galaxies randomly located in a
field of view of 1 deg^2 (i.e. the source density expected for the current
radio weak lensing survey proposal with SKA1) are also performed. At SNR >= 10,
the multiplicative bias is only a factor 1.5 worse than what found when
analysing individual sources, and is still comparable to the bias values
reported for similar measurement methods at optical wavelengths. The additive
bias is unchanged from the case of individual sources, but is significantly
larger than typically found in optical surveys. This bias depends on the shape
of the uv coverage and we suggest that a uv-plane weighting scheme to produce a
more isotropic shape could reduce and control additive bias. | [
0,
1,
0,
0,
0,
0
] |
Title: MOA Data Reveal a New Mass, Distance, and Relative Proper Motion for Planetary System OGLE-2015-BLG-0954L,
Abstract: We present the MOA Collaboration light curve data for planetary microlensing
event OGLE-2015-BLG-0954, which was previously announced in a paper by the
KMTNet and OGLE Collaborations. The MOA data cover the caustic exit, which was
not covered by the KMTNet or OGLE data, and they provide a more reliable
measurement of the finite source effect. The MOA data also provide a new source
color measurement that reveals a lens-source relative proper motion of
$\mu_{\rm rel} = 11.8\pm 0.8\,$mas/yr, which compares to the value of $\mu_{\rm
rel} = 18.4\pm 1.7\,$mas/yr reported in the KMTNet-OGLE paper. This new MOA
value for $\mu_{\rm rel}$ has an a priori probability that is a factor of $\sim
100$ times larger than the previous value, and it does not require a lens
system distance of $D_L < 1\,$kpc. Based on the corrected source color, we find
that the lens system consists of a planet of mass $3.4^{+3.7}_{-1.6} M_{\rm
Jup}$ orbiting a $0.30^{+0.34}_{-0.14}M_\odot$ star at an orbital separation of
$2.1^{+2.2}_{-1.0}\,$AU and a distance of $1.2^{+1.1}_{-0.5}\,$kpc. | [
0,
1,
0,
0,
0,
0
] |
Title: Single crystal polarized neutron diffraction study of the magnetic structure of HoFeO$_3$,
Abstract: Polarised neutron diffraction measurements have been made on HoFeO$_3$ single
crystals magnetised in both the [001] and [100] directions ($Pbnm$ setting).
The polarisation dependencies of Bragg reflection intensities were measured
both with a high field of H = 9 T parallel to [001] at T = 70 K and with the
lower field H = 0.5 T parallel to [100] at T = 5, 15, 25~K. A Fourier
projection of magnetization induced parallel to [001], made using the $hk0$
reflections measured in 9~T, indicates that almost all of it is due to
alignment of Ho moments. Further analysis of the asymmetries of general
reflections in these data showed that although, at 70~K, 9~T applied parallel
to [001] hardly perturbs the antiferromagnetic order of the Fe sublattices, it
induces significant antiferromagnetic order of the Ho sublattices in the
$x\mhyphen y$ plane, with the antiferromagnetic components of moment having the
same order of magnitude as the induced ferromagnetic ones. Strong intensity
asymmetries measured in the low temperature $\Gamma_2$ structure with a lower
field, 0.5 T $\parallel$ [100] allowed the variation of the ordered components
of the Ho and Fe moments to be followed. Their absolute orientations, in the
180\degree\ domain stabilised by the field were determined relative to the
distorted perovskite structure,. This relationship fixes the sign of the
Dzyalshinski-Moriya (D-M) interaction which leads to the weak ferromagnetism.
Our results indicate that the combination of strong y-axis anisotropy of the Ho
moments and Ho-Fe exchange interactions breaks the centrosymmetry of the
structure and could lead to ferroelectric polarization. | [
0,
1,
0,
0,
0,
0
] |
Title: Rigidity of volume-minimizing hypersurfaces in Riemannian 5-manifolds,
Abstract: In this paper we generalize the main result of [4] for manifolds that are not
necessarily Einstein. In fact, we obtain an upper bound for the volume of a
locally volume-minimizing closed hypersurface $\Sigma$ of a Riemannian
5-manifold $M$ with scalar curvature bounded from below by a positive constant
in terms of the total traceless Ricci curvature of $\Sigma$. Furthermore, if
$\Sigma$ saturates the respective upper bound and $M$ has nonnegative Ricci
curvature, then $\Sigma$ is isometric to $\mathbb{S}^4$ up to scaling and $M$
splits in a neighborhood of $\Sigma$. Also, we obtain a rigidity result for the
Riemannian cover of $M$ when $\Sigma$ minimizes the volume in its homotopy
class and saturates the upper bound. | [
0,
0,
1,
0,
0,
0
] |
Title: Dolha - an Efficient and Exact Data Structure for Streaming Graphs,
Abstract: A streaming graph is a graph formed by a sequence of incoming edges with time
stamps. Unlike static graphs, the streaming graph is highly dynamic and time
related. In the real world, the high volume and velocity streaming graphs such
as internet traffic data, social network communication data and financial
transfer data are bringing challenges to the classic graph data structures. We
present a new data structure: double orthogonal list in hash table (Dolha)
which is a high speed and high memory efficiency graph structure applicable to
streaming graph. Dolha has constant time cost for single edge and near linear
space cost that we can contain billions of edges information in memory size and
process an incoming edge in nanoseconds. Dolha also has linear time cost for
neighborhood queries, which allow it to support most algorithms in graphs
without extra cost. We also present a persistent structure based on Dolha that
has the ability to handle the sliding window update and time related queries. | [
1,
0,
0,
0,
0,
0
] |
Title: Minimal Representations of Lie Algebras With Non-Trivial Levi Decomposition,
Abstract: We obtain minimal dimension matrix representations for each of the Lie
algebras of dimension five, six, seven, and eight obtained by Turkowski that
have a non-trivial Levi decomposition. The Key technique involves using
subspace associated to a particular representation of semi-simple Lie algebra
to help in the construction of the radical in the putative Levi decomposition. | [
0,
0,
1,
0,
0,
0
] |
Title: A Longitudinal Higher-Order Diagnostic Classification Model,
Abstract: Providing diagnostic feedback about growth is crucial to formative decisions
such as targeted remedial instructions or interventions. This paper proposed a
longitudinal higher-order diagnostic classification modeling approach for
measuring growth. The new modeling approach is able to provide quantitative
values of overall and individual growth by constructing a multidimensional
higher-order latent structure to take into account the correlations among
multiple latent attributes that are examined across different occasions. In
addition, potential local item dependence among anchor (or repeated) items can
also be taken into account. Model parameter estimation is explored in a
simulation study. An empirical example is analyzed to illustrate the
applications and advantages of the proposed modeling approach. | [
0,
0,
0,
1,
0,
0
] |
Title: On Fairness and Calibration,
Abstract: The machine learning community has become increasingly concerned with the
potential for bias and discrimination in predictive models. This has motivated
a growing line of work on what it means for a classification procedure to be
"fair." In this paper, we investigate the tension between minimizing error
disparity across different population groups while maintaining calibrated
probability estimates. We show that calibration is compatible only with a
single error constraint (i.e. equal false-negatives rates across groups), and
show that any algorithm that satisfies this relaxation is no better than
randomizing a percentage of predictions for an existing classifier. These
unsettling findings, which extend and generalize existing results, are
empirically confirmed on several datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Role of 1-D finite size Heisenberg chain in increasing metal to insulator transition temperature in hole rich VO2,
Abstract: VO2 samples are grown with different oxygen concentrations leading to
different monoclinic, M1 and triclinic, T insulating phases which undergo a
first order metal to insulator transition (MIT) followed by a structural phase
transition (SPT) to rutile tetragonal phase. The metal insulator transition
temperature (Tc) was found to be increased with increasing native defects.
Vanadium vacancy (VV) is envisaged to create local strains in the lattice which
prevents twisting of the V-V dimers promoting metastable monoclinic, M2 and T
phases at intermediate temperatures. It is argued that MIT is driven by strong
electronic correlation. The low temperature insulating phase can be considered
as a collection of one-dimensional (1-D) half-filled band, which undergoes Mott
transition to 1-D infinitely long Heisenberg spin 1/2 chains leading to
structural distortion due to spin-phonon coupling. Presence of VV creates
localized holes (d0) in the nearest neighbor, thereby fragmenting the spin 1/2
chains at nanoscale, which in turn increase the Tc value more than that of an
infinitely long one. The Tc value scales inversely with the average size of
fragmented Heisenberg spin 1/2 chains following a critical exponent of 2/3,
which is exactly the same predicted theoretically for Heisenberg spin 1/2 chain
at nanoscale undergoing SPT (spin-Peierls transition). Thus, the observation of
MIT and SPT at the same time in VO2 can be explained from our phenomenological
model of reduced 1-D Heisenberg spin 1/2 chains. The reported increase
(decrease) in Tc value of VO2 by doping with metal having valency less (more)
than four, can also be understood easily with our unified model, for the first
time, considering finite size scaling of Heisenberg chains. | [
0,
1,
0,
0,
0,
0
] |
Title: A multilayer multiconfiguration time-dependent Hartree study of the nonequilibrium Anderson impurity model at zero temperature,
Abstract: Quantum transport is studied for the nonequilibrium Anderson impurity model
at zero temperature employing the multilayer multiconfiguration time-dependent
Hartree theory within the second quantization representation (ML-MCTDH-SQR) of
Fock space. To adress both linear and nonlinear conductance in the Kondo
regime, two new techniques of the ML-MCTDH-SQR simulation methodology are
introduced: (i) the use of correlated initial states, which is achieved by
imaginary time propagation of the overall Hamiltonian at zero voltage and (ii)
the adoption of the logarithmic discretization of the electronic continuum.
Employing the improved methodology, the signature of the Kondo effect is
analyzed. | [
0,
1,
0,
0,
0,
0
] |
Title: Semi-Lagrangian one-step methods for two classes of time-dependent partial differential systems,
Abstract: Semi-Lagrangian methods are numerical methods designed to find approximate
solutions to particular time-dependent partial differential equations (PDEs)
that describe the advection process. We propose semi-Lagrangian one-step
methods for numerically solving initial value problems for two general systems
of partial differential equations. Along the characteristic lines of the PDEs,
we use ordinary differential equation (ODE) numerical methods to solve the
PDEs. The main benefit of our methods is the efficient achievement of high
order local truncation error through the use of Runge-Kutta methods along the
characteristics. In addition, we investigate the numerical analysis of
semi-Lagrangian methods applied to systems of PDEs: stability, convergence, and
maximum error bounds. | [
0,
0,
1,
0,
0,
0
] |
Title: Efficient Nearest-Neighbor Search for Dynamical Systems with Nonholonomic Constraints,
Abstract: Nearest-neighbor search dominates the asymptotic complexity of sampling-based
motion planning algorithms and is often addressed with k-d tree data
structures. While it is generally believed that the expected complexity of
nearest-neighbor queries is $O(log(N))$ in the size of the tree, this paper
reveals that when a classic k-d tree approach is used with sub-Riemannian
metrics, the expected query complexity is in fact $\Theta(N^p \log(N))$ for a
number $p \in [0, 1)$ determined by the degree of nonholonomy of the system.
These metrics arise naturally in nonholonomic mechanical systems, including
classic wheeled robot models. To address this negative result, we propose novel
k-d tree build and query strategies tailored to sub-Riemannian metrics and
demonstrate significant improvements in the running time of nearest-neighbor
search queries. | [
1,
0,
0,
0,
0,
0
] |
Title: Primitivity, Uniform Minimality and State Complexity of Boolean Operations,
Abstract: A minimal deterministic finite automaton (DFA) is uniformly minimal if it
always remains minimal when the final state set is replaced by a non-empty
proper subset of the state set. We prove that a permutation DFA is uniformly
minimal if and only if its transition monoid is a primitive group. We use this
to study boolean operations on group languages, which are recognized by direct
products of permutation DFAs. A direct product cannot be uniformly minimal,
except in the trivial case where one of the DFAs in the product is a one-state
DFA. However, non-trivial direct products can satisfy a weaker condition we
call uniform boolean minimality, where only final state sets used to recognize
boolean operations are considered. We give sufficient conditions for a direct
product of two DFAs to be uniformly boolean minimal, which in turn gives
sufficient conditions for pairs of group languages to have maximal state
complexity under all binary boolean operations ("maximal boolean complexity").
In the case of permutation DFAs with one final state, we give necessary and
sufficient conditions for pairs of group languages to have maximal boolean
complexity. Our results demonstrate a connection between primitive groups and
automata with strong minimality properties. | [
1,
0,
1,
0,
0,
0
] |
Title: Summary of Topological Study of Chaotic CBC Mode of Operation,
Abstract: In cryptography, block ciphers are the most fundamental elements in many
symmetric-key encryption systems. The Cipher Block Chaining, denoted CBC,
presents one of the most famous mode of operation that uses a block cipher to
provide confidentiality or authenticity. In this research work, we intend to
summarize our results that have been detailed in our previous series of
articles. The goal of this series has been to obtain a complete topological
study of the CBC block cipher mode of operation after proving his chaotic
behavior according to the reputed definition of Devaney. | [
1,
1,
0,
0,
0,
0
] |
Title: MSO+nabla is undecidable,
Abstract: This paper is about an extension of monadic second-order logic over infinite
trees, which adds a quantifier that says "the set of branches \pi which satisfy
a formula \phi(\pi) has probability one". This logic was introduced by
Michalewski and Mio; we call it MSO+nabla following Shelah and Lehmann. The
logic MSO+nabla subsumes many qualitative probabilistic formalisms, including
qualitative probabilistic CTL, probabilistic LTL, or parity tree automata with
probabilistic acceptance conditions. We consider the decision problem: decide
if a sentence of MSO+nabla is true in the infinite binary tree? For sentences
from the weak variant of this logic (set quantifiers range only over finite
sets) the problem was known to be decidable, but the question for the full
logic remained open. In this paper we show that the problem for the full logic
MSO+nabla is undecidable. | [
1,
0,
0,
0,
0,
0
] |
Title: Computer-aided implant design for the restoration of cranial defects,
Abstract: Patient-specific cranial implants are important and necessary in the surgery
of cranial defect restoration. However, traditional methods of manual design of
cranial implants are complicated and time-consuming. Our purpose is to develop
a novel software named EasyCrania to design the cranial implants conveniently
and efficiently. The process can be divided into five steps, which are
mirroring model, clipping surface, surface fitting, the generation of the
initial implant and the generation of the final implant. The main concept of
our method is to use the geometry information of the mirrored model as the base
to generate the final implant. The comparative studies demonstrated that the
EasyCrania can improve the efficiency of cranial implant design significantly.
And, the intra- and inter-rater reliability of the software were stable, which
were 87.07+/-1.6% and 87.73+/-1.4% respectively. | [
1,
0,
0,
0,
0,
0
] |
Title: Dimension-free Information Concentration via Exp-Concavity,
Abstract: Information concentration of probability measures have important implications
in learning theory. Recently, it is discovered that the information content of
a log-concave distribution concentrates around their differential entropy,
albeit with an unpleasant dependence on the ambient dimension. In this work, we
prove that if the potentials of the log-concave distribution are exp-concave,
which is a central notion for fast rates in online and statistical learning,
then the concentration of information can be further improved to depend only on
the exp-concavity parameter, and hence, it can be dimension independent.
Central to our proof is a novel yet simple application of the variance
Brascamp-Lieb inequality. In the context of learning theory, our
concentration-of-information result immediately implies high-probability
results to many of the previous bounds that only hold in expectation. | [
0,
0,
0,
1,
0,
0
] |
Title: Transfer results for Frobenius extensions,
Abstract: We study Frobenius extensions which are free-filtered by a totally ordered,
finitely generated abelian group, and their free-graded counterparts. First we
show that the Frobenius property passes up from a free-graded extension to a
free-filtered extension, then also from a free-filtered extension to the
extension of their Rees algebras. Our main theorem states that, under some
natural hypotheses, a free-filtered extension of algebras is Frobenius if and
only if the associated graded extension is Frobenius. In the final section we
apply this theorem to provide new examples and non-examples of Frobenius
extensions. | [
0,
0,
1,
0,
0,
0
] |
Title: Recency-weighted Markovian inference,
Abstract: We describe a Markov latent state space (MLSS) model, where the latent state
distribution is a decaying mixture over multiple past states. We present a
simple sampling algorithm that allows to approximate such high-order MLSS with
fixed time and memory costs. | [
1,
0,
0,
1,
0,
0
] |
Title: A revisit on the compactness of commutators,
Abstract: A new characterization of CMO(R^n) is established by the local mean
oscillation. Some characterizations of iterated compact commutators on weighted
Lebesgue spaces are given, which are new even in the unweighted setting for the
first order commutators. | [
0,
0,
1,
0,
0,
0
] |
Title: Question Answering through Transfer Learning from Large Fine-grained Supervision Data,
Abstract: We show that the task of question answering (QA) can significantly benefit
from the transfer learning of models trained on a different large, fine-grained
QA dataset. We achieve the state of the art in two well-studied QA datasets,
WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique
from SQuAD. For WikiQA, our model outperforms the previous best model by more
than 8%. We demonstrate that finer supervision provides better guidance for
learning lexical and syntactic information than coarser supervision, through
quantitative results and visual analysis. We also show that a similar transfer
learning procedure achieves the state of the art on an entailment task. | [
1,
0,
0,
0,
0,
0
] |
Title: On mesoprimary decomposition of monoid congruences,
Abstract: We prove two main results concerning mesoprimary decomposition of monoid
congruences, as introduced by Kahle and Miller. First, we identify which
associated prime congruences appear in every mesoprimary decomposition, thereby
completing the theory of mesoprimary decomposition of monoid congruences as a
more faithful analog of primary decomposition. Second, we answer a question
posed by Kahle and Miller by characterizing which finite posets arise as the
set of associated prime congruences of monoid congruences. | [
0,
0,
1,
0,
0,
0
] |
Title: Assessing the state of e-Readiness for Small and Medium Companies in Mexico: a Proposed Taxonomy and Adoption Model,
Abstract: Emerging economies frequently show a large component of their Gross Domestic
Product to be dependant on the economic activity of small and medium
enterprises. Nevertheless, e-business solutions are more likely designed for
large companies. SMEs seem to follow a classical family-based management, used
to traditional activities, rather than seeking new ways of adding value to
their business strategy. Thus, a large portion of a nations economy may be at
disadvantage for competition. This paper aims at assessing the state of
e-business readiness of Mexican SMEs based on already published e-business
evolution models and by means of a survey research design. Data is being
collected in three cities with differing sizes and infrastructure conditions.
Statistical results are expected to be presented. A second part of this
research aims at applying classical adoption models to suggest potential causal
relationships, as well as more suitable recommendations for development. | [
0,
0,
0,
0,
0,
1
] |
Title: Data Augmentation for Robust Keyword Spotting under Playback Interference,
Abstract: Accurate on-device keyword spotting (KWS) with low false accept and false
reject rate is crucial to customer experience for far-field voice control of
conversational agents. It is particularly challenging to maintain low false
reject rate in real world conditions where there is (a) ambient noise from
external sources such as TV, household appliances, or other speech that is not
directed at the device (b) imperfect cancellation of the audio playback from
the device, resulting in residual echo, after being processed by the Acoustic
Echo Cancellation (AEC) system. In this paper, we propose a data augmentation
strategy to improve keyword spotting performance under these challenging
conditions. The training set audio is artificially corrupted by mixing in music
and TV/movie audio, at different signal to interference ratios. Our results
show that we get around 30-45% relative reduction in false reject rates, at a
range of false alarm rates, under audio playback from such devices. | [
0,
0,
0,
1,
0,
0
] |
Title: A Canonical-based NPN Boolean Matching Algorithm Utilizing Boolean Difference and Cofactor Signature,
Abstract: This paper presents a new compact canonical-based algorithm to solve the
problem of single-output completely specified NPN Boolean matching. We propose
a new signature vector Boolean difference and cofactor (DC) signature vector.
Our algorithm utilizes the Boolean difference, cofactor signature and symmetry
properties to search for canonical transformations. The use of symmetry and
Boolean difference notably reduces the search space and speeds up the Boolean
matching process compared to the algorithm proposed in [1]. We tested our
algorithm on a large number of circuits. The experimental results showed that
the average runtime of our algorithm 37% higher and its average search space
67% smaller compared to [1] when tested on general circuits. | [
1,
0,
0,
0,
0,
0
] |
Title: Cross ratios on boundaries of symmetric spaces and Euclidean buildings,
Abstract: We generalize the natural cross ratio on the ideal boundary of a rank one
symmetric spaces, or even $\mathrm{CAT}(-1)$ space, to higher rank symmetric
spaces and (non-locally compact) Euclidean buildings - we obtain vector valued
cross ratios defined on simplices of the building at infinity. We show several
properties of those cross ratios; for example that (under some restrictions)
periods of hyperbolic isometries give back the translation vector. In addition,
we show that cross ratio preserving maps on the chamber set are induced by
isometries and vice versa - motivating that the cross ratios bring the geometry
of the symmetric space/Euclidean building to the boundary. | [
0,
0,
1,
0,
0,
0
] |
Title: Heterogeneous inputs to central pattern generators can shape insect gaits,
Abstract: In our previous work, we studied an interconnected bursting neuron model for
insect locomotion, and its corresponding phase oscillator model, which at high
speed can generate stable tripod gaits with three legs off the ground
simultaneously in swing, and at low speed can generate stable tetrapod gaits
with two legs off the ground simultaneously in swing. However, at low speed
several other stable locomotion patterns, that are not typically observed as
insect gaits, may coexist. In the present paper, by adding heterogeneous
external input to each oscillator, we modify the bursting neuron model so that
its corresponding phase oscillator model produces only one stable gait at each
speed, specifically: a unique stable tetrapod gait at low speed, a unique
stable tripod gait at high speed, and a unique branch of stable transition
gaits connecting them. This suggests that control signals originating in the
brain and central nervous system can modify gait patterns. | [
0,
0,
0,
0,
1,
0
] |
Title: Pinning down the mass of Kepler-10c: the importance of sampling and model comparison,
Abstract: Initial RV characterisation of the enigmatic planet Kepler-10c suggested a
mass of $\sim17$ M$_\oplus$, which was remarkably high for a planet with radius
$2.32$ R$_\oplus$; further observations and subsequent analysis hinted at a
(possibly much) lower mass, but masses derived using RVs from two different
spectrographs (HARPS-N and HIRES) were incompatible at a $3\sigma$-level. We
demonstrate here how such mass discrepancies may readily arise from sub-optimal
sampling and/or neglecting to model even a single coherent signal (stellar,
planetary, or otherwise) that may be present in RVs. We then present a
plausible resolution of the mass discrepancy, and ultimately characterise
Kepler-10c as having mass $7.37_{-1.19}^{+1.32}$ M$_\oplus$, and mean density
$3.14^{+0.63}_{-0.55}$ g cm$^{-3}$. | [
0,
1,
0,
0,
0,
0
] |
Title: Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy,
Abstract: Building effective, enjoyable, and safe autonomous vehicles is a lot harder
than has historically been considered. The reason is that, simply put, an
autonomous vehicle must interact with human beings. This interaction is not a
robotics problem nor a machine learning problem nor a psychology problem nor an
economics problem nor a policy problem. It is all of these problems put into
one. It challenges our assumptions about the limitations of human beings at
their worst and the capabilities of artificial intelligence systems at their
best. This work proposes a set of principles for designing and building
autonomous vehicles in a human-centered way that does not run away from the
complexity of human nature but instead embraces it. We describe our development
of the Human-Centered Autonomous Vehicle (HCAV) as an illustrative case study
of implementing these principles in practice. | [
1,
0,
0,
0,
0,
0
] |
Title: A Relaxation-based Network Decomposition Algorithm for Parallel Transient Stability Simulation with Improved Convergence,
Abstract: Transient stability simulation of a large-scale and interconnected electric
power system involves solving a large set of differential algebraic equations
(DAEs) at every simulation time-step. With the ever-growing size and complexity
of power grids, dynamic simulation becomes more time-consuming and
computationally difficult using conventional sequential simulation techniques.
To cope with this challenge, this paper aims to develop a fully distributed
approach intended for implementation on High Performance Computer (HPC)
clusters. A novel, relaxation-based domain decomposition algorithm known as
Parallel-General-Norton with Multiple-port Equivalent (PGNME) is proposed as
the core technique of a two-stage decomposition approach to divide the overall
dynamic simulation problem into a set of subproblems that can be solved
concurrently to exploit parallelism and scalability. While the convergence
property has traditionally been a concern for relaxation-based decomposition,
an estimation mechanism based on multiple-port network equivalent is adopted as
the preconditioner to enhance the convergence of the proposed algorithm. The
proposed algorithm is illustrated using rigorous mathematics and validated both
in terms of speed-up and capability. Moreover, a complexity analysis is
performed to support the observation that PGNME scales well when the size of
the subproblems are sufficiently large. | [
1,
0,
0,
0,
0,
0
] |
Title: Random group cobordisms of rank 7/4,
Abstract: We construct a model of random groups of rank 7/4, and show that in this
model the random group has the exponential mesoscopic rank property. | [
0,
0,
1,
0,
0,
0
] |
Title: Transport properties across the many-body localization transition in quasiperiodic and random systems,
Abstract: We theoretically study transport properties in one-dimensional interacting
quasiperiodic systems at infinite temperature. We compare and contrast the
dynamical transport properties across the many-body localization (MBL)
transition in quasiperiodic and random models. Using exact diagonalization we
compute the optical conductivity $\sigma(\omega)$ and the return probability
$R(\tau)$ and study their average low-frequency and long-time power-law
behavior, respectively. We show that the low-energy transport dynamics is
markedly distinct in both the thermal and MBL phases in quasiperiodic and
random models and find that the diffusive and MBL regimes of the quasiperiodic
model are more robust than those in the random system. Using the distribution
of the DC conductivity, we quantify the contribution of sample-to-sample and
state-to-state fluctuations of $\sigma(\omega)$ across the MBL transition. We
find that the activated dynamical scaling ansatz works poorly in the
quasiperiodic model but holds in the random model with an estimated activation
exponent $\psi\approx 0.9$. We argue that near the MBL transition in
quasiperiodic systems, critical eigenstates give rise to a subdiffusive
crossover regime on finite-size systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net,
Abstract: The lasso and elastic net linear regression models impose a
double-exponential prior distribution on the model parameters to achieve
regression shrinkage and variable selection, allowing the inference of robust
models from large data sets. However, there has been limited success in
deriving estimates for the full posterior distribution of regression
coefficients in these models, due to a need to evaluate analytically
intractable partition function integrals. Here, the Fourier transform is used
to express these integrals as complex-valued oscillatory integrals over
"regression frequencies". This results in an analytic expansion and stationary
phase approximation for the partition functions of the Bayesian lasso and
elastic net, where the non-differentiability of the double-exponential prior
has so far eluded such an approach. Use of this approximation leads to highly
accurate numerical estimates for the expectation values and marginal posterior
distributions of the regression coefficients, and allows for Bayesian inference
of much higher dimensional models than previously possible. | [
0,
0,
1,
1,
0,
0
] |
Title: Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem,
Abstract: One of the key challenges for operations researchers solving real-world
problems is designing and implementing high-quality heuristics to guide their
search procedures. In the past, machine learning techniques have failed to play
a major role in operations research approaches, especially in terms of guiding
branching and pruning decisions. We integrate deep neural networks into a
heuristic tree search procedure to decide which branch to choose next and to
estimate a bound for pruning the search tree of an optimization problem. We
call our approach Deep Learning assisted heuristic Tree Search (DLTS) and apply
it to a well-known problem from the container terminals literature, the
container pre-marshalling problem (CPMP). Our approach is able to learn
heuristics customized to the CPMP solely through analyzing the solutions to
CPMP instances, and applies this knowledge within a heuristic tree search to
produce the highest quality heuristic solutions to the CPMP to date. | [
1,
0,
0,
0,
0,
0
] |
Title: Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates,
Abstract: The optimization of algorithm (hyper-)parameters is crucial for achieving
peak performance across a wide range of domains, ranging from deep neural
networks to solvers for hard combinatorial problems. The resulting algorithm
configuration (AC) problem has attracted much attention from the machine
learning community. However, the proper evaluation of new AC procedures is
hindered by two key hurdles. First, AC benchmarks are hard to set up. Second
and even more significantly, they are computationally expensive: a single run
of an AC procedure involves many costly runs of the target algorithm whose
performance is to be optimized in a given AC benchmark scenario. One common
workaround is to optimize cheap-to-evaluate artificial benchmark functions
(e.g., Branin) instead of actual algorithms; however, these have different
properties than realistic AC problems. Here, we propose an alternative
benchmarking approach that is similarly cheap to evaluate but much closer to
the original AC problem: replacing expensive benchmarks by surrogate benchmarks
constructed from AC benchmarks. These surrogate benchmarks approximate the
response surface corresponding to true target algorithm performance using a
regression model, and the original and surrogate benchmark share the same
(hyper-)parameter space. In our experiments, we construct and evaluate
surrogate benchmarks for hyperparameter optimization as well as for AC problems
that involve performance optimization of solvers for hard combinatorial
problems, drawing training data from the runs of existing AC procedures. We
show that our surrogate benchmarks capture overall important characteristics of
the AC scenarios, such as high- and low-performing regions, from which they
were derived, while being much easier to use and orders of magnitude cheaper to
evaluate. | [
1,
0,
0,
1,
0,
0
] |
Title: An open-source platform to study uniaxial stress effects on nanoscale devices,
Abstract: We present an automatic measurement platform that enables the
characterization of nanodevices by electrical transport and optical
spectroscopy as a function of uniaxial stress. We provide insights into and
detailed descriptions of the mechanical device, the substrate design and
fabrication, and the instrument control software, which is provided under
open-source license. The capability of the platform is demonstrated by
characterizing the piezo-resistance of an InAs nanowire device using a
combination of electrical transport and Raman spectroscopy. The advantages of
this measurement platform are highlighted by comparison with state-of-the-art
piezo-resistance measurements in InAs nanowires. We envision that the
systematic application of this methodology will provide new insights into the
physics of nanoscale devices and novel materials for electronics, and thus
contribute to the assessment of the potential of strain as a technology booster
for nanoscale electronics. | [
0,
1,
0,
0,
0,
0
] |
Title: Parametrised second-order complexity theory with applications to the study of interval computation,
Abstract: We extend the framework for complexity of operators in analysis devised by
Kawamura and Cook (2012) to allow for the treatment of a wider class of
representations. The main novelty is to endow represented spaces of interest
with an additional function on names, called a parameter, which measures the
complexity of a given name. This parameter generalises the size function which
is usually used in second-order complexity theory and therefore also central to
the framework of Kawamura and Cook. The complexity of an algorithm is measured
in terms of its running time as a second-order function in the parameter, as
well as in terms of how much it increases the complexity of a given name, as
measured by the parameters on the input and output side.
As an application we develop a rigorous computational complexity theory for
interval computation. In the framework of Kawamura and Cook the representation
of real numbers based on nested interval enclosures does not yield a reasonable
complexity theory. In our new framework this representation is polytime
equivalent to the usual Cauchy representation based on dyadic rational
approximation. By contrast, the representation of continuous real functions
based on interval enclosures is strictly smaller in the polytime reducibility
lattice than the usual representation, which encodes a modulus of continuity.
Furthermore, the function space representation based on interval enclosures is
optimal in the sense that it contains the minimal amount of information amongst
those representations which render evaluation polytime computable. | [
1,
0,
0,
0,
0,
0
] |
Title: VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning,
Abstract: Deep generative models provide powerful tools for distributions over
complicated manifolds, such as those of natural images. But many of these
methods, including generative adversarial networks (GANs), can be difficult to
train, in part because they are prone to mode collapse, which means that they
characterize only a few modes of the true distribution. To address this, we
introduce VEEGAN, which features a reconstructor network, reversing the action
of the generator by mapping from data to noise. Our training objective retains
the original asymptotic consistency guarantee of GANs, and can be interpreted
as a novel autoencoder loss over the noise. In sharp contrast to a traditional
autoencoder over data points, VEEGAN does not require specifying a loss
function over the data, but rather only over the representations, which are
standard normal by assumption. On an extensive set of synthetic and real world
image datasets, VEEGAN indeed resists mode collapsing to a far greater extent
than other recent GAN variants, and produces more realistic samples. | [
0,
0,
0,
1,
0,
0
] |
Title: Adaptive posterior contraction rates for the horseshoe,
Abstract: We investigate the frequentist properties of Bayesian procedures for
estimation based on the horseshoe prior in the sparse multivariate normal means
model. Previous theoretical results assumed that the sparsity level, that is,
the number of signals, was known. We drop this assumption and characterize the
behavior of the maximum marginal likelihood estimator (MMLE) of a key parameter
of the horseshoe prior. We prove that the MMLE is an effective estimator of the
sparsity level, in the sense that it leads to (near) minimax optimal estimation
of the underlying mean vector generating the data. Besides this empirical Bayes
procedure, we consider the hierarchical Bayes method of putting a prior on the
unknown sparsity level as well. We show that both Bayesian techniques lead to
rate-adaptive optimal posterior contraction, which implies that the horseshoe
posterior is a good candidate for generating rate-adaptive credible sets. | [
0,
0,
1,
1,
0,
0
] |
Title: Investigating Enactive Learning for Autonomous Intelligent Agents,
Abstract: The enactive approach to cognition is typically proposed as a viable
alternative to traditional cognitive science. Enactive cognition displaces the
explanatory focus from the internal representations of the agent to the direct
sensorimotor interaction with its environment. In this paper, we investigate
enactive learning through means of artificial agent simulations. We compare the
performances of the enactive agent to an agent operating on classical
reinforcement learning in foraging tasks within maze environments. The
characteristics of the agents are analysed in terms of the accessibility of the
environmental states, goals, and exploration/exploitation tradeoffs. We confirm
that the enactive agent can successfully interact with its environment and
learn to avoid unfavourable interactions using intrinsically defined goals. The
performance of the enactive agent is shown to be limited by the number of
affordable actions. | [
1,
0,
0,
0,
0,
0
] |
Title: Predicting Pulsar Scintillation from Refractive Plasma Sheets,
Abstract: The dynamic and secondary spectra of many pulsars show evidence for
long-lived, aligned images of the pulsar that are stationary on a thin
scattering sheet. One explanation for this phenomenon considers the effects of
wave crests along sheets in the ionized interstellar medium, such as those due
to Alfvén waves propagating along current sheets. If these sheets are closely
aligned to our line-of-sight to the pulsar, high bending angles arise at the
wave crests and a selection effect causes alignment of images produced at
different crests, similar to grazing reflection off of a lake. Using geometric
optics, we develop a simple parameterized model of these corrugated sheets that
can be constrained with a single observation and that makes observable
predictions for variations in the scintillation of the pulsar over time and
frequency. This model reveals qualitative differences between lensing from
overdense and underdense corrugated sheets: Only if the sheet is overdense
compared to the surrounding interstellar medium can the lensed images be
brighter than the line-of-sight image to the pulsar, and the faint lensed
images are closer to the pulsar at higher frequencies if the sheet is
underdense, but at lower frequencies if the sheet is overdense. | [
0,
1,
0,
0,
0,
0
] |
Title: Quantum Monte Carlo with variable spins: fixed-phase and fixed-node approximations,
Abstract: We study several aspects of the recently introduced fixed-phase spin-orbit
diffusion Monte Carlo (FPSODMC) method, in particular, its relation to the
fixed-node method and its potential use as a general approach for electronic
structure calculations. We illustrate constructions of spinor-based wave
functions with the full space-spin symmetry without assigning up or down spin
labels to particular electrons, effectively "complexifying" even ordinary
real-valued wave functions. Interestingly, with proper choice of the simulation
parameters and spin variables, such fixed-phase calculations enable one to
reach also the fixed-node limit. The fixed-phase solution provides a
straightforward interpretation as the lowest bosonic state in a given effective
potential generated by the many-body approximate phase. In addition, the
divergences present at real wave function nodes are smoothed out to lower
dimensionality, decreasing thus the variation of sampled quantities and making
the sampling also more straightforward. We illustrate some of these properties
on calculations of selected first-row systems that recover the fixed-node
results with quantitatively similar levels of the corresponding biases. At the
same time, the fixed-phase approach opens new possibilities for more general
trial wave functions with further opportunities for increasing accuracy in
practical calculations. | [
0,
1,
0,
0,
0,
0
] |
Title: A Tutorial on Canonical Correlation Methods,
Abstract: Canonical correlation analysis is a family of multivariate statistical
methods for the analysis of paired sets of variables. Since its proposition,
canonical correlation analysis has for instance been extended to extract
relations between two sets of variables when the sample size is insufficient in
relation to the data dimensionality, when the relations have been considered to
be non-linear, and when the dimensionality is too large for human
interpretation. This tutorial explains the theory of canonical correlation
analysis including its regularised, kernel, and sparse variants. Additionally,
the deep and Bayesian CCA extensions are briefly reviewed. Together with the
numerical examples, this overview provides a coherent compendium on the
applicability of the variants of canonical correlation analysis. By bringing
together techniques for solving the optimisation problems, evaluating the
statistical significance and generalisability of the canonical correlation
model, and interpreting the relations, we hope that this article can serve as a
hands-on tool for applying canonical correlation methods in data analysis. | [
1,
0,
0,
1,
0,
0
] |
Title: A note on $p^λ$-convex set in a complete Riemannian manifold,
Abstract: In this paper we have generalized the notion of $\lambda$-radial contraction
in complete Riemannian manifold and developed the concept of $p^\lambda$-convex
function. We have also given a counter example proving the fact that in general
$\lambda$-radial contraction of a geodesic is not necessarily a geodesic. We
have also deduced some relations between geodesic convex sets and
$p^\lambda$-convex sets and showed that under certain conditions they are
equivalent. | [
0,
0,
1,
0,
0,
0
] |
Title: Transpiling Programmable Computable Functions to Answer Set Programs,
Abstract: Programming Computable Functions (PCF) is a simplified programming language
which provides the theoretical basis of modern functional programming
languages. Answer set programming (ASP) is a programming paradigm focused on
solving search problems. In this paper we provide a translation from PCF to
ASP. Using this translation it becomes possible to specify search problems
using PCF. | [
1,
0,
0,
0,
0,
0
] |
Title: Scattering polarization of the $d$-states of ions and solar magnetic field: Effects of isotropic collisions,
Abstract: Analysis of solar magnetic fields using observations as well as theoretical
interpretations of the scattering polarization is commonly designated as a high
priority area of the solar research. The interpretation of the observed
polarization raises a serious theoretical challenge to the researchers involved
in this field. In fact, realistic interpretations need detailed investigations
of the depolarizing role of isotropic collisions with neutral hydrogen. The
goal of this paper is to determine new relationships which allow the
calculation of any collisional rates of the d-levels of ions by simply
determining the value of n^* and $E_p$ without the need of determining the
interaction potentials and treating the dynamics of collisions. The
determination of n^* and E_p is easy and based on atomic data usually available
online. Accurate collisional rates allow a reliable diagnostics of solar
magnetic fields. In this work we applied our collisional FORTRAN code to a
large number of cases involving complex and simple ions. After that, the
results are utilized and injected in a genetic programming code developed with
C-langugae in order to infer original relationships which will be of great help
to solar applications. We discussed the accurarcy of our collisional rates in
the cases of polarized complex atoms and atoms with hyperfine structure. The
relationships are expressed on the tensorial basis and we explain how to
include their contributions in the master equation giving the variation of the
density matrix elements. As a test, we compared the results obtained through
the general relationships provided in this work with the results obtained
directly by running our code of collisions. These comparisons show a percentage
of error of about 10% in the average value. | [
0,
1,
0,
0,
0,
0
] |
Title: Persistence Codebooks for Topological Data Analysis,
Abstract: Topological data analysis, such as persistent homology has shown beneficial
properties for machine learning in many tasks. Topological representations,
such as the persistence diagram (PD), however, have a complex structure
(multiset of intervals) which makes it difficult to combine with typical
machine learning workflows. We present novel compact fixed-size vectorial
representations of PDs based on clustering and bag of words encodings that cope
well with the inherent sparsity of PDs. Our novel representations outperform
state-of-the-art approaches from topological data analysis and are
computationally more efficient. | [
0,
0,
0,
1,
0,
0
] |
Title: Tonic activation of extrasynaptic NMDA receptors decreases intrinsic excitability and promotes bistability in a model of neuronal activity,
Abstract: NMDA receptors (NMDA-R) typically contribute to excitatory synaptic
transmission in the central nervous system. While calcium influx through NMDA-R
plays a critical role in synaptic plasticity, indirect experimental evidence
also exists demonstrating actions of NMDAR-mediated calcium influx on neuronal
excitability through the activation of calcium-activated potassium channels.
But, so far, this mechanism has not been studied theoretically. Our theoretical
model provide a simple description of neuronal electrical activity including
the tonic activity of NMDA receptors and a cytosolic calcium compartment. We
show that calcium influx through NMDA-R can directly be coupled to activation
of calcium-activated potassium channels providing an overall inhibitory effect
on neuronal excitability. Furthermore, the presence of tonic NMDA-R activity
promotes bistability in electrical activity by dramatically increasing the
stimulus interval where both a stable steady state and repetitive firing can
exist. This results could provide an intrinsic mechanism for the constitution
of memory traces in neuronal circuits. They also shed light on the way by which
beta-amyloids can decrease neuronal activity when interfering with NMDA-R in
Alzheimer's disease. | [
0,
0,
0,
0,
1,
0
] |
Title: Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-Linear Chaining Extended,
Abstract: Aligning sequencing reads on graph representations of genomes is an important
ingredient of pan-genomics. Such approaches typically find a set of local
anchors that indicate plausible matches between substrings of a read to
subpaths of the graph. These anchor matches are then combined to form a
(semi-local) alignment of the complete read on a subpath. Co-linear chaining is
an algorithmically rigorous approach to combine the anchors. It is a well-known
approach for the case of two sequences as inputs. Here we extend the approach
so that one of the inputs can be a directed acyclic graph (DAGs), e.g. a
splicing graph in transcriptomics or a variant graph in pan-genomics.
This extension to DAGs turns out to have a tight connection to the minimum
path cover problem, asking for a minimum-cardinality set of paths that cover
all the nodes of a DAG. We study the case when the size $k$ of a minimum path
cover is small, which is often the case in practice. First, we propose an
algorithm for finding a minimum path cover of a DAG $(V,E)$ in $O(k|E|\log|V|)$
time, improving all known time-bounds when $k$ is small and the DAG is not too
dense. Second, we introduce a general technique for extending dynamic
programming (DP) algorithms from sequences to DAGs. This is enabled by our
minimum path cover algorithm, and works by mimicking the DP algorithm for
sequences on each path of the minimum path cover. This technique generally
produces algorithms that are slower than their counterparts on sequences only
by a factor $k$. Our technique can be applied, for example, to the classical
longest increasing subsequence and longest common subsequence problems,
extended to labeled DAGs. Finally, we apply this technique to the co-linear
chaining problem. We also implemented the new co-linear chaining approach.
Experiments on splicing graphs show that the new method is efficient also in
practice. | [
1,
0,
0,
0,
0,
0
] |
Title: Steady States of Rotating Stars and Galaxies,
Abstract: A rotating continuum of particles attracted to each other by gravity may be
modeled by the Euler-Poisson system. The existence of solutions is a very
classical problem. Here it is proven that a curve of solutions exists,
parametrized by the rotation speed, with a fixed mass independent of the speed.
The rotation is allowed to vary with the distance to the axis. A special case
is when the equation of state is $p=\rho^\gamma,\ 6/5<\gamma<2,\ \gamma\ne4/3$,
in contrast to previous variational methods which have required $4/3 < \gamma$.
The continuum of particles may alternatively be modeled microscopically by
the Vlasov-Poisson system. The kinetic density is a prescribed function. We
prove an analogous theorem asserting the existence of a curve of solutions with
constant mass. In this model the whole range $(6/5,2)$ is allowed, including
$\gamma=4/3$. | [
0,
0,
1,
0,
0,
0
] |
Title: Critical properties of the contact process with quenched dilution,
Abstract: We have studied the critical properties of the contact process on a square
lattice with quenched site dilution by Monte Carlo simulations. This was
achieved by generating in advance the percolating cluster, through the use of
an appropriate epidemic model, and then by the simulation of the contact
process on the top of the percolating cluster. The dynamic critical exponents
were calculated by assuming an activated scaling relation and the static
exponents by the usual power law behavior. Our results are in agreement with
the prediction that the quenched diluted contact process belongs to the
universality class of the random transverse-field Ising model. We have also
analyzed the model and determined the phase diagram by the use of a mean-field
theory that takes into account the correlation between neighboring sites. | [
0,
1,
0,
0,
0,
0
] |
Title: On Data-Dependent Random Features for Improved Generalization in Supervised Learning,
Abstract: The randomized-feature approach has been successfully employed in large-scale
kernel approximation and supervised learning. The distribution from which the
random features are drawn impacts the number of features required to
efficiently perform a learning task. Recently, it has been shown that employing
data-dependent randomization improves the performance in terms of the required
number of random features. In this paper, we are concerned with the
randomized-feature approach in supervised learning for good generalizability.
We propose the Energy-based Exploration of Random Features (EERF) algorithm
based on a data-dependent score function that explores the set of possible
features and exploits the promising regions. We prove that the proposed score
function with high probability recovers the spectrum of the best fit within the
model class. Our empirical results on several benchmark datasets further verify
that our method requires smaller number of random features to achieve a certain
generalization error compared to the state-of-the-art while introducing
negligible pre-processing overhead. EERF can be implemented in a few lines of
code and requires no additional tuning parameters. | [
1,
0,
0,
1,
0,
0
] |
Title: Anomalous electron spectrum and its relation to peak structure of electron scattering rate in cuprate superconductors,
Abstract: The recent discovery of a direct link between the sharp peak in the electron
quasiparticle scattering rate of cuprate superconductors and the well-known
peak-dip-hump structure in the electron quasiparticle excitation spectrum is
calling for an explanation. Within the framework of the kinetic-energy driven
superconducting mechanism, the complicated line-shape in the electron
quasiparticle excitation spectrum of cuprate superconductors is investigated.
It is shown that the interaction between electrons by the exchange of spin
excitations generates a notable peak structure in the electron quasiparticle
scattering rate around the antinodal and nodal regions. However, this peak
structure disappears at the hot spots, which leads to that the striking
peak-dip-hump structure is developed around the antinodal and nodal regions,
and vanishes at the hot spots. The theory also confirms that the sharp peak
observed in the electron quasiparticle scattering rate is directly responsible
for the remarkable peak-dip-hump structure in the electron quasiparticle
excitation spectrum of cuprate superconductors. | [
0,
1,
0,
0,
0,
0
] |
Title: A comparison theorem for MW-motivic cohomology,
Abstract: We prove that for a finitely generated field over an infinite perfect field
k, and for any integer n, the (n,n)-th MW-motivic cohomology group identifies
with the n-th Milnor-Witt K-theory group of that field | [
0,
0,
1,
0,
0,
0
] |
Title: Wiener Filtering for Passive Linear Quantum Systems,
Abstract: This paper considers a version of the Wiener filtering problem for
equalization of passive quantum linear quantum systems. We demonstrate that
taking into consideration the quantum nature of the signals involved leads to
features typically not encountered in classical equalization problems. Most
significantly, finding a mean-square optimal quantum equalizing filter amounts
to solving a nonconvex constrained optimization problem. We discuss two
approaches to solving this problem, both involving a relaxation of the
constraint. In both cases, unlike classical equalization, there is a threshold
on the variance of the noise below which an improvement of the mean-square
error cannot be guaranteed. | [
1,
0,
0,
0,
0,
0
] |
Title: Density Estimation with Contaminated Data: Minimax Rates and Theory of Adaptation,
Abstract: This paper studies density estimation under pointwise loss in the setting of
contamination model. The goal is to estimate $f(x_0)$ at some
$x_0\in\mathbb{R}$ with i.i.d. observations, $$ X_1,\dots,X_n\sim
(1-\epsilon)f+\epsilon g, $$ where $g$ stands for a contamination distribution.
In the context of multiple testing, this can be interpreted as estimating the
null density at a point. We carefully study the effect of contamination on
estimation through the following model indices: contamination proportion
$\epsilon$, smoothness of target density $\beta_0$, smoothness of contamination
density $\beta_1$, and level of contamination $m$ at the point to be estimated,
i.e. $g(x_0)\leq m$. It is shown that the minimax rate with respect to the
squared error loss is of order $$
[n^{-\frac{2\beta_0}{2\beta_0+1}}]\vee[\epsilon^2(1\wedge
m)^2]\vee[n^{-\frac{2\beta_1}{2\beta_1+1}}\epsilon^{\frac{2}{2\beta_1+1}}], $$
which characterizes the exact influence of contamination on the difficulty of
the problem. We then establish the minimal cost of adaptation to contamination
proportion, to smoothness and to both of the numbers. It is shown that some
small price needs to be paid for adaptation in any of the three cases.
Variations of Lepski's method are considered to achieve optimal adaptation.
The problem is also studied when there is no smoothness assumption on the
contamination distribution. This setting that allows for an arbitrary
contamination distribution is recognized as Huber's $\epsilon$-contamination
model. The minimax rate is shown to be $$
[n^{-\frac{2\beta_0}{2\beta_0+1}}]\vee [\epsilon^{\frac{2\beta_0}{\beta_0+1}}].
$$ The adaptation theory is also different from the smooth contamination case.
While adaptation to either contamination proportion or smoothness only costs a
logarithmic factor, adaptation to both numbers is proved to be impossible. | [
0,
0,
1,
1,
0,
0
] |
Title: Composable Deep Reinforcement Learning for Robotic Manipulation,
Abstract: Model-free deep reinforcement learning has been shown to exhibit good
performance in domains ranging from video games to simulated robotic
manipulation and locomotion. However, model-free methods are known to perform
poorly when the interaction time with the environment is limited, as is the
case for most real-world robotic tasks. In this paper, we study how maximum
entropy policies trained using soft Q-learning can be applied to real-world
robotic manipulation. The application of this method to real-world manipulation
is facilitated by two important features of soft Q-learning. First, soft
Q-learning can learn multimodal exploration strategies by learning policies
represented by expressive energy-based models. Second, we show that policies
learned with soft Q-learning can be composed to create new policies, and that
the optimality of the resulting policy can be bounded in terms of the
divergence between the composed policies. This compositionality provides an
especially valuable tool for real-world manipulation, where constructing new
policies by composing existing skills can provide a large gain in efficiency
over training from scratch. Our experimental evaluation demonstrates that soft
Q-learning is substantially more sample efficient than prior model-free deep
reinforcement learning methods, and that compositionality can be performed for
both simulated and real-world tasks. | [
1,
0,
0,
1,
0,
0
] |
Title: Stimulated Raman Scattering Imposes Fundamental Limits to the Duration and Bandwidth of Temporal Cavity Solitons,
Abstract: Temporal cavity solitons (CS) are optical pulses that can persist in passive
resonators, and they play a key role in the generation of coherent
microresonator frequency combs. In resonators made of amorphous materials, such
as fused silica, they can exhibit a spectral red-shift due to stimulated Raman
scattering. Here we show that this Raman-induced self-frequency-shift imposes a
fundamental limit on the duration and bandwidth of temporal CSs. Specifically,
we theoretically predict that stimulated Raman scattering introduces a
previously unidentified Hopf bifurcation that leads to destabilization of CSs
at large pump-cavity detunings, limiting the range of detunings over which they
can exist. We have confirmed our theoretical predictions by performing
extensive experiments in several different synchronously-driven fiber ring
resonators, obtaining results in excellent agreement with numerical
simulations. Our results could have significant implications for the future
design of Kerr frequency comb systems based on amorphous microresonators. | [
0,
1,
0,
0,
0,
0
] |
Title: A Unifying Framework for Convergence Analysis of Approximate Newton Methods,
Abstract: Many machine learning models are reformulated as optimization problems. Thus,
it is important to solve a large-scale optimization problem in big data
applications. Recently, subsampled Newton methods have emerged to attract much
attention for optimization due to their efficiency at each iteration, rectified
a weakness in the ordinary Newton method of suffering a high cost in each
iteration while commanding a high convergence rate. Other efficient stochastic
second order methods are also proposed. However, the convergence properties of
these methods are still not well understood. There are also several important
gaps between the current convergence theory and the performance in real
applications. In this paper, we aim to fill these gaps. We propose a unifying
framework to analyze local convergence properties of second order methods.
Based on this framework, our theoretical analysis matches the performance in
real applications. | [
1,
0,
0,
0,
0,
0
] |
Title: A Newman property for BLD-mappings,
Abstract: We define a Newman property for BLD-mappings and study its connections to the
porosity of the branch set in the setting of generalized manifolds equipped
with complete path metrics. | [
0,
0,
1,
0,
0,
0
] |
Title: Fast and Accurate Time Series Classification with WEASEL,
Abstract: Time series (TS) occur in many scientific and commercial applications,
ranging from earth surveillance to industry automation to the smart grids. An
important type of TS analysis is classification, which can, for instance,
improve energy load forecasting in smart grids by detecting the types of
electronic devices based on their energy consumption profiles recorded by
automatic sensors. Such sensor-driven applications are very often characterized
by (a) very long TS and (b) very large TS datasets needing classification.
However, current methods to time series classification (TSC) cannot cope with
such data volumes at acceptable accuracy; they are either scalable but offer
only inferior classification quality, or they achieve state-of-the-art
classification quality but cannot scale to large data volumes.
In this paper, we present WEASEL (Word ExtrAction for time SEries
cLassification), a novel TSC method which is both scalable and accurate. Like
other state-of-the-art TSC methods, WEASEL transforms time series into feature
vectors, using a sliding-window approach, which are then analyzed through a
machine learning classifier. The novelty of WEASEL lies in its specific method
for deriving features, resulting in a much smaller yet much more discriminative
feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more
accurate than the best current non-ensemble algorithms at orders-of-magnitude
lower classification and training times, and it is almost as accurate as
ensemble classifiers, whose computational complexity makes them inapplicable
even for mid-size datasets. The outstanding robustness of WEASEL is also
confirmed by experiments on two real smart grid datasets, where it
out-of-the-box achieves almost the same accuracy as highly tuned,
domain-specific methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security,
Abstract: Online programming discussion platforms such as Stack Overflow serve as a
rich source of information for software developers. Available information
include vibrant discussions and oftentimes ready-to-use code snippets.
Anecdotes report that software developers copy and paste code snippets from
those information sources for convenience reasons. Such behavior results in a
constant flow of community-provided code snippets into production software. To
date, the impact of this behaviour on code security is unknown. We answer this
highly important question by quantifying the proliferation of security-related
code snippets from Stack Overflow in Android applications available on Google
Play. Access to the rich source of information available on Stack Overflow
including ready-to-use code snippets provides huge benefits for software
developers. However, when it comes to code security there are some caveats to
bear in mind: Due to the complex nature of code security, it is very difficult
to provide ready-to-use and secure solutions for every problem. Hence,
integrating a security-related code snippet from Stack Overflow into production
software requires caution and expertise. Unsurprisingly, we observed insecure
code snippets being copied into Android applications millions of users install
from Google Play every day. To quantitatively evaluate the extent of this
observation, we scanned Stack Overflow for code snippets and evaluated their
security score using a stochastic gradient descent classifier. In order to
identify code reuse in Android applications, we applied state-of-the-art static
analysis. Our results are alarming: 15.4% of the 1.3 million Android
applications we analyzed, contained security-related code snippets from Stack
Overflow. Out of these 97.9% contain at least one insecure code snippet. | [
1,
0,
0,
0,
0,
0
] |
Title: K-theory of group Banach algebras and Banach property RD,
Abstract: We investigate Banach algebras of convolution operators on the $L^p$ spaces
of a locally compact group, and their K-theory. We show that for a discrete
group, the corresponding K-theory groups depend continuously on $p$ in an
inductive sense. Via a Banach version of property RD, we show that for a large
class of groups, the K-theory groups of the Banach algebras are independent of
$p$. | [
0,
0,
1,
0,
0,
0
] |
Title: Learning from various labeling strategies for suicide-related messages on social media: An experimental study,
Abstract: Suicide is an important but often misunderstood problem, one that researchers
are now seeking to better understand through social media. Due in large part to
the fuzzy nature of what constitutes suicidal risks, most supervised approaches
for learning to automatically detect suicide-related activity in social media
require a great deal of human labor to train. However, humans themselves have
diverse or conflicting views on what constitutes suicidal thoughts. So how to
obtain reliable gold standard labels is fundamentally challenging and, we
hypothesize, depends largely on what is asked of the annotators and what slice
of the data they label. We conducted multiple rounds of data labeling and
collected annotations from crowdsourcing workers and domain experts. We
aggregated the resulting labels in various ways to train a series of supervised
models. Our preliminary evaluations show that using unanimously agreed labels
from multiple annotators is helpful to achieve robust machine models. | [
1,
0,
0,
0,
0,
0
] |
Title: Modeling Information Flow Through Deep Neural Networks,
Abstract: This paper proposes a principled information theoretic analysis of
classification for deep neural network structures, e.g. convolutional neural
networks (CNN). The output of convolutional filters is modeled as a random
variable Y conditioned on the object class C and network filter bank F. The
conditional entropy (CENT) H(Y |C,F) is shown in theory and experiments to be a
highly compact and class-informative code, that can be computed from the filter
outputs throughout an existing CNN and used to obtain higher classification
results than the original CNN itself. Experiments demonstrate the effectiveness
of CENT feature analysis in two separate CNN classification contexts. 1) In the
classification of neurodegeneration due to Alzheimer's disease (AD) and natural
aging from 3D magnetic resonance image (MRI) volumes, 3 CENT features result in
an AUC=94.6% for whole-brain AD classification, the highest reported accuracy
on the public OASIS dataset used and 12% higher than the softmax output of the
original CNN trained for the task. 2) In the context of visual object
classification from 2D photographs, transfer learning based on a small set of
CENT features identified throughout an existing CNN leads to AUC values
comparable to the 1000-feature softmax output of the original network when
classifying previously unseen object categories. The general information
theoretical analysis explains various recent CNN design successes, e.g. densely
connected CNN architectures, and provides insights for future research
directions in deep learning. | [
1,
0,
0,
1,
0,
0
] |
Title: Deep Learning applied to Road Traffic Speed forecasting,
Abstract: In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to
forecast a regression model for time dependent data. These algorithm's are
designed to handle Floating Car Data (FCD) historic speeds to predict road
traffic data. For this we aggregate the speeds into the network inputs in an
innovative way. We compare the RMSE thus obtained with the results of a simpler
physical model, and show that the latter achieves better RMSE accuracy. We also
propose a new indicator, which evaluates the algorithms improvement when
compared to a benchmark prediction. We conclude by questioning the interest of
using deep learning methods for this specific regression task. | [
1,
0,
0,
1,
0,
0
] |
Title: Wait For It: Identifying "On-Hold" Self-Admitted Technical Debt,
Abstract: Self-admitted technical debt refers to situations where a software developer
knows that their current implementation is not optimal and indicates this using
a source code comment. In this work, we hypothesize that it is possible to
develop automated techniques to understand a subset of these comments in more
detail, and to propose tool support that can help developers manage
self-admitted technical debt more effectively. Based on a qualitative study of
335 comments indicating self-admitted technical debt, we first identify one
particular class of debt amenable to automated management: "on-hold"
self-admitted technical debt, i.e., debt which contains a condition to indicate
that a developer is waiting for a certain event or an updated functionality
having been implemented elsewhere. We then design and evaluate an automated
classifier which can automatically identify these "on-hold" instances with a
precision of 0.81 as well as detect the specific conditions that developers are
waiting for. Our work presents a first step towards automated tool support that
is able to indicate when certain instances of self-admitted technical debt are
ready to be addressed. | [
1,
0,
0,
0,
0,
0
] |
Title: Real time observation of granular rock analogue material deformation and failure using nonlinear laser interferometry,
Abstract: A better understanding and anticipation of natural processes such as
landsliding or seismic fault activity requires detailed theoretical and
experimental analysis of rock mechanics and geomaterial dynamics. These last
decades, considerable progress has been made towards understanding deformation
and fracture process in laboratory experiment on granular rock materials, as
the well-known shear banding experiment. One of the reasons for this progress
is the continuous improvement in the instrumental techniques of observation.
But the lack of real time methods does not allow the detection of indicators of
the upcoming fracture process and thus to anticipate the phenomenon. Here, we
have performed uniaxial compression experiments to analyse the response of a
granular rock material sample to different shocks. We use a novel
interferometric laser sensor based on the nonlinear self-mixing interferometry
technique to observe in real time the deformations of the sample and assess its
usefulness as a diagnostic tool for the analysis of geomaterial dynamics. Due
to the high spatial and temporal resolution of this approach, we observe both
vibrations processes in response to a dynamic loading and the onset of failure.
The latter is preceded by a continuous variation of vibration period of the
material. After several shocks, the material response is no longer reversible
and we detect a progressive accumulation of irreversible deformation leading to
the fracture process. We demonstrate that material failure is anticipated by
the critical slowing down of the surface vibrational motion, which may
therefore be envisioned as an early warning signal or predictor to the
macroscopic failure of the sample. The nonlinear self-mixing interferometry
technique is readily extensible to fault propagation measurements. As such, it
opens a new window of observation for the study of geomaterial deformation and
failure. | [
0,
1,
0,
0,
0,
0
] |
Title: CREATE: Multimodal Dataset for Unsupervised Learning, Generative Modeling and Prediction of Sensory Data from a Mobile Robot in Indoor Environments,
Abstract: The CREATE database is composed of 14 hours of multimodal recordings from a
mobile robotic platform based on the iRobot Create. The various sensors cover
vision, audition, motors and proprioception. The dataset has been designed in
the context of a mobile robot that can learn multimodal representations of its
environment, thanks to its ability to navigate the environment. This ability
can also be used to learn the dependencies and relationships between the
different modalities of the robot (e.g. vision, audition), as they reflect both
the external environment and the internal state of the robot. The provided
multimodal dataset is expected to have multiple usages, such as multimodal
unsupervised object learning, multimodal prediction and egomotion/causality
detection. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep learning enhanced mobile-phone microscopy,
Abstract: Mobile-phones have facilitated the creation of field-portable, cost-effective
imaging and sensing technologies that approach laboratory-grade instrument
performance. However, the optical imaging interfaces of mobile-phones are not
designed for microscopy and produce spatial and spectral distortions in imaging
microscopic specimens. Here, we report on the use of deep learning to correct
such distortions introduced by mobile-phone-based microscopes, facilitating the
production of high-resolution, denoised and colour-corrected images, matching
the performance of benchtop microscopes with high-end objective lenses, also
extending their limited depth-of-field. After training a convolutional neural
network, we successfully imaged various samples, including blood smears,
histopathology tissue sections, and parasites, where the recorded images were
highly compressed to ease storage and transmission for telemedicine
applications. This method is applicable to other low-cost, aberrated imaging
systems, and could offer alternatives for costly and bulky microscopes, while
also providing a framework for standardization of optical images for clinical
and biomedical applications. | [
1,
1,
0,
0,
0,
0
] |
Title: Minimal Controllability of Conjunctive Boolean Networks is NP-Complete,
Abstract: Given a conjunctive Boolean network (CBN) with $n$ state-variables, we
consider the problem of finding a minimal set of state-variables to directly
affect with an input so that the resulting conjunctive Boolean control network
(CBCN) is controllable. We give a necessary and sufficient condition for
controllability of a CBCN; an $O(n^2)$-time algorithm for testing
controllability; and prove that nonetheless the minimal controllability problem
for CBNs is NP-hard. | [
1,
0,
1,
0,
0,
0
] |
Title: D-optimal designs for complex Ornstein-Uhlenbeck processes,
Abstract: Complex Ornstein-Uhlenbeck (OU) processes have various applications in
statistical modelling. They play role e.g. in the description of the motion of
a charged test particle in a constant magnetic field or in the study of
rotating waves in time-dependent reaction diffusion systems, whereas Kolmogorov
used such a process to model the so-called Chandler wobble, small deviation in
the Earth's axis of rotation. In these applications parameter estimation and
model fitting is based on discrete observations of the underlying stochastic
process, however, the accuracy of the results strongly depend on the
observation points.
This paper studies the properties of D-optimal designs for estimating the
parameters of a complex OU process with a trend. We show that in contrast with
the case of the classical real OU process, a D-optimal design exists not only
for the trend parameter, but also for joint estimation of the covariance
parameters, moreover, these optimal designs are equidistant. | [
0,
0,
1,
1,
0,
0
] |
Title: Self-adjointness and spectral properties of Dirac operators with magnetic links,
Abstract: We define Dirac operators on $\mathbb{S}^3$ (and $\mathbb{R}^3$) with
magnetic fields supported on smooth, oriented links and prove self-adjointness
of certain (natural) extensions. We then analyze their spectral properties and
show, among other things, that these operators have discrete spectrum. Certain
examples, such as circles in $\mathbb{S}^3$, are investigated in detail and we
compute the dimension of the zero-energy eigenspace. | [
0,
0,
1,
0,
0,
0
] |
Title: Latent tree models,
Abstract: Latent tree models are graphical models defined on trees, in which only a
subset of variables is observed. They were first discussed by Judea Pearl as
tree-decomposable distributions to generalise star-decomposable distributions
such as the latent class model. Latent tree models, or their submodels, are
widely used in: phylogenetic analysis, network tomography, computer vision,
causal modeling, and data clustering. They also contain other well-known
classes of models like hidden Markov models, Brownian motion tree model, the
Ising model on a tree, and many popular models used in phylogenetics. This
article offers a concise introduction to the theory of latent tree models. We
emphasise the role of tree metrics in the structural description of this model
class, in designing learning algorithms, and in understanding fundamental
limits of what and when can be learned. | [
0,
0,
1,
1,
0,
0
] |
Title: Towards a Knowledge Graph based Speech Interface,
Abstract: Applications which use human speech as an input require a speech interface
with high recognition accuracy. The words or phrases in the recognised text are
annotated with a machine-understandable meaning and linked to knowledge graphs
for further processing by the target application. These semantic annotations of
recognised words can be represented as a subject-predicate-object triples which
collectively form a graph often referred to as a knowledge graph. This type of
knowledge representation facilitates to use speech interfaces with any spoken
input application, since the information is represented in logical, semantic
form, retrieving and storing can be followed using any web standard query
languages. In this work, we develop a methodology for linking speech input to
knowledge graphs and study the impact of recognition errors in the overall
process. We show that for a corpus with lower WER, the annotation and linking
of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight,
a tool to interlink text documents with the linked open data is used to link
the speech recognition output to the DBpedia knowledge graph. Such a
knowledge-based speech recognition interface is useful for applications such as
question answering or spoken dialog systems. | [
1,
0,
0,
0,
0,
0
] |
Title: Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size,
Abstract: A key problem in research on adversarial examples is that vulnerability to
adversarial examples is usually measured by running attack algorithms. Because
the attack algorithms are not optimal, the attack algorithms are prone to
overestimating the size of perturbation needed to fool the target model. In
other words, the attack-based methodology provides an upper-bound on the size
of a perturbation that will fool the model, but security guarantees require a
lower bound. CLEVER is a proposed scoring method to estimate a lower bound.
Unfortunately, an estimate of a bound is not a bound. In this report, we show
that gradient masking, a common problem that causes attack methodologies to
provide only a very loose upper bound, causes CLEVER to overestimate the size
of perturbation needed to fool the model. In other words, CLEVER does not
resolve the key problem with the attack-based methodology, because it fails to
provide a lower bound. | [
0,
0,
0,
1,
0,
0
] |
Title: Quantum capacitance of double-layer graphene,
Abstract: We study the ground-state properties of a double layer graphene system with
the Coulomb interlayer electron-electron interaction modeled within the random
phase approximation. We first obtain an expression of the quantum capacitance
of a two layer system. In addition, we calculate the many-body
exchange-correlation energy and quantum capacitance of the hybrid double layer
graphene system at zero-temperature. We show an enhancement of the majority
density layer thermodynamic density-of-states owing to an increasing interlayer
interaction between two layers near the Dirac point. The quantum capacitance
near the neutrality point behaves like square root of the total density,
$\alpha \sqrt{n}$, where the coefficient $\alpha$ decreases by increasing the
charge density imbalance between two layers. Furthermore, we show that the
quantum capacitance changes linearly by the gate voltage. Our results can be
verified by current experiments. | [
0,
1,
0,
0,
0,
0
] |
Title: Distributed Time Synchronization for Networks with Random Delays and Measurement Noise,
Abstract: In this paper a new distributed asynchronous algorithm is proposed for time
synchronization in networks with random communication delays, measurement noise
and communication dropouts. Three different types of the drift correction
algorithm are introduced, based on different kinds of local time increments.
Under nonrestrictive conditions concerning network properties, it is proved
that all the algorithm types provide convergence in the mean square sense and
with probability one (w.p.1) of the corrected drifts of all the nodes to the
same value (consensus). An estimate of the convergence rate of these algorithms
is derived. For offset correction, a new algorithm is proposed containing a
compensation parameter coping with the influence of random delays and special
terms taking care of the influence of both linearly increasing time and drift
correction. It is proved that the corrected offsets of all the nodes converge
in the mean square sense and w.p.1. An efficient offset correction algorithm
based on consensus on local compensation parameters is also proposed. It is
shown that the overall time synchronization algorithm can also be implemented
as a flooding algorithm with one reference node. It is proved that it is
possible to achieve bounded error between local corrected clocks in the mean
square sense and w.p.1. Simulation results provide an additional practical
insight into the algorithm properties and show its advantage over the existing
methods. | [
1,
0,
0,
0,
0,
0
] |
Title: Controller Synthesis for Discrete-time Hybrid Polynomial Systems via Occupation Measures,
Abstract: We present a novel controller synthesis approach for discrete-time hybrid
polynomial systems, a class of systems that can model a wide variety of
interactions between robots and their environment. The approach is rooted in
recently developed techniques that use occupation measures to formulate the
controller synthesis problem as an infinite-dimensional linear program. The
relaxation of the linear program as a finite-dimensional semidefinite program
can be solved to generate a control law. The approach has several advantages
including that the formulation is convex, that the formulation and the
extracted controllers are simple, and that the computational complexity is
polynomial in the state and control input dimensions. We illustrate our
approach on some robotics examples. | [
1,
0,
0,
0,
0,
0
] |
Title: Semiflat Orbifold Projections,
Abstract: We compute the semiflat positive cone $K_0^{+SF}(A_\theta^\sigma)$ of the
$K_0$-group of the irrational rotation orbifold $A_\theta^\sigma$ under the
noncommutative Fourier transform $\sigma$ and show that it is determined by
classes of positive trace and the vanishing of two topological invariants. The
semiflat orbifold projections are 3-dimensional and come in three basic
topological genera: $(2,0,0)$, $(1,1,2)$, $(0,0,2)$. (A projection is called
semiflat when it has the form $h + \sigma(h)$ where $h$ is a flip-invariant
projection such that $h\sigma(h)=0$.) Among other things, we also show that
every number in $(0,1) \cap (2\mathbb Z + 2\mathbb Z\theta)$ is the trace of a
semiflat projection in $A_\theta$. The noncommutative Fourier transform is the
order 4 automorphism $\sigma: V \to U \to V^{-1}$ (and the flip is $\sigma^2$:
$U \to U^{-1},\ V \to V^{-1}$), where $U,V$ are the canonical unitary
generators of the rotation algebra $A_\theta$ satisfying $VU = e^{2\pi i\theta}
UV$. | [
0,
0,
1,
0,
0,
0
] |
Title: Magnetization process of the S = 1/2 two-leg organic spin-ladder compound BIP-BNO,
Abstract: We have measured the magnetization of the organic compound BIP-BNO
(3,5'-bis(N-tert-butylaminoxyl)-3',5-dibromobiphenyl) up to 76 T where the
magnetization is saturated. The S = 1/2 antiferromagnetic Heisenberg two-leg
spin-ladder model accounts for the obtained experimental data regarding the
magnetization curve, which is clarified using the quantum Monte Carlo method.
The exchange constants on the rung and the side rail of the ladder are
estimated to be J(rung)/kB = 65.7 K and J(leg)/kB = 14.1 K, respectively,
deeply in the strong coupling region: J(rung)/J(leg) > 1. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Over-sampling Framework for Classifying Imbalanced Data,
Abstract: Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose Deep Over-sampling (DOS), a framework for extending the
synthetic over-sampling method to exploit the deep feature space acquired by a
convolutional neural network (CNN). Its key feature is an explicit, supervised
representation learning, for which the training data presents each raw input
sample with a synthetic embedding target in the deep feature space, which is
sampled from the linear subspace of in-class neighbors. We implement an
iterative process of training the CNN and updating the targets, which induces
smaller in-class variance among the embeddings, to increase the discriminative
power of the deep representation. We present an empirical study using public
benchmarks, which shows that the DOS framework not only counteracts class
imbalance better than the existing method, but also improves the performance of
the CNN in the standard, balanced settings. | [
1,
0,
0,
1,
0,
0
] |
Title: The Double Galaxy Cluster Abell 2465 III. X-ray and Weak-lensing Observations,
Abstract: We report Chandra X-ray observations and optical weak-lensing measurements
from Subaru/Suprime-Cam images of the double galaxy cluster Abell 2465
(z=0.245). The X-ray brightness data are fit to a beta-model to obtain the
radial gas density profiles of the northeast (NE) and southwest (SW)
sub-components, which are seen to differ in structure. We determine core radii,
central temperatures, the gas masses within $r_{500c}$, and the total masses
for the broader NE and sharper SW components assuming hydrostatic equilibrium.
The central entropy of the NE clump is about two times higher than the SW.
Along with its structural properties, this suggests that it has undergone
merging on its own. The weak-lensing analysis gives virial masses for each
substructure, which compare well with earlier dynamical results. The derived
outer mass contours of the SW sub-component from weak lensing are more
irregular and extended than those of the NE. Although there is a weak
enhancement and small offsets between X-ray gas and mass centers from weak
lensing, the lack of large amounts of gas between the two sub-clusters
indicates that Abell 2465 is in a pre-merger state. A dynamical model that is
consistent with the observed cluster data, based on the FLASH program and the
radial infall model, is constructed, where the subclusters currently separated
by ~1.2Mpc are approaching each other at ~2000km/s and will meet in ~0.4Gyr. | [
0,
1,
0,
0,
0,
0
] |
Title: Monte Carlo study of magnetic nanoparticles adsorbed on halloysite $Al_2Si_2O_5(OH)_4$ nanotubes,
Abstract: We study properties of magnetic nanoparticles adsorbed on the halloysite
surface. For that a distinct magnetic Hamiltonian with random distribution of
spins on a cylindrical surface was solved by using a nonequilibrium Monte Carlo
method. The parameters for our simulations: anisotropy constant, nanoparticle
size distribution, saturated magnetization and geometrical parameters of the
halloysite template were taken from recent experiments. We calculate the
hysteresis loops and temperature dependence of the zero field cooling (ZFC)
susceptibility, which maximum determines the blocking temperature. It is shown
that the dipole-dipole interaction between nanoparticles moderately increases
the blocking temperature and weakly increases the coercive force. The obtained
hysteresis loops (e.g., the value of the coercive force) for Ni nanoparticles
are in reasonable agreement with the experimental data. We also discuss the
sensitivity of the hysteresis loops and ZFC susceptibilities to the change of
anisotropy and dipole-dipole interaction, as well as the 3d-shell occupation of
the metallic nanoparticles; in particular we predict larger coercive force for
Fe, than for Ni nanoparticles. | [
0,
1,
0,
0,
0,
0
] |
Title: Conversion of Mersenne Twister to double-precision floating-point numbers,
Abstract: The 32-bit Mersenne Twister generator MT19937 is a widely used random number
generator. To generate numbers with more than 32 bits in bit length, and
particularly when converting into 53-bit double-precision floating-point
numbers in $[0,1)$ in the IEEE 754 format, the typical implementation
concatenates two successive 32-bit integers and divides them by a power of $2$.
In this case, the 32-bit MT19937 is optimized in terms of its equidistribution
properties (the so-called dimension of equidistribution with $v$-bit accuracy)
under the assumption that one will mainly be using 32-bit output values, and
hence the concatenation sometimes degrades the dimension of equidistribution
compared with the simple use of 32-bit outputs. In this paper, we analyze such
phenomena by investigating hidden $\mathbb{F}_2$-linear relations among the
bits of high-dimensional outputs. Accordingly, we report that MT19937 with a
specific lag set fails several statistical tests, such as the overlapping
collision test, matrix rank test, and Hamming independence test. | [
1,
0,
0,
1,
0,
0
] |
Title: Computational Study of Halide Perovskite-Derived A$_2$BX$_6$ Inorganic Compounds: Chemical Trends in Electronic Structure and Structural Stability,
Abstract: The electronic structure and energetic stability of A$_2$BX$_6$ halide
compounds with the cubic and tetragonal variants of the perovskite-derived
K$_2$PtCl$_6$ prototype structure are investigated computationally within the
frameworks of density-functional-theory (DFT) and hybrid (HSE06) functionals.
The HSE06 calculations are undertaken for seven known A$_2$BX$_6$ compounds
with A = K, Rb and Cs, and B = Sn, Pd, Pt, Te, and X = I. Trends in band gaps
and energetic stability are identified, which are explored further employing
DFT calculations over a larger range of chemistries, characterized by A = K,
Rb, Cs, B = Si, Ge, Sn, Pb, Ni, Pd, Pt, Se and Te and X = Cl, Br, I. For the
systems investigated in this work, the band gap increases from iodide to
bromide to chloride. Further, variations in the A site cation influences the
band gap as well as the preferred degree of tetragonal distortion. Smaller A
site cations such as K and Rb favor tetragonal structural distortions,
resulting in a slightly larger band gap. For variations in the B site in the
(Ni, Pd, Pt) group and the (Se, Te) group, the band gap increases with
increasing cation size. However, no observed chemical trend with respect to
cation size for band gap was found for the (Si, Sn, Ge, Pb) group. The findings
in this work provide guidelines for the design of halide A$_2$BX$_6$ compounds
for potential photovoltaic applications. | [
0,
1,
0,
0,
0,
0
] |
Title: A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models,
Abstract: For high-dimensional sparse linear models, how to construct confidence
intervals for coefficients remains a difficult question. The main reason is the
complicated limiting distributions of common estimators such as the Lasso.
Several confidence interval construction methods have been developed, and
Bootstrap Lasso+OLS is notable for its simple technicality, good
interpretability, and comparable performance with other more complicated
methods. However, Bootstrap Lasso+OLS depends on the beta-min assumption, a
theoretic criterion that is often violated in practice. In this paper, we
introduce a new method called Bootstrap Lasso+Partial Ridge (LPR) to relax this
assumption. LPR is a two-stage estimator: first using Lasso to select features
and subsequently using Partial Ridge to refit the coefficients. Simulation
results show that Bootstrap LPR outperforms Bootstrap Lasso+OLS when there
exist small but non-zero coefficients, a common situation violating the
beta-min assumption. For such coefficients, compared to Bootstrap Lasso+OLS,
confidence intervals constructed by Bootstrap LPR have on average 50% larger
coverage probabilities. Bootstrap LPR also has on average 35% shorter
confidence interval lengths than the de-sparsified Lasso methods, regardless of
whether linear models are misspecified. Additionally, we provide theoretical
guarantees of Bootstrap LPR under appropriate conditions and implement it in
the R package "HDCI." | [
0,
0,
0,
1,
0,
0
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.