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Title: The Large D Limit of Planar Diagrams,
Abstract: We show that in $\text{O}(D)$ invariant matrix theories containing a large
number $D$ of complex or Hermitian matrices, one can define a
$D\rightarrow\infty$ limit for which the sum over planar diagrams truncates to
a tractable, yet non-trivial, sum over melon diagrams. In particular, results
obtained recently in SYK and tensor models can be generalized to traditional,
string-inspired matrix quantum mechanical models of black holes. | [
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] |
Title: Trajectory Tracking Control of a Flexible Spine Robot, With and Without a Reference Input,
Abstract: The Underactuated Lightweight Tensegrity Robotic Assistive Spine (ULTRA
Spine) project is an ongoing effort to develop a flexible, actuated backbone
for quadruped robots. In this work, model-predictive control is used to track a
trajectory in the robot's state space, in simulation. The state trajectory used
here corresponds to a bending motion of the spine, with translations and
rotations of the moving vertebrae. Two different controllers are presented in
this work: one that does not use a reference input but includes smoothing
constrants, and a second one that uses a reference input without smoothing. For
the smoothing controller, without reference inputs, the error converges to
zero, while the simpler-to-tune controller with an input reference shows small
errors but not complete convergence. It is expected that this controller will
converge as it is improved further. | [
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] |
Title: Noncoherent Analog Network Coding using LDPC-coded FSK,
Abstract: Analog network coding (ANC) is a throughput increasing technique for the
two-way relay channel (TWRC) whereby two end nodes transmit simultaneously to a
relay at the same time and band, followed by the relay broadcasting the
received sum of signals to the end nodes. Coherent reception under ANC is
challenging due to requiring oscillator synchronization for all nodes, a
problem further exacerbated by Doppler shift. This work develops a noncoherent
M-ary frequency-shift keyed (FSK) demodulator implementing ANC. The demodulator
produces soft outputs suitable for use with capacity-approaching channel codes
and supports information feedback from the channel decoder. A unique aspect of
the formulation is the presence of an infinite summation in the received symbol
probability density function. Detection and channel decoding succeed when the
truncated summation contains a sufficient number of terms. Bit error rate
performance is investigated by Monte Carlo simulation, considering modulation
orders two, four and eight, channel coded and uncoded operation, and with and
without information feedback from decoder to demodulator. The channel code
considered for simulation is the LDPC code defined by the DVB-S2 standard. To
our knowledge this work is the first to develop a noncoherent soft-output
demodulator for ANC. | [
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] |
Title: Feature selection in weakly coherent matrices,
Abstract: A problem of paramount importance in both pure (Restricted Invertibility
problem) and applied mathematics (Feature extraction) is the one of selecting a
submatrix of a given matrix, such that this submatrix has its smallest singular
value above a specified level. Such problems can be addressed using
perturbation analysis. In this paper, we propose a perturbation bound for the
smallest singular value of a given matrix after appending a column, under the
assumption that its initial coherence is not large, and we use this bound to
derive a fast algorithm for feature extraction. | [
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] |
Title: Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications,
Abstract: Security threats such as jamming and route manipulation can have significant
consequences on the performance of modern wireless networks. To increase the
efficacy and stealthiness of such threats, a number of extremely challenging,
cross-layer attacks have been recently unveiled. Although existing research has
thoroughly addressed many single-layer attacks, the problem of detecting and
mitigating cross-layer attacks still remains unsolved. For this reason, in this
paper we propose a novel framework to analyze and address cross-layer attacks
in wireless networks. Specifically, our framework consists of a detection and a
mitigation component. The attack detection component is based on a Bayesian
learning detection scheme that constructs a model of observed evidence to
identify stealthy attack activities. The mitigation component comprises a
scheme that achieves the desired trade-off between security and performance. We
specialize and evaluate the proposed framework by considering a specific
cross-layer attack that uses jamming as an auxiliary tool to achieve route
manipulation. Simulations and experimental results obtained with a test-bed
made up by USRP software-defined radios demonstrate the effectiveness of the
proposed methodology. | [
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] |
Title: MHD Turbulence in spin-down flows of liquid metals,
Abstract: Intense spin-down flows allow one to reach high Rm in relatively small
laboratory setups using moderate mass of liquid metals. The spin-down flow in
toroidal channels was the first flow configuration used for studying dynamo
effects in non-stationary flows. In this paper, we estimate the effect of
small-scale dynamo in liquid metal spin-down flows realized in laboratory
experiments. Our simulations have confirmed the conclusion that the dynamo
effects observed in the experiments done on gallium are weak -- a slight burst
of small-scale magnetic energy arises only at the highest available rotation
velocity of the channel. In sodium flows, the induction effects are quite
strong -- an essential part of kinetic energy of sodium spin-down flows is
converted into magnetic energy and dissipates because of Joule heat losses. We
have extended our simulations beyond the capabilities of existing laboratory
facilities and examined the spin-down flows at the channel rotation velocity
Omega>> 50 rps. It has been found that $\Omega\approx 100$rps is enough to
reach the equipartition of magnetic and kinetic spectral power density at the
lowest wave numbers (largest scales), whereas at Omega >= 200rps the intensity
of the magnetic field becomes comparable to the intensity of velocity field
fluctuations. We have also studied the influence of the Pm on the efficiency of
small-scale dynamo in spin-down flows. In the experimental spin-down flows, the
small-scale dynamo remains in a quasi-kinematic regime, and magnetic energy is
mainly dissipated at the same scale, wherein it is converted from kinetic
energy. The real small-scale dynamo starts to operate at Pm>10^{-4}, and the
inertial range of the magnetic energy spectrum appears. Thereupon the energy
dissipation is postponed to a later time and smaller scales, and the peak of
turbulent energy (both kinetic and magnetic) slightly increases with Pm. | [
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] |
Title: Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks,
Abstract: The multi-agent path-finding (MAPF) problem has recently received a lot of
attention. However, it does not capture important characteristics of many
real-world domains, such as automated warehouses, where agents are constantly
engaged with new tasks. In this paper, we therefore study a lifelong version of
the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In
the MAPD problem, agents have to attend to a stream of delivery tasks in an
online setting. One agent has to be assigned to each delivery task. This agent
has to first move to a given pickup location and then to a given delivery
location while avoiding collisions with other agents. We present two decoupled
MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS).
Theoretically, we show that they solve all well-formed MAPD instances, a
realistic subclass of MAPD instances. Experimentally, we compare them against a
centralized strawman MAPD algorithm without this guarantee in a simulated
warehouse system. TP can easily be extended to a fully distributed MAPD
algorithm and is the best choice when real-time computation is of primary
concern since it remains efficient for MAPD instances with hundreds of agents
and tasks. TPTS requires limited communication among agents and balances well
between TP and the centralized MAPD algorithm. | [
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] |
Title: Examples of finite dimensional algebras which do not satisfy the derived Jordan--Hölder property,
Abstract: We construct a matrix algebra $\Lambda(A,B)$ from two given finite
dimensional elementary algebras $A$ and $B$ and give some sufficient conditions
on $A$ and $B$ under which the derived Jordan--Hölder property (DJHP) fails
for $\Lambda(A,B)$. This provides finite dimensional algebras of finite global
dimension which do not satisfy DJHP. | [
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1,
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] |
Title: Learning retrosynthetic planning through self-play,
Abstract: The problem of retrosynthetic planning can be framed as one player game, in
which the chemist (or a computer program) works backwards from a molecular
target to simpler starting materials though a series of choices regarding which
reactions to perform. This game is challenging as the combinatorial space of
possible choices is astronomical, and the value of each choice remains
uncertain until the synthesis plan is completed and its cost evaluated. Here,
we address this problem using deep reinforcement learning to identify policies
that make (near) optimal reaction choices during each step of retrosynthetic
planning. Using simulated experience or self-play, we train neural networks to
estimate the expected synthesis cost or value of any given molecule based on a
representation of its molecular structure. We show that learned policies based
on this value network outperform heuristic approaches in synthesizing
unfamiliar molecules from available starting materials using the fewest number
of reactions. We discuss how the learned policies described here can be
incorporated into existing synthesis planning tools and how they can be adapted
to changes in the synthesis cost objective or material availability. | [
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] |
Title: Real-space analysis of scanning tunneling microscopy topography datasets using sparse modeling approach,
Abstract: A sparse modeling approach is proposed for analyzing scanning tunneling
microscopy topography data, which contains numerous peaks corresponding to
surface atoms. The method, based on the relevance vector machine with
$\mathrm{L}_1$ regularization and $k$-means clustering, enables separation of
the peaks and atomic center positioning with accuracy beyond the resolution of
the measurement grid. The validity and efficiency of the proposed method are
demonstrated using synthetic data in comparison to the conventional
least-square method. An application of the proposed method to experimental data
of a metallic oxide thin film clearly indicates the existence of defects and
corresponding local lattice deformations. | [
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] |
Title: Scattered light intensity measurements of plasma treated Polydimethylsiloxane films: A measure to detect surface modification,
Abstract: Polydimethylsiloxane (PDMS) films possess different chemical and physical
properties based on surface modification. The bond structure of pristine PDMS
films and plasma treated PDMS films differ in a particular region of silicate
bonds. We have studied the surface physical properties of pristine PDMS films
and plasma treated PDMS films through optical technique. It is already known
that plasma treated PDMS films forms very thin SiO2 layer on its surface. Due
to difference in coefficient of thermal expansion of the surface SiO2 and the
remaining bulk layer, the SiO2 layer develops cracks. These formations are
explored to characterize PDMS surface by observing intensity of scattered light
while the films are stretched. Pristine PDMS films do not show such optical
scattering. Further the intensity measurements were repeated over period of
time to monitor surface properties with time. It was observed that plasma
treated PDMS film; the scattered light intensity is linearly dependent on the
applied force for stretching. After a substantial time, scattering intensity is
reduced to the value which is almost equal to that of pristine PDMS surface. It
is proposed that basic light scattering through plasma treated PDMS occurs due
to the decrease in the width of the cracks when the PDMS film is stretched. The
change in the scattering property of plasma treated PDMS surface over time
could be attributed to healing of cracks by the migration of polymer chain
molecules from the bulk to the surface. It is also reported that with reference
to various bonds present on surface plasma treated PDMS surface regains its
original properties as that of pristine PDMS with time. Therefore the optical
technique could be employed to study surface characteristics of PDMS surface as
an alternative approach to conventional spectroscopic techniques. | [
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] |
Title: A hierarchical Bayesian model for predicting ecological interactions using evolutionary relationships,
Abstract: Identifying undocumented or potential future interactions among species is a
challenge facing modern ecologists. Recent link prediction methods rely on
trait data, however large species interaction databases are typically sparse
and covariates are limited to only a fraction of species. On the other hand,
evolutionary relationships, encoded as phylogenetic trees, can act as proxies
for underlying traits and historical patterns of parasite sharing among hosts.
We show that using a network-based conditional model, phylogenetic information
provides strong predictive power in a recently published global database of
host-parasite interactions. By scaling the phylogeny using an evolutionary
model, our method allows for biological interpretation often missing from
latent variable models. To further improve on the phylogeny-only model, we
combine a hierarchical Bayesian latent score framework for bipartite graphs
that accounts for the number of interactions per species with the host
dependence informed by phylogeny. Combining the two information sources yields
significant improvement in predictive accuracy over each of the submodels
alone. As many interaction networks are constructed from presence-only data, we
extend the model by integrating a correction mechanism for missing
interactions, which proves valuable in reducing uncertainty in unobserved
interactions. | [
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] |
Title: Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images,
Abstract: Pathological lung segmentation (PLS) is an important, yet challenging,
medical image application due to the wide variability of pathological lung
appearance and shape. Because PLS is often a pre-requisite for other imaging
analytics, methodological simplicity and generality are key factors in
usability. Along those lines, we present a bottom-up deep-learning based
approach that is expressive enough to handle variations in appearance, while
remaining unaffected by any variations in shape. We incorporate the deeply
supervised learning framework, but enhance it with a simple, yet effective,
progressive multi-path scheme, which more reliably merges outputs from
different network stages. The result is a deep model able to produce finer
detailed masks, which we call progressive holistically-nested networks
(P-HNNs). Using extensive cross-validation, our method is tested on
multi-institutional datasets comprising 929 CT scans (848 publicly available),
of pathological lungs, reporting mean dice scores of 0.985 and demonstrating
significant qualitative and quantitative improvements over state-of-the art
approaches. | [
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] |
Title: On the Helium fingers in the intracluster medium,
Abstract: In this paper we investigate the convection phenomenon in the intracluster
medium (the weakly-collisional magnetized inhomogeneous plasma permeating
galaxy clusters) where the concentration gradient of the Helium ions is not
ignorable. To this end, we build upon the general machinery employed to study
the salt finger instability found in the oceans. The salt finger instability is
a form of double diffusive convection where the diffusions of two physical
quantities---heat and salt concentrations---occur with different diffusion
rates. The analogous instability in the intracluster medium may result owing to
the magnetic field mediated anisotropic diffusions of the heat and the Helium
ions (in the sea of the Hydrogen ions and the free electrons). These two
diffusions have inherently different diffusion rates. Hence the convection
caused by the onset of this instability is an example of double diffusive
convection in the astrophysical settings. A consequence of this instability is
the formation of the vertical filamentary structures having more concentration
of the Helium ions with respect to the immediate neighbourhoods of the
filaments. We term these structures as Helium fingers in analogy with the salt
fingers found in the case of the salt finger instability. Here we show that the
width of a Helium finger scales as one-fourth power of the radius of the inner
region of the intracluster medium in the supercritical regime. We also
determine the explicit mathematical expression of the criterion for the onset
of the heat-flux-driven buoyancy instability modified by the presence of
inhomogeneously distributed Helium ions. | [
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] |
Title: Absolute frequency determination of molecular transition in the Doppler regime at kHz level of accuracy,
Abstract: We present absolute frequency measurement of the unperturbed P7 P7 O$_2$
B-band transition with relative standard uncertainty of $2\times10^{-11}$. We
reached the level of accuracy typical for Doppler-free techniques, with
Doppler-limited spectroscopy. The Doppler-limited shapes of the P7 P7 spectral
line were measured with a frequency-stabilized cavity ring-down spectrometer
referenced to an $^{88}$Sr optical atomic clock by an optical frequency comb. | [
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1,
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] |
Title: Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification,
Abstract: Similarity search is essential to many important applications and often
involves searching at scale on high-dimensional data based on their similarity
to a query. In biometric applications, recent vulnerability studies have shown
that adversarial machine learning can compromise biometric recognition systems
by exploiting the biometric similarity information. Existing methods for
biometric privacy protection are in general based on pairwise matching of
secured biometric templates and have inherent limitations in search efficiency
and scalability. In this paper, we propose an inference-based framework for
privacy-preserving similarity search in Hamming space. Our approach builds on
an obfuscated distance measure that can conceal Hamming distance in a dynamic
interval. Such a mechanism enables us to systematically design statistically
reliable methods for retrieving most likely candidates without knowing the
exact distance values. We further propose to apply Montgomery multiplication
for generating search indexes that can withstand adversarial similarity
analysis, and show that information leakage in randomized Montgomery domains
can be made negligibly small. Our experiments on public biometric datasets
demonstrate that the inference-based approach can achieve a search accuracy
close to the best performance possible with secure computation methods, but the
associated cost is reduced by orders of magnitude compared to cryptographic
primitives. | [
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] |
Title: Sublinear elliptic problems under radiality. Harmonic $NA$ groups and Euclidean spaces,
Abstract: Let $\L $ be the Laplace operator on $\R ^d$, $d\geq 3$ or the Laplace
Beltrami operator on the harmonic $NA$ group (in particular on a rank one
noncompact symmetric space).
For the equation $ \L u - \varphi(\cdot,u)=0$ we give necessary and
sufficient conditions for the existence of entire bounded or large solutions
under the hypothesis of radiality of $\varphi$ with respect to the first
variable.
A Harnack-type inequality for positive continuous solutions is also proved. | [
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] |
Title: Bayesian Uncertainty Directed Trial Designs,
Abstract: Most Bayesian response-adaptive designs unbalance randomization rates towards
the most promising arms with the goal of increasing the number of positive
treatment outcomes during the study, even though the primary aim of the trial
is different. We discuss Bayesian uncertainty directed designs (BUD), a class
of Bayesian designs in which the investigator specifies an information measure
tailored to the experiment. All decisions during the trial are selected to
optimize the available information at the end of the study. The approach can be
applied to several designs, ranging from early stage multi-arm trials to
biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of
the patient allocation proportion to treatments, and illustrate the
finite-sample operating characteristics of BUD designs through examples,
including multi-arm trials, biomarker-stratified trials, and trials with
multiple co-primary endpoints. | [
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] |
Title: Wasserstein Learning of Deep Generative Point Process Models,
Abstract: Point processes are becoming very popular in modeling asynchronous sequential
data due to their sound mathematical foundation and strength in modeling a
variety of real-world phenomena. Currently, they are often characterized via
intensity function which limits model's expressiveness due to unrealistic
assumptions on its parametric form used in practice. Furthermore, they are
learned via maximum likelihood approach which is prone to failure in
multi-modal distributions of sequences. In this paper, we propose an
intensity-free approach for point processes modeling that transforms nuisance
processes to a target one. Furthermore, we train the model using a
likelihood-free leveraging Wasserstein distance between point processes.
Experiments on various synthetic and real-world data substantiate the
superiority of the proposed point process model over conventional ones. | [
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] |
Title: Interior Structures and Tidal Heating in the TRAPPIST-1 Planets,
Abstract: With seven planets, the TRAPPIST-1 system has the largest number of
exoplanets discovered in a single system so far. The system is of
astrobiological interest, because three of its planets orbit in the habitable
zone of the ultracool M dwarf. Assuming the planets are composed of
non-compressible iron, rock, and H$_2$O, we determine possible interior
structures for each planet. To determine how much tidal heat may be dissipated
within each planet, we construct a tidal heat generation model using a single
uniform viscosity and rigidity for each planet based on the planet's
composition. With the exception of TRAPPIST-1c, all seven of the planets have
densities low enough to indicate the presence of significant H$_2$O in some
form. Planets b and c experience enough heating from planetary tides to
maintain magma oceans in their rock mantles; planet c may have eruptions of
silicate magma on its surface, which may be detectable with next-generation
instrumentation. Tidal heat fluxes on planets d, e, and f are lower, but are
still twenty times higher than Earth's mean heat flow. Planets d and e are the
most likely to be habitable. Planet d avoids the runaway greenhouse state if
its albedo is $\gtrsim$ 0.3. Determining the planet's masses within $\sim0.1$
to 0.5 Earth masses would confirm or rule out the presence of H$_2$O and/or
iron in each planet, and permit detailed models of heat production and
transport in each planet. Understanding the geodynamics of ice-rich planets f,
g, and h requires more sophisticated modeling that can self-consistently
balance heat production and transport in both rock and ice layers. | [
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] |
Title: A Driver-in-the Loop Fuel Economic Control Strategy for Connected Vehicles in Urban Roads,
Abstract: In this paper, we focus on developing driver-in-the loop fuel economic
control strategy for multiple connected vehicles. The control strategy is
considered to work in a driver assistance framework where the controller gives
command to a driver to follow while considering the ability of the driver in
following control commands. Our proposed method uses vehicle-to-vehicle (V2V)
communication, exploits traffic lights' Signal Phase and Timing (SPAT)
information, models driver error injection with Markov chain, and employs
scenario tree based stochastic model predictive control to improve vehicle fuel
economy and traffic mobility. The proposed strategy is decentralized in nature
as every vehicle evaluates its own strategy using only local information.
Simulation results show the effect of consideration of driver error injection
when synthesizing fuel economic controllers in a driver assistance fashion. | [
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] |
Title: A General and Adaptive Robust Loss Function,
Abstract: We present a generalization of the Cauchy/Lorentzian, Geman-McClure,
Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2
loss functions. By introducing robustness as a continous parameter, our loss
function allows algorithms built around robust loss minimization to be
generalized, which improves performance on basic vision tasks such as
registration and clustering. Interpreting our loss as the negative log of a
univariate density yields a general probability distribution that includes
normal and Cauchy distributions as special cases. This probabilistic
interpretation enables the training of neural networks in which the robustness
of the loss automatically adapts itself during training, which improves
performance on learning-based tasks such as generative image synthesis and
unsupervised monocular depth estimation, without requiring any manual parameter
tuning. | [
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] |
Title: Asymptotically Efficient Estimation of Smooth Functionals of Covariance Operators,
Abstract: Let $X$ be a centered Gaussian random variable in a separable Hilbert space
${\mathbb H}$ with covariance operator $\Sigma.$ We study a problem of
estimation of a smooth functional of $\Sigma$ based on a sample $X_1,\dots
,X_n$ of $n$ independent observations of $X.$ More specifically, we are
interested in functionals of the form $\langle f(\Sigma), B\rangle,$ where
$f:{\mathbb R}\mapsto {\mathbb R}$ is a smooth function and $B$ is a nuclear
operator in ${\mathbb H}.$ We prove concentration and normal approximation
bounds for plug-in estimator $\langle f(\hat \Sigma),B\rangle,$ $\hat
\Sigma:=n^{-1}\sum_{j=1}^n X_j\otimes X_j$ being the sample covariance based on
$X_1,\dots, X_n.$ These bounds show that $\langle f(\hat \Sigma),B\rangle$ is
an asymptotically normal estimator of its expectation ${\mathbb E}_{\Sigma}
\langle f(\hat \Sigma),B\rangle$ (rather than of parameter of interest $\langle
f(\Sigma),B\rangle$) with a parametric convergence rate $O(n^{-1/2})$ provided
that the effective rank ${\bf r}(\Sigma):= \frac{{\bf tr}(\Sigma)}{\|\Sigma\|}$
(${\rm tr}(\Sigma)$ being the trace and $\|\Sigma\|$ being the operator norm of
$\Sigma$) satisfies the assumption ${\bf r}(\Sigma)=o(n).$ At the same time, we
show that the bias of this estimator is typically as large as $\frac{{\bf
r}(\Sigma)}{n}$ (which is larger than $n^{-1/2}$ if ${\bf r}(\Sigma)\geq
n^{1/2}$). In the case when ${\mathbb H}$ is finite-dimensional space of
dimension $d=o(n),$ we develop a method of bias reduction and construct an
estimator $\langle h(\hat \Sigma),B\rangle$ of $\langle f(\Sigma),B\rangle$
that is asymptotically normal with convergence rate $O(n^{-1/2}).$ Moreover, we
study asymptotic properties of the risk of this estimator and prove minimax
lower bounds for arbitrary estimators showing the asymptotic efficiency of
$\langle h(\hat \Sigma),B\rangle$ in a semi-parametric sense. | [
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] |
Title: Evolution of magnetic and dielectric properties in Sr-substituted high temperature multiferroic YBaCuFeO5,
Abstract: We report the evolution of structural, magnetic and dielectric properties due
to partial substitution of Ba by Sr in the high temperature multiferroic
YBaCuFeO5. This compound exhibits ferroelectric and antiferromagnetic
transitions around 200 K and these two phenomena are presumed to be coupled
with each other. Our studies on magnetic and dielectric properties of the
YBa1-xSrxCuFeO5 (x = 0.0, 0.25 and 0.5) show that substitution of Sr shifts
magnetic transition towards higher temperature whereas dielectric transition to
lower temperature. These results points to the fact that magnetic and
dielectric transitions get decoupled as a result of chemical pressure in form
of Sr substitution. The nature of magnetodielectric coupling changes across the
series with the presence of higher order coupling terms. Additionally in these
compounds glassy dynamics of electric dipoles is observed at low temperatures. | [
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] |
Title: Regular Intersecting Families,
Abstract: We call a family of sets intersecting, if any two sets in the family
intersect. In this paper we investigate intersecting families $\mathcal{F}$ of
$k$-element subsets of $[n]:=\{1,\ldots, n\},$ such that every element of $[n]$
lies in the same (or approximately the same) number of members of
$\mathcal{F}$. In particular, we show that we can guarantee $|\mathcal{F}| =
o({n-1\choose k-1})$ if and only if $k=o(n)$. | [
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] |
Title: Stochastic Maximum Likelihood Optimization via Hypernetworks,
Abstract: This work explores maximum likelihood optimization of neural networks through
hypernetworks. A hypernetwork initializes the weights of another network, which
in turn can be employed for typical functional tasks such as regression and
classification. We optimize hypernetworks to directly maximize the conditional
likelihood of target variables given input. Using this approach we obtain
competitive empirical results on regression and classification benchmarks. | [
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] |
Title: Magnetic resonance of rubidium atoms passing through a multi-layered transmission magnetic grating,
Abstract: We measured the magnetic resonance of rubidium atoms passing through periodic
magnetic fields generated by two types of multilayered transmission magnetic
grating. One of the gratings reported here was assembled by stacking four
layers of magnetic films so that the direction of magnetization alternated at
each level. The other grating was assembled so that the magnetization at each
level was aligned. For both types of grating, the experimental results were in
good agreement with our calculations. We studied the feasibility of extending
the frequency band of the grating and narrowing its resonance linewidth by
performing calculations. For magnetic resonance precision spectroscopy, we
conclude that the multi-layered transmission magnetic grating can generate
periodic fields with narrower linewidths at higher frequencies when a larger
number of layers is assembled at a shorter period length. Moreover, the
frequency band of this type of grating can potentially achieve frequencies of
up to hundreds of PHz. | [
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] |
Title: Fast multi-output relevance vector regression,
Abstract: This paper aims to decrease the time complexity of multi-output relevance
vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output
dimensions, M is the number of basis functions, and V<M. The experimental
results demonstrate that the proposed method is more competitive than the
existing method, with regard to computation time. MATLAB codes are available at
this http URL. | [
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] |
Title: Computing LPMLN Using ASP and MLN Solvers,
Abstract: LPMLN is a recent addition to probabilistic logic programming languages. Its
main idea is to overcome the rigid nature of the stable model semantics by
assigning a weight to each rule in a way similar to Markov Logic is defined. We
present two implementations of LPMLN, $\text{LPMLN2ASP}$ and
$\text{LPMLN2MLN}$. System $\text{LPMLN2ASP}$ translates LPMLN programs into
the input language of answer set solver $\text{CLINGO}$, and using weak
constraints and stable model enumeration, it can compute most probable stable
models as well as exact conditional and marginal probabilities. System
$\text{LPMLN2MLN}$ translates LPMLN programs into the input language of Markov
Logic solvers, such as $\text{ALCHEMY}$, $\text{TUFFY}$, and $\text{ROCKIT}$,
and allows for performing approximate probabilistic inference on LPMLN
programs. We also demonstrate the usefulness of the LPMLN systems for computing
other languages, such as ProbLog and Pearl's Causal Models, that are shown to
be translatable into LPMLN. (Under consideration for acceptance in TPLP) | [
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0
] |
Title: Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences,
Abstract: In his seminal book `The Inmates are Running the Asylum: Why High-Tech
Products Drive Us Crazy And How To Restore The Sanity' [2004, Sams
Indianapolis, IN, USA], Alan Cooper argues that a major reason why software is
often poorly designed (from a user perspective) is that programmers are in
charge of design decisions, rather than interaction designers. As a result,
programmers design software for themselves, rather than for their target
audience, a phenomenon he refers to as the `inmates running the asylum'. This
paper argues that explainable AI risks a similar fate. While the re-emergence
of explainable AI is positive, this paper argues most of us as AI researchers
are building explanatory agents for ourselves, rather than for the intended
users. But explainable AI is more likely to succeed if researchers and
practitioners understand, adopt, implement, and improve models from the vast
and valuable bodies of research in philosophy, psychology, and cognitive
science, and if evaluation of these models is focused more on people than on
technology. From a light scan of literature, we demonstrate that there is
considerable scope to infuse more results from the social and behavioural
sciences into explainable AI, and present some key results from these fields
that are relevant to explainable AI. | [
1,
0,
0,
0,
0,
0
] |
Title: Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks,
Abstract: An image is a very effective tool for conveying emotions. Many researchers
have investigated in computing the image emotions by using various features
extracted from images. In this paper, we focus on two high level features, the
object and the background, and assume that the semantic information of images
is a good cue for predicting emotion. An object is one of the most important
elements that define an image, and we find out through experiments that there
is a high correlation between the object and the emotion in images. Even with
the same object, there may be slight difference in emotion due to different
backgrounds, and we use the semantic information of the background to improve
the prediction performance. By combining the different levels of features, we
build an emotion based feed forward deep neural network which produces the
emotion values of a given image. The output emotion values in our framework are
continuous values in the 2-dimensional space (Valence and Arousal), which are
more effective than using a few number of emotion categories in describing
emotions. Experiments confirm the effectiveness of our network in predicting
the emotion of images. | [
1,
0,
0,
0,
0,
0
] |
Title: Soft Rough Graphs,
Abstract: Soft set theory and rough set theory are mathematical tools to deal with
uncertainties. In [3], authors combined these concepts and introduced soft
rough sets. In this paper, we introduce the concepts of soft rough graphs,
vertex and edge induced soft rough graphs and soft rough trees. We define some
products with examples in soft rough graphs. | [
0,
0,
1,
0,
0,
0
] |
Title: PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction,
Abstract: To date, developing a good model for early intensive care unit (ICU)
mortality prediction is still challenging. This paper presents a patient based
predictive modeling framework (PPMF) to improve the performance of ICU
mortality prediction using data collected during the first 48 hours of ICU
admission. PPMF consists of three main components verifying three related
research hypotheses. The first component captures dynamic changes of patients
status in the ICU using their time series data (e.g., vital signs and
laboratory tests). The second component is a local approximation algorithm that
classifies patients based on their similarities. The third component is a
Gradient Decent wrapper that updates feature weights according to the
classification feedback. Experiments using data from MIMICIII show that PPMF
significantly outperforms: (1) the severity score systems, namely SASP III,
APACHE IV, and MPM0III, (2) the aggregation based classifiers that utilize
summarized time series, and (3) baseline feature selection methods. | [
1,
0,
0,
0,
0,
0
] |
Title: Zeros of real random polynomials spanned by OPUC,
Abstract: Let \( \{\varphi_i\}_{i=0}^\infty \) be a sequence of orthonormal polynomials
on the unit circle with respect to a probability measure \( \mu \). We study
zero distribution of random linear combinations of the form \[
P_n(z)=\sum_{i=0}^{n-1}\eta_i\varphi_i(z), \] where \( \eta_0,\dots,\eta_{n-1}
\) are i.i.d. standard Gaussian variables. We use the Christoffel-Darboux
formula to simplify the density functions provided by Vanderbei for the
expected number real and complex of zeros of \( P_n \). From these expressions,
under the assumption that \( \mu \) is in the Nevai class, we deduce the
limiting value of these density functions away from the unit circle. Under the
mere assumption that \( \mu \) is doubling on subarcs of \( \T \) centered at
\( 1 \) and \( -1 \), we show that the expected number of real zeros of \( P_n
\) is at most \[ (2/\pi) \log n +O(1), \] and that the asymptotic equality
holds when the corresponding recurrence coefficients decay no slower than \(
n^{-(3+\epsilon)/2} \), \( \epsilon>0 \). We conclude with providing results
that estimate the expected number of complex zeros of \( P_n \) in shrinking
neighborhoods of compact subsets of \( \T \). | [
0,
0,
1,
0,
0,
0
] |
Title: Indirect observation of molecular disassociation in solid benzene at low temperatures,
Abstract: The molecular dynamics of solid benzene are extremely complex; especially
below 77 K, its inner mechanics remain mostly unexplored. Benzene is also a
prototypical molecular crystal that becomes energetically frustrated at low
temperatures and usually unusual phenomena accompanies such scenarios. We
performed dielectric constant measurements on solid benzene down to 5 K and
observed a previously unidentified minimum in the imaginary part of the
dielectric constant at Tm=17.9 K. Results obtained on deuterated solid benzene
(C6D6, where D is deuterium) show an isotope effect in the form of a shift of
the critical temperature to Tm'=18.9 K. Our findings indicate that at Tm, only
the protons without the carbon atoms continue again to undergo rotational
tunneling about the hexad axes. The deuterons appear to do the same accounting
for an indirect observation of a continued and sustained 12-body tunneling
event. We discuss how similar experiments performed on hydrogen-based molecular
crystals can be exploited to help us obtain more insight on the quantum
mechanics of many-body tunneling. | [
0,
1,
0,
0,
0,
0
] |
Title: Green's function-based control-oriented modeling of electric field for dielectrophoresis,
Abstract: In this paper, we propose a novel approach to obtaining a reliable and simple
mathematical model of a dielectrophoretic force for model-based feedback
micromanipulation. Any such model is expected to sufficiently accurately relate
the voltages (electric potentials) applied to the electrodes to the resulting
forces exerted on microparticles at given locations in the workspace. This
model also has to be computationally simple enough to be used in real time as
required by model-based feedback control. Most existing models involve solving
two- or three-dimensional mixed boundary value problems. As such, they are
usually analytically intractable and have to be solved numerically instead. A
numerical solution is, however, infeasible in real time, hence such models are
not suitable for feedback control. We present a novel approximation of the
boundary value data for which a closed-form analytical solution is feasible; we
solve a mixed boundary value problem numerically off-line only once, and based
on this solution we approximate the mixed boundary conditions by Dirichlet
boundary conditions. This way we get an approximated boundary value problem
allowing the application of the analytical framework of Green's functions. Thus
obtained closed-form analytical solution is amenable to real-time use and
closely matches the numerical solution of the original exact problem. | [
0,
1,
0,
0,
0,
0
] |
Title: Hilbert Bases and Lecture Hall Partitions,
Abstract: In the interest of finding the minimum additive generating set for the set of
$\boldsymbol{s}$-lecture hall partitions, we compute the Hilbert bases for the
$\boldsymbol{s}$-lecture hall cones in certain cases. In particular, we compute
the Hilbert bases for two well-studied families of sequences, namely the $1\mod
k$ sequences and the $\ell$-sequences. Additionally, we provide a
characterization of the Hilbert bases for $\boldsymbol{u}$-generated Gorenstein
$\boldsymbol{s}$-lecture hall cones in low dimensions. | [
0,
0,
1,
0,
0,
0
] |
Title: CLUBB-SILHS: A parameterization of subgrid variability in the atmosphere,
Abstract: This document provides a detailed overview of the CLUBB-SILHS cloud and
turbulence parameterization, including theoretical background, model equations,
closure assumptions, simulation results, comparison with other parameterization
methods, FAQs, and source code documentation. | [
0,
1,
0,
0,
0,
0
] |
Title: Relative Entropy in CFT,
Abstract: By using Araki's relative entropy, Lieb's convexity and the theory of
singular integrals, we compute the mutual information associated with free
fermions, and we deduce many results about entropies for chiral CFT's which are
embedded into free fermions, and their extensions. Such relative entropies in
CFT are here computed explicitly for the first time in a mathematical rigorous
way. Our results agree with previous computations by physicists based on
heuristic arguments; in addition we uncover a surprising connection with the
theory of subfactors, in particular by showing that a certain duality, which is
argued to be true on physical grounds, is in fact violated if the global
dimension of the conformal net is greater than $1.$ | [
0,
0,
1,
0,
0,
0
] |
Title: Approximate Supermodularity Bounds for Experimental Design,
Abstract: This work provides performance guarantees for the greedy solution of
experimental design problems. In particular, it focuses on A- and E-optimal
designs, for which typical guarantees do not apply since the mean-square error
and the maximum eigenvalue of the estimation error covariance matrix are not
supermodular. To do so, it leverages the concept of approximate supermodularity
to derive non-asymptotic worst-case suboptimality bounds for these greedy
solutions. These bounds reveal that as the SNR of the experiments decreases,
these cost functions behave increasingly as supermodular functions. As such,
greedy A- and E-optimal designs approach (1-1/e)-optimality. These results
reconcile the empirical success of greedy experimental design with the
non-supermodularity of the A- and E-optimality criteria. | [
1,
0,
1,
1,
0,
0
] |
Title: A new precision measurement of the α-decay half-life of 190Pt,
Abstract: A laboratory measurement of the $\alpha$-decay half-life of $^{190}$Pt has
been performed using a low background Frisch grid ionisation chamber. A total
amount of 216.60(17) mg of natural platinum has been measured for 75.9 days.
The resulting half-life is $(4.97\pm0.16)\times 10^{11}$ years, with a total
uncertainty of 3.2%. This number is in good agreement with the half-life
obtained using the geological comparison method. | [
0,
1,
0,
0,
0,
0
] |
Title: Online Inverse Reinforcement Learning via Bellman Gradient Iteration,
Abstract: This paper develops an online inverse reinforcement learning algorithm aimed
at efficiently recovering a reward function from ongoing observations of an
agent's actions. To reduce the computation time and storage space in reward
estimation, this work assumes that each observed action implies a change of the
Q-value distribution, and relates the change to the reward function via the
gradient of Q-value with respect to reward function parameter. The gradients
are computed with a novel Bellman Gradient Iteration method that allows the
reward function to be updated whenever a new observation is available. The
method's convergence to a local optimum is proved.
This work tests the proposed method in two simulated environments, and
evaluates the algorithm's performance under a linear reward function and a
non-linear reward function. The results show that the proposed algorithm only
requires a limited computation time and storage space, but achieves an
increasing accuracy as the number of observations grows. We also present a
potential application to robot cleaners at home. | [
1,
0,
0,
0,
0,
0
] |
Title: SchNet: A continuous-filter convolutional neural network for modeling quantum interactions,
Abstract: Deep learning has the potential to revolutionize quantum chemistry as it is
ideally suited to learn representations for structured data and speed up the
exploration of chemical space. While convolutional neural networks have proven
to be the first choice for images, audio and video data, the atoms in molecules
are not restricted to a grid. Instead, their precise locations contain
essential physical information, that would get lost if discretized. Thus, we
propose to use continuous-filter convolutional layers to be able to model local
correlations without requiring the data to lie on a grid. We apply those layers
in SchNet: a novel deep learning architecture modeling quantum interactions in
molecules. We obtain a joint model for the total energy and interatomic forces
that follows fundamental quantum-chemical principles. This includes
rotationally invariant energy predictions and a smooth, differentiable
potential energy surface. Our architecture achieves state-of-the-art
performance for benchmarks of equilibrium molecules and molecular dynamics
trajectories. Finally, we introduce a more challenging benchmark with chemical
and structural variations that suggests the path for further work. | [
0,
1,
0,
1,
0,
0
] |
Title: Generalized magnetic mirrors,
Abstract: We propose generalized magnetic mirrors that can be achieved by excitations
of sole electric resonances. Conventional approaches to obtain magnetic mirrors
rely heavily on exciting the fundamental magnetic dipoles, whereas here we
reveal that besides magnetic resonances, electric resonances of higher orders
can be also employed to obtain highly efficient magnetic mirrors. Based on the
electromagnetic duality, it is also shown that electric mirrors can be achieved
by exciting magnetic resonances. We provide direct demonstrations of the
generalized mirrors proposed in a simple system of one-dimensional periodic
array of all-dielectric wires, which may shed new light to many advanced fields
of photonics related to resonant multipolar excitations and interferences. | [
0,
1,
0,
0,
0,
0
] |
Title: Cryogenic readout for multiple VUV4 Multi-Pixel Photon Counters in liquid xenon,
Abstract: We present the performances and characterization of an array made of
S13370-3050CN (VUV4 generation) Multi-Pixel Photon Counters manufactured by
Hamamatsu and equipped with a low power consumption preamplifier operating at
liquid xenon temperature (~ 175 K). The electronics is designed for the readout
of a matrix of maximum dimension of 8 x 8 individual photosensors and it is
based on a single operational amplifier. The detector prototype presented in
this paper utilizes the Analog Devices AD8011 current feedback operational
amplifier, but other models can be used depending on the application. A biasing
correction circuit has been implemented for the gain equalization of
photosensors operating at different voltages. The results show single photon
detection capability making this device a promising choice for future
generation of large scale dark matter detectors based on liquid xenon, such as
DARWIN. | [
0,
1,
0,
0,
0,
0
] |
Title: A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting,
Abstract: One of the key technologies for future large-scale location-aware services
covering a complex of multi-story buildings --- e.g., a big shopping mall and a
university campus --- is a scalable indoor localization technique. In this
paper, we report the current status of our investigation on the use of deep
neural networks (DNNs) for scalable building/floor classification and
floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the
hierarchical nature of the building/floor estimation and floor-level
coordinates estimation of a location, we propose a new DNN architecture
consisting of a stacked autoencoder for the reduction of feature space
dimension and a feed-forward classifier for multi-label classification of
building/floor/location, on which the multi-building and multi-floor indoor
localization system based on Wi-Fi fingerprinting is built. Experimental
results for the performance of building/floor estimation and floor-level
coordinates estimation of a given location demonstrate the feasibility of the
proposed DNN-based indoor localization system, which can provide near
state-of-the-art performance using a single DNN, for the implementation with
lower complexity and energy consumption at mobile devices. | [
1,
0,
0,
1,
0,
0
] |
Title: A latent spatial factor approach for synthesizing opioid associated deaths and treatment admissions in Ohio counties,
Abstract: Background: Opioid misuse is a major public health issue in the United States
and in particular Ohio. However, the burden of the epidemic is challenging to
quantify as public health surveillance measures capture different aspects of
the problem. Here we synthesize county-level death and treatment counts to
compare the relative burden across counties and assess associations with social
environmental covariates. Methods: We construct a generalized spatial factor
model to jointly model death and treatment rates for each county. For each
outcome, we specify a spatial rates parameterization for a Poisson regression
model with spatially varying factor loadings. We use a conditional
autoregressive model to account for spatial dependence within a Bayesian
framework. Results: The estimated spatial factor was highest in the southern
and southwestern counties of the state, representing a higher burden of the
opioid epidemic. We found that relatively high rates of treatment contributed
to the factor in the southern part of the state; whereas, relatively higher
rates of death contributed in the southwest. The estimated factor was also
positively associated with the proportion of residents aged 18-64 on disability
and negatively associated with the proportion of residents reporting white
race. Conclusions: We synthesized the information in the opioid associated
death and treatment counts through a spatial factor model to estimate a latent
factor representing the consensus between the two surveillance measures. We
believe this framework provides a coherent approach to describe the epidemic
while leveraging information from multiple surveillance measures. | [
0,
0,
0,
1,
0,
0
] |
Title: The Cost of Transportation : Spatial Analysis of US Fuel Prices,
Abstract: The geography of fuel prices has many various implications, from its
significant impact on accessibility to being an indicator of territorial equity
and transportation policy. In this paper, we study the spatio-temporal patterns
of fuel price in the US at a very high resolution using a newly constructed
dataset collecting daily oil prices for two months, on a significant proportion
of US gas facilities. These data have been collected using a
specifically-designed large scale data crawling technology that we describe. We
study the influence of socio-economic variables, by using complementary
methods: Geographically Weighted Regression to take into account spatial
non-stationarity, and linear econometric modeling to condition at the state and
test county level characteristics. The former yields an optimal spatial range
roughly corresponding to stationarity scale, and significant influence of
variables such as median income or wage per job, with a non-simple spatial
behavior that confirms the importance of geographical particularities. On the
other hand, multi-level modeling reveals a strong state fixed effect, while
county specific characteristics still have significant impact. Through the
combination of such methods, we unveil the superposition of a governance
process with a local socio- economical spatial process. We discuss one
important application that is the elaboration of locally parametrized
car-regulation policies. | [
0,
1,
0,
1,
0,
0
] |
Title: Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors,
Abstract: Shape priors have been widely utilized in medical image segmentation to
improve segmentation accuracy and robustness. A major way to encode such a
prior shape model is to use a mesh representation, which is prone to causing
self-intersection or mesh folding. Those problems require complex and expensive
algorithms to mitigate. In this paper, we propose a novel shape prior directly
embedded in the voxel grid space, based on gradient vector flows of a
pre-segmentation. The flexible and powerful prior shape representation is ready
to be extended to simultaneously segmenting multiple interacting objects with
minimum separation distance constraint. The problem is formulated as a Markov
random field problem whose exact solution can be efficiently computed with a
single minimum s-t cut in an appropriately constructed graph. The proposed
algorithm is validated on two multi-object segmentation applications: the brain
tissue segmentation in MRI images, and the bladder/prostate segmentation in CT
images. Both sets of experiments show superior or competitive performance of
the proposed method to other state-of-the-art methods. | [
1,
0,
0,
0,
0,
0
] |
Title: Discovery of water at high spectral resolution in the atmosphere of 51 Peg b,
Abstract: We report the detection of water absorption features in the dayside spectrum
of the first-known hot Jupiter, 51 Peg b, confirming the star-planet system to
be a double-lined spectroscopic binary. We used high-resolution (R~100,000),
3.2 micron spectra taken with CRIRES/VLT to trace the radial-velocity shift of
the water features in the planet's dayside atmosphere during 4 hours of its
4.23-day orbit after superior conjunction. We detect the signature of molecular
absorption by water at a significance of 5.6 sigma at a systemic velocity of
Vsys=-33+/-2 km/s, coincident with the host star, with a corresponding orbital
velocity Kp = 133^+4.3_-3.5 km/s. This translates directly to a planet mass of
Mp=0.476^+0.032_-0.031MJ, placing it at the transition boundary between Jovian
and Neptunian worlds. We determine upper and lower limits on the orbital
inclination of the system of 70<i (deg)<82.2. We also provide an updated
orbital solution for 51 Peg b, using an extensive set of 639 stellar radial
velocities measured between 1994 and 2013, finding no significant evidence of
an eccentric orbit. We find no evidence of significant absorption or emission
from other major carbon-bearing molecules of the planet, including methane and
carbon dioxide. The atmosphere is non-inverted in the temperature-pressure
region probed by these observations. The deepest absorption lines reach an
observed relative contrast of 0.9x10^-3 with respect to the host star continuum
flux, at an angular separation of 3 milliarcseconds. This work is consistent
with a previous tentative report of K-band molecular absorption for 51 Peg b by
Brogi et al. (2013). | [
0,
1,
0,
0,
0,
0
] |
Title: A strengthened inequality of Alon-Babai-Suzuki's conjecture on set systems with restricted intersections modulo p,
Abstract: Let $K=\{k_1,k_2,\ldots,k_r\}$ and $L=\{l_1,l_2,\ldots,l_s\}$ be disjoint
subsets of $\{0,1,\ldots,p-1\}$, where $p$ is a prime and
$A=\{A_1,A_2,\ldots,A_m\}$ be a family of subsets of $[n]$ such that
$|A_i|\pmod{p}\in K$ for all $A_i\in A$ and $|A_i\cap A_j|\pmod{p}\in L$ for
$i\ne j$. In 1991, Alon, Babai and Suzuki conjectured that if $n\geq
s+\max_{1\leq i\leq r} k_i$, then $|A|\leq {n\choose s}+{n\choose
s-1}+\cdots+{n\choose s-r+1}$. In 2000, Qian and Ray-Chaudhuri proved the
conjecture under the condition $n\geq 2s-r$. In 2015, Hwang and Kim verified
the conjecture of Alon, Babai and Suzuki.
In this paper, we will prove that if $n\geq 2s-2r+1$ or $n\geq s+\max_{1\leq
i\leq r}k_i$, then \[ |A|\leq{n-1\choose s}+{n-1\choose s-1}+\cdots+{n-1\choose
s-2r+1}. \] This result strengthens the upper bound of Alon, Babai and Suzuki's
conjecture when $n\geq 2s-2$. | [
0,
0,
1,
0,
0,
0
] |
Title: Traditional and Heavy-Tailed Self Regularization in Neural Network Models,
Abstract: Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep
Neural Networks (DNNs), including both production quality, pre-trained models
such as AlexNet and Inception, and smaller models trained from scratch, such as
LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly
indicate that the empirical spectral density (ESD) of DNN layer matrices
displays signatures of traditionally-regularized statistical models, even in
the absence of exogenously specifying traditional forms of regularization, such
as Dropout or Weight Norm constraints. Building on recent results in RMT, most
notably its extension to Universality classes of Heavy-Tailed matrices, we
develop a theory to identify \emph{5+1 Phases of Training}, corresponding to
increasing amounts of \emph{Implicit Self-Regularization}. For smaller and/or
older DNNs, this Implicit Self-Regularization is like traditional Tikhonov
regularization, in that there is a `size scale' separating signal from noise.
For state-of-the-art DNNs, however, we identify a novel form of
\emph{Heavy-Tailed Self-Regularization}, similar to the self-organization seen
in the statistical physics of disordered systems. This implicit
Self-Regularization can depend strongly on the many knobs of the training
process. By exploiting the generalization gap phenomena, we demonstrate that we
can cause a small model to exhibit all 5+1 phases of training simply by
changing the batch size. | [
1,
0,
0,
1,
0,
0
] |
Title: Interfacial Mechanical Behaviors in Carbon Nanotube Assemblies,
Abstract: Interface widely exists in carbon nanotube (CNT) assembly materials, taking
place at different length scales. It determines severely the mechanical
properties of these assembly materials. Here I assess the mechanical properties
of individual CNTs and CNT bundles, the inter-layer or inter-shell mechanics in
multi-walled CNTs, the shear properties between adjacent CNTs, and the
assembly-dependent mechanical and multifunctional properties of macroscopic CNT
fibers and films. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning a Latent Space of Multitrack Measures,
Abstract: Discovering and exploring the underlying structure of multi-instrumental
music using learning-based approaches remains an open problem. We extend the
recent MusicVAE model to represent multitrack polyphonic measures as vectors in
a latent space. Our approach enables several useful operations such as
generating plausible measures from scratch, interpolating between measures in a
musically meaningful way, and manipulating specific musical attributes. We also
introduce chord conditioning, which allows all of these operations to be
performed while keeping harmony fixed, and allows chords to be changed while
maintaining musical "style". By generating a sequence of measures over a
predefined chord progression, our model can produce music with convincing
long-term structure. We demonstrate that our latent space model makes it
possible to intuitively control and generate musical sequences with rich
instrumentation (see this https URL for generated audio). | [
0,
0,
0,
1,
0,
0
] |
Title: Multimodal Affect Analysis for Product Feedback Assessment,
Abstract: Consumers often react expressively to products such as food samples, perfume,
jewelry, sunglasses, and clothing accessories. This research discusses a
multimodal affect recognition system developed to classify whether a consumer
likes or dislikes a product tested at a counter or kiosk, by analyzing the
consumer's facial expression, body posture, hand gestures, and voice after
testing the product. A depth-capable camera and microphone system - Kinect for
Windows - is utilized. An emotion identification engine has been developed to
analyze the images and voice to determine affective state of the customer. The
image is segmented using skin color and adaptive threshold. Face, body and
hands are detected using the Haar cascade classifier. Canny edges are
identified and the lip, body and hand contours are extracted using spatial
filtering. Edge count and orientation around the mouth, cheeks, eyes,
shoulders, fingers and the location of the edges are used as features.
Classification is done by an emotion template mapping algorithm and training a
classifier using support vector machines. The real-time performance, accuracy
and feasibility for multimodal affect recognition in feedback assessment are
evaluated. | [
1,
0,
0,
0,
0,
0
] |
Title: Distributed Learning for Cooperative Inference,
Abstract: We study the problem of cooperative inference where a group of agents
interact over a network and seek to estimate a joint parameter that best
explains a set of observations. Agents do not know the network topology or the
observations of other agents. We explore a variational interpretation of the
Bayesian posterior density, and its relation to the stochastic mirror descent
algorithm, to propose a new distributed learning algorithm. We show that, under
appropriate assumptions, the beliefs generated by the proposed algorithm
concentrate around the true parameter exponentially fast. We provide explicit
non-asymptotic bounds for the convergence rate. Moreover, we develop explicit
and computationally efficient algorithms for observation models belonging to
exponential families. | [
1,
0,
1,
1,
0,
0
] |
Title: Selection of training populations (and other subset selection problems) with an accelerated genetic algorithm (STPGA: An R-package for selection of training populations with a genetic algorithm),
Abstract: Optimal subset selection is an important task that has numerous algorithms
designed for it and has many application areas. STPGA contains a special
genetic algorithm supplemented with a tabu memory property (that keeps track of
previously tried solutions and their fitness for a number of iterations), and
with a regression of the fitness of the solutions on their coding that is used
to form the ideal estimated solution (look ahead property) to search for
solutions of generic optimal subset selection problems. I have initially
developed the programs for the specific problem of selecting training
populations for genomic prediction or association problems, therefore I give
discussion of the theory behind optimal design of experiments to explain the
default optimization criteria in STPGA, and illustrate the use of the programs
in this endeavor. Nevertheless, I have picked a few other areas of application:
supervised and unsupervised variable selection based on kernel alignment,
supervised variable selection with design criteria, influential observation
identification for regression, solving mixed integer quadratic optimization
problems, balancing gains and inbreeding in a breeding population. Some of
these illustrations pertain new statistical approaches. | [
0,
0,
0,
1,
0,
0
] |
Title: CERES in Propositional Proof Schemata,
Abstract: Cut-elimination is one of the most famous problems in proof theory, and it
was defined and solved for first-order sequent calculus by Gentzen in his
celebrated Hauptsatz. Ceres is a different cut-elimination algorithm for first-
and higher-order classical logic. Ceres was extended to proof schemata, which
are templates for usual first-order proofs, with parameters for natural
numbers. However, while Ceres is known to be a complete cut-elimination
algorithm for first-order logic, it is not clear whether this holds for
first-order schemata too: given in input a proof schema with cuts, does Ceres
always produce a schema for a cut-free proof? The difficult step is finding and
representing an appropriate refutation schema for the characteristic term
schema of a proof schema. In this thesis, we progress in solving this problem
by restricting Ceres to propositional schemata, which are templates for
propositional proofs. By limiting adequately the expressivity of propositional
schemata and proof schemata, we aim at providing a version of schematic Ceres
which is a complete cut-elimination algorithm for propositional schemata. We
focus on one particular step of Ceres: resolution refutation schemata. First,
we prove that by naively adapting Ceres for first-order schemata to our case,
we end up with an incomplete algorithm. Then, we modify slightly the concept of
resolution refutation schema: to refute a clause set, first we bring it to a
generic form, and then we use a fixed refutation of that generic clause set.
Our variation of schematic Ceres is the first step towards completeness with
respect to propositional schemata. | [
1,
0,
1,
0,
0,
0
] |
Title: A Schur decomposition reveals the richness of structure in homogeneous, isotropic turbulence as a consequence of localised shear,
Abstract: An improved understanding of turbulence is essential for the effective
modelling and control of industrial and geophysical processes. Homogeneous,
isotropic turbulence (HIT) is the archetypal field for developing turbulence
physics theory. Based on the Schur transform, we introduce an additive
decomposition of the velocity gradient tensor into a normal part (containing
the eigenvalues) and a non-normal or shear-related tensor. We re-interrogate
some key properties of HIT and show that the the tendency of the flow to form
disc-like structures is not a property of the normal tensor; it emerges from an
interaction with the non-normality. Also, the alignment between the vorticity
vector and the second eigenvector of the strain tensor is another consequence
of local shear processes. | [
0,
1,
0,
0,
0,
0
] |
Title: Parametrizing filters of a CNN with a GAN,
Abstract: It is commonly agreed that the use of relevant invariances as a good
statistical bias is important in machine-learning. However, most approaches
that explicitly incorporate invariances into a model architecture only make use
of very simple transformations, such as translations and rotations. Hence,
there is a need for methods to model and extract richer transformations that
capture much higher-level invariances. To that end, we introduce a tool
allowing to parametrize the set of filters of a trained convolutional neural
network with the latent space of a generative adversarial network. We then show
that the method can capture highly non-linear invariances of the data by
visualizing their effect in the data space. | [
1,
0,
0,
1,
0,
0
] |
Title: Monolayer FeSe on SrTiO$_3$,
Abstract: Epitaxial engineering of solid-state heterointerfaces is a leading avenue to
realizing enhanced or novel electronic states of matter. As a recent example,
bulk FeSe is an unconventional superconductor with a modest transition
temperature ($T_c$) of 9 K. When a single atomic layer of FeSe is grown on
SrTiO$_3$, however, its $T_c$ can skyrocket by an order of magnitude to 65 K or
109 K. Since this discovery in 2012, efforts to reproduce, understand, and
extend these findings continue to draw both excitement and scrutiny. In this
review, we first present a critical survey of experimental measurements
performed using a wide range of techniques. We then turn to the open question
of microscopic mechanisms of superconductivity. We examine contrasting
indications for both phononic (conventional) and magnetic/orbital
(unconventional) means of electron pairing, and speculations about whether they
could work cooperatively to boost $T_c$ in a monolayer of FeSe. | [
0,
1,
0,
0,
0,
0
] |
Title: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models,
Abstract: Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many
related tasks (large $K$) under a high-dimensional (large $p$) situation is an
important task. Most previous studies for the joint estimation of multiple
sGGMs rely on penalized log-likelihood estimators that involve expensive and
difficult non-smooth optimizations. We propose a novel approach, FASJEM for
\underline{fa}st and \underline{s}calable \underline{j}oint
structure-\underline{e}stimation of \underline{m}ultiple sGGMs at a large
scale. As the first study of joint sGGM using the Elementary Estimator
framework, our work has three major contributions: (1) We solve FASJEM through
an entry-wise manner which is parallelizable. (2) We choose a proximal
algorithm to optimize FASJEM. This improves the computational efficiency from
$O(Kp^3)$ to $O(Kp^2)$ and reduces the memory requirement from $O(Kp^2)$ to
$O(K)$. (3) We theoretically prove that FASJEM achieves a consistent estimation
with a convergence rate of $O(\log(Kp)/n_{tot})$. On several synthetic and four
real-world datasets, FASJEM shows significant improvements over baselines on
accuracy, computational complexity, and memory costs. | [
1,
0,
0,
1,
0,
0
] |
Title: Quantum Spin Liquids Unveil the Genuine Mott State,
Abstract: The Widom line identifies the locus in the phase diagram where a
supercritical gas crosses over from gas-like to a more liquid-like behavior. A
similar transition exists in correlated electron liquids, where the interplay
of Coulomb repulsion, bandwidth and temperature triggers between the Mott
insulating state and an incoherent conduction regime. Here we explore the
electrodynamic response of three organic quantum spin liquids with different
degrees of effective correlation, where the absence of magnetic order enables
unique insight into the nature of the genuine Mott state down to the most
relevant low-temperature region. Combining optical spectroscopy with
pressure-dependent dc transport and theoretical calculations, we succeeded to
construct a phase diagram valid for all Mott insulators on a quantitative
scale. In the vicinity of the low-temperature phase boundary, we discover
metallic fluctuations within the Mott gap, exhibiting enhanced absorption upon
cooling that is not present in antiferromagnetic Mott insulators. Our findings
reveal the phase coexistence region and Pomeranchuk-like anomaly of the Mott
transition, previously predicted but never observed. | [
0,
1,
0,
0,
0,
0
] |
Title: Parameter and State Estimation in Queues and Related Stochastic Models: A Bibliography,
Abstract: This is an annotated bibliography on estimation and inference results for
queues and related stochastic models. The purpose of this document is to
collect and categorise works in the field, allowing for researchers and
practitioners to explore the various types of results that exist. This
bibliography attempts to include all known works that satisfy both of these
requirements: -Works that deal with queueing models. -Works that contain
contributions related to the methodology of parameter estimation, state
estimation, hypothesis testing, confidence interval and/or actual datasets of
application areas. Our attempt is to make this bibliography exhaustive, yet
there are possibly some papers that we have missed. As it is updated
continuously, additions and comments are welcomed. The sections below
categorise the works based on several categories. A single paper may appear in
several categories simultaneously. The final section lists all works in
chronological order along with short descriptions of the contributions. This
bibliography is maintained at
this http URL and may be cited as such.
We welcome additions and corrections. | [
1,
0,
1,
1,
0,
0
] |
Title: Towards a Social Virtual Reality Learning Environment in High Fidelity,
Abstract: Virtual Learning Environments (VLEs) are spaces designed to educate students
remotely via online platforms. Although traditional VLEs such as iSocial have
shown promise in educating students, they offer limited immersion that
diminishes learning effectiveness. This paper outlines a virtual reality
learning environment (VRLE) over a high-speed network, which promotes
educational effectiveness and efficiency via our creation of flexible content
and infrastructure which meet established VLE standards with improved
immersion. This paper further describes our implementation of multiple learning
modules developed in High Fidelity, a "social VR" platform. Our experiment
results show that the VR mode of content delivery better stimulates the
generalization of lessons to the real world than non-VR lessons and provides
improved immersion when compared to an equivalent desktop version. | [
1,
0,
0,
0,
0,
0
] |
Title: L2 Regularization versus Batch and Weight Normalization,
Abstract: Batch Normalization is a commonly used trick to improve the training of deep
neural networks. These neural networks use L2 regularization, also called
weight decay, ostensibly to prevent overfitting. However, we show that L2
regularization has no regularizing effect when combined with normalization.
Instead, regularization has an influence on the scale of weights, and thereby
on the effective learning rate. We investigate this dependence, both in theory,
and experimentally. We show that popular optimization methods such as ADAM only
partially eliminate the influence of normalization on the learning rate. This
leads to a discussion on other ways to mitigate this issue. | [
1,
0,
0,
1,
0,
0
] |
Title: Bitwise Operations of Cellular Automaton on Gray-scale Images,
Abstract: Cellular Automata (CA) theory is a discrete model that represents the state
of each of its cells from a finite set of possible values which evolve in time
according to a pre-defined set of transition rules. CA have been applied to a
number of image processing tasks such as Convex Hull Detection, Image Denoising
etc. but mostly under the limitation of restricting the input to binary images.
In general, a gray-scale image may be converted to a number of different binary
images which are finally recombined after CA operations on each of them
individually. We have developed a multinomial regression based weighed
summation method to recombine binary images for better performance of CA based
Image Processing algorithms. The recombination algorithm is tested for the
specific case of denoising Salt and Pepper Noise to test against standard
benchmark algorithms such as the Median Filter for various images and noise
levels. The results indicate several interesting invariances in the application
of the CA, such as the particular noise realization and the choice of
sub-sampling of pixels to determine recombination weights. Additionally, it
appears that simpler algorithms for weight optimization which seek local minima
work as effectively as those that seek global minima such as Simulated
Annealing. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems,
Abstract: Recently, deep learning approaches with various network architectures have
achieved significant performance improvement over existing iterative
reconstruction methods in various imaging problems. However, it is still
unclear why these deep learning architectures work for specific inverse
problems. To address these issues, here we show that the long-searched-for
missing link is the convolution framelets for representing a signal by
convolving local and non-local bases. The convolution framelets was originally
developed to generalize the theory of low-rank Hankel matrix approaches for
inverse problems, and this paper further extends the idea so that we can obtain
a deep neural network using multilayer convolution framelets with perfect
reconstruction (PR) under rectilinear linear unit nonlinearity (ReLU). Our
analysis also shows that the popular deep network components such as residual
block, redundant filter channels, and concatenated ReLU (CReLU) do indeed help
to achieve the PR, while the pooling and unpooling layers should be augmented
with high-pass branches to meet the PR condition. Moreover, by changing the
number of filter channels and bias, we can control the shrinkage behaviors of
the neural network. This discovery leads us to propose a novel theory for deep
convolutional framelets neural network. Using numerical experiments with
various inverse problems, we demonstrated that our deep convolution framelets
network shows consistent improvement over existing deep architectures.This
discovery suggests that the success of deep learning is not from a magical
power of a black-box, but rather comes from the power of a novel signal
representation using non-local basis combined with data-driven local basis,
which is indeed a natural extension of classical signal processing theory. | [
1,
0,
0,
1,
0,
0
] |
Title: Enhanced bacterial swimming speeds in macromolecular polymer solutions,
Abstract: The locomotion of swimming bacteria in simple Newtonian fluids can
successfully be described within the framework of low Reynolds number
hydrodynamics. The presence of polymers in biofluids generally increases the
viscosity, which is expected to lead to slower swimming for a constant
bacterial motor torque. Surprisingly, however, several experiments have shown
that bacterial speeds increase in polymeric fluids, and there is no clear
understanding why. Therefore we perform extensive coarse-grained simulations of
a bacterium swimming in explicitly modeled solutions of macromolecular polymers
of different lengths and densities. We observe an increase of up to 60% in
swimming speed with polymer density and demonstrate that this is due to a
depletion of polymers in the vicinity of the bacterium leading to an effective
slip. However this in itself cannot predict the large increase in swimming
velocity: coupling to the chirality of the bacterial flagellum is also
necessary. | [
0,
1,
0,
0,
0,
0
] |
Title: An Algebraic Treatment of Recursion,
Abstract: I review the three principal methods to assign meaning to recursion in
process algebra: the denotational, the operational and the algebraic approach,
and I extend the latter to unguarded recursion. | [
1,
0,
0,
0,
0,
0
] |
Title: On Approximation for Fractional Stochastic Partial Differential Equations on the Sphere,
Abstract: This paper gives the exact solution in terms of the Karhunen-Loève
expansion to a fractional stochastic partial differential equation on the unit
sphere $\mathbb{S}^{2}\subset \mathbb{R}^{3}$ with fractional Brownian motion
as driving noise and with random initial condition given by a fractional
stochastic Cauchy problem. A numerical approximation to the solution is given
by truncating the Karhunen-Loève expansion. We show the convergence rates
of the truncation errors in degree and the mean square approximation errors in
time. Numerical examples using an isotropic Gaussian random field as initial
condition and simulations of evolution of cosmic microwave background (CMB) are
given to illustrate the theoretical results. | [
0,
0,
1,
1,
0,
0
] |
Title: Learning Structural Node Embeddings Via Diffusion Wavelets,
Abstract: Nodes residing in different parts of a graph can have similar structural
roles within their local network topology. The identification of such roles
provides key insight into the organization of networks and can be used for a
variety of machine learning tasks. However, learning structural representations
of nodes is a challenging problem, and it has typically involved manually
specifying and tailoring topological features for each node. In this paper, we
develop GraphWave, a method that represents each node's network neighborhood
via a low-dimensional embedding by leveraging heat wavelet diffusion patterns.
Instead of training on hand-selected features, GraphWave learns these
embeddings in an unsupervised way. We mathematically prove that nodes with
similar network neighborhoods will have similar GraphWave embeddings even
though these nodes may reside in very different parts of the network, and our
method scales linearly with the number of edges. Experiments in a variety of
different settings demonstrate GraphWave's real-world potential for capturing
structural roles in networks, and our approach outperforms existing
state-of-the-art baselines in every experiment, by as much as 137%. | [
1,
0,
0,
1,
0,
0
] |
Title: Excitonic mass gap in uniaxially strained graphene,
Abstract: We study the conditions for spontaneously generating an excitonic mass gap
due to Coulomb interactions between anisotropic Dirac fermions in uniaxially
strained graphene. The mass gap equation is realized as a self-consistent
solution for the self-energy within the Hartree-Fock mean-field and static
random phase approximations. It depends not only on momentum, due to the
long-range nature of the interaction, but also on the velocity anisotropy
caused by the presence of uniaxial strain. We solve the nonlinear integral
equation self-consistently by performing large scale numerical calculations on
variable grid sizes. We evaluate the mass gap at the charge neutrality (Dirac)
point as a function of the dimensionless coupling constant and anisotropy
parameter. We also obtain the phase diagram of the critical coupling, at which
the gap becomes finite, against velocity anisotropy. Our numerical study
indicates that with an increase in uniaxial strain in graphene the strength of
critical coupling decreases, which suggests anisotropy supports formation of
excitonic mass gap in graphene. | [
0,
1,
0,
0,
0,
0
] |
Title: On spectral properties of high-dimensional spatial-sign covariance matrices in elliptical distributions with applications,
Abstract: Spatial-sign covariance matrix (SSCM) is an important substitute of sample
covariance matrix (SCM) in robust statistics. This paper investigates the SSCM
on its asymptotic spectral behaviors under high-dimensional elliptical
populations, where both the dimension $p$ of observations and the sample size
$n$ tend to infinity with their ratio $p/n\to c\in (0, \infty)$. The empirical
spectral distribution of this nonparametric scatter matrix is shown to converge
in distribution to a generalized Marčenko-Pastur law. Beyond this, a new
central limit theorem (CLT) for general linear spectral statistics of the SSCM
is also established. For polynomial spectral statistics, explicit formulae of
the limiting mean and covarance functions in the CLT are provided. The derived
results are then applied to an estimation procedure and a test procedure for
the spectrum of the shape component of population covariance matrices. | [
0,
0,
1,
1,
0,
0
] |
Title: Decoupled Potential Integral Equations for Electromagnetic Scattering from Dielectric Objects,
Abstract: Recent work on developing novel integral equation formulations has involved
using potentials as opposed to fields. This is a consequence of the additional
flexibility offered by using potentials to develop well conditioned systems.
Most of the work in this arena has wrestled with developing this formulation
for perfectly conducting objects (Vico et al., 2014 and Liu et al., 2015), with
recent effort made to addressing similar problems for dielectrics (Li et al.,
2017). In this paper, we present well-conditioned decoupled potential integral
equation (DPIE) formulated for electromagnetic scattering from homogeneous
dielectric objects, a fully developed version of that presented in the
conference communication (Li et al., 2017). The formulation is based on
boundary conditions derived for decoupled boundary conditions on the scalar and
vector potentials. The resulting DPIE is the second kind integral equation, and
does not suffer from either low frequency or dense mesh breakdown. Analytical
properties of the DPIE are studied. Results on the sphere analysis are provided
to demonstrate the conditioning and spectrum of the resulting linear system. | [
0,
1,
0,
0,
0,
0
] |
Title: High-field transport properties of a P-doped BaFe2As2 film on technical substrate,
Abstract: High temperature (high-Tc) superconductors like cuprates have superior
critical current properties in magnetic fields over other superconductors.
However, superconducting wires for high-field-magnet applications are still
dominated by low-Tc Nb3Sn due probably to cost and processing issues. The
recent discovery of a second class of high-Tc materials, Fe-based
superconductors, may provide another option for high-field-magnet wires. In
particular, AEFe2As2 (AE: Alkali earth elements, AE-122) is one of the best
candidates for high-field-magnet applications because of its high upper
critical field, Hc2, moderate Hc2 anisotropy, and intermediate Tc. Here we
report on in-field transport properties of P-doped BaFe2As2 (Ba-122) thin films
grown on technical substrates (i.e., biaxially textured oxides templates on
metal tapes) by pulsed laser deposition. The P-doped Ba-122 coated conductor
sample exceeds a transport Jc of 10^5 A/cm^2 at 15 T for both major
crystallographic directions of the applied magnetic field, which is favourable
for practical applications. Our P-doped Ba-122 coated conductors show a
superior in-field Jc over MgB2 and NbTi, and a comparable level to Nb3Sn above
20 T. By analysing the E-J curves for determining Jc, a non-Ohmic linear
differential signature is observed at low field due to flux flow along the
grain boundaries. However, grain boundaries work as flux pinning centres as
demonstrated by the pinning force analysis. | [
0,
1,
0,
0,
0,
0
] |
Title: Construction of and efficient sampling from the simplicial configuration model,
Abstract: Simplicial complexes are now a popular alternative to networks when it comes
to describing the structure of complex systems, primarily because they encode
multi-node interactions explicitly. With this new description comes the need
for principled null models that allow for easy comparison with empirical data.
We propose a natural candidate, the simplicial configuration model. The core of
our contribution is an efficient and uniform Markov chain Monte Carlo sampler
for this model. We demonstrate its usefulness in a short case study by
investigating the topology of three real systems and their randomized
counterparts (using their Betti numbers). For two out of three systems, the
model allows us to reject the hypothesis that there is no organization beyond
the local scale. | [
0,
1,
1,
1,
0,
0
] |
Title: Local and 2-local derivations and automorphisms on simple Leibniz algebras,
Abstract: The present paper is devoted to local and 2-local derivations and
automorphism of complex finite-dimensional simple Leibniz algebras. We prove
that all local derivations and 2-local derivations on a finite-dimensional
complex simple Leibniz algebra are automatically derivations. We show that
nilpotent Leibniz algebras as a rule admit local derivations and 2-local
derivations which are not derivations. Further we consider automorphisms of
simple Leibniz algebras. We prove that every 2-local automorphism on a complex
finite-dimensional simple Leibniz algebra is an automorphism and show that
nilpotent Leibniz algebras admit 2-local automorphisms which are not
automorphisms. A similar problem concerning local automorphism on simple
Leibniz algebras is reduced to the case of simple Lie algebras. | [
0,
0,
1,
0,
0,
0
] |
Title: RodFIter: Attitude Reconstruction from Inertial Measurement by Functional Iteration,
Abstract: Rigid motion computation or estimation is a cornerstone in numerous fields.
Attitude computation can be achieved by integrating the angular velocity
measured by gyroscopes, the accuracy of which is crucially important for the
dead-reckoning inertial navigation. The state-of-the-art attitude algorithms
have unexceptionally relied on the simplified differential equation of the
rotation vector to obtain the attitude. This paper proposes a Functional
Iteration technique with the Rodrigues vector (named the RodFIter method) to
analytically reconstruct the attitude from gyroscope measurements. The RodFIter
method is provably exact in reconstructing the incremental attitude as long as
the angular velocity is exact. Notably, the Rodrigues vector is analytically
obtained and can be used to update the attitude over the considered time
interval. The proposed method gives birth to an ultimate attitude algorithm
scheme that can be naturally extended to the general rigid motion computation.
It is extensively evaluated under the attitude coning motion and compares
favorably in accuracy with the mainstream attitude algorithms. This work is
believed having eliminated the long-standing theoretical barrier in exact
motion integration from inertial measurements. | [
1,
0,
0,
0,
0,
0
] |
Title: Influence Networks in International Relations,
Abstract: Measuring influence and determining what drives it are persistent questions
in political science and in network analysis more generally. Herein we focus on
the domain of international relations. Our major substantive question is: How
can we determine what characteristics make an actor influential? To address the
topic of influence, we build on a multilinear tensor regression framework
(MLTR) that captures influence relationships using a tensor generalization of a
vector autoregression model. Influence relationships in that approach are
captured in a pair of n x n matrices and provide measurements of how the
network actions of one actor may influence the future actions of another. A
limitation of the MLTR and earlier latent space approaches is that there are no
direct mechanisms through which to explain why a certain actor is more or less
influential than others. Our new framework, social influence regression,
provides a way to statistically model the influence of one actor on another as
a function of characteristics of the actors. Thus we can move beyond just
estimating that an actor influences another to understanding why. To highlight
the utility of this approach, we apply it to studying monthly-level conflictual
events between countries as measured through the Integrated Crisis Early
Warning System (ICEWS) event data project. | [
0,
1,
0,
1,
0,
0
] |
Title: Incorporating Feedback into Tree-based Anomaly Detection,
Abstract: Anomaly detectors are often used to produce a ranked list of statistical
anomalies, which are examined by human analysts in order to extract the actual
anomalies of interest. Unfortunately, in realworld applications, this process
can be exceedingly difficult for the analyst since a large fraction of
high-ranking anomalies are false positives and not interesting from the
application perspective. In this paper, we aim to make the analyst's job easier
by allowing for analyst feedback during the investigation process. Ideally, the
feedback influences the ranking of the anomaly detector in a way that reduces
the number of false positives that must be examined before discovering the
anomalies of interest. In particular, we introduce a novel technique for
incorporating simple binary feedback into tree-based anomaly detectors. We
focus on the Isolation Forest algorithm as a representative tree-based anomaly
detector, and show that we can significantly improve its performance by
incorporating feedback, when compared with the baseline algorithm that does not
incorporate feedback. Our technique is simple and scales well as the size of
the data increases, which makes it suitable for interactive discovery of
anomalies in large datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Towards An Adaptive Compliant Aerial Manipulator for Contact-Based Interaction,
Abstract: As roles for unmanned aerial vehicles (UAV) continue to diversify, the
ability to sense and interact closely with the environment becomes increasingly
important. Within this paper we report on the initial flight tests of a novel
adaptive compliant actuator which will allow a UAV to carry out such tasks as
the "pick and placement" of remote sensors, structural testing and
contact-based inspection. Three key results are discussed and presented; the
ability to physically compensate impact forces or apply interaction forces by
the UAV through the use of the active compliant manipulator; to be able to
tailor these forces through tuning of the manipulator controller gains; and the
ability to apply a rapid series of physical pulses in order to excite remotely
placed sensors, e.g. vibration sensors. The paper describes the overall system
requirements and system modelling considerations which have driven the concept
through to flight testing. A series of over sixty flight tests have been used
to generate initial results which clearly demonstrate the potential of this new
type of compliant aerial actuator. Results are discussed in line with potential
applications; and a series of future flight tests are described which will
enable us to refine and characterise the overall system. | [
1,
0,
0,
0,
0,
0
] |
Title: On the number of inequivalent Gabidulin codes,
Abstract: Maximum rank-distance (MRD) codes are extremal codes in the space of $m\times
n$ matrices over a finite field, equipped with the rank metric. Up to
generalizations, the classical examples of such codes were constructed in the
1970s and are today known as Gabidulin codes. Motivated by several recent
approaches to construct MRD codes that are inequivalent to Gabidulin codes, we
study the equivalence issue for Gabidulin codes themselves. This shows in
particular that the family of Gabidulin codes already contains a huge subset of
MRD codes that are pairwise inequivalent, provided that $2\le m\le n-2$. | [
1,
0,
1,
0,
0,
0
] |
Title: A Connection Between Mixing and Kac's Chaos,
Abstract: The Boltzmann equation is an integro-differential equation which describes
the density function of the distribution of the velocities of the molecules of
dilute monoatomic gases under the assumption that the energy is only
transferred via collisions between the molecules. In 1956 Kac studied the
Boltzmann equation and defined a property of the density function that he
called the "Boltzmann property" which describes the behavior of the density
function at a given fixed time as the number of particles tends to infinity.
The Boltzmann property has been studied extensively since then, and now it is
simply called chaos, or Kac's chaos. On the other hand, in ergodic theory,
chaos usually refers to the mixing properties of a dynamical system as time
tends to infinity. A relationship is derived between Kac's chaos and the notion
of mixing. | [
0,
0,
1,
0,
0,
0
] |
Title: The aCORN Backscatter-Suppressed Beta Spectrometer,
Abstract: Backscatter of electrons from a beta spectrometer, with incomplete energy
deposition, can lead to undesirable effects in many types of experiments. We
present and discuss the design and operation of a backscatter-suppressed beta
spectrometer that was developed as part of a program to measure the
electron-antineutrino correlation coefficient in neutron beta decay (aCORN). An
array of backscatter veto detectors surrounds a plastic scintillator beta
energy detector. The spectrometer contains an axial magnetic field gradient, so
electrons are efficiently admitted but have a low probability for escaping back
through the entrance after backscattering. The design, construction,
calibration, and performance of the spectrometer are discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: Deterministic Browser,
Abstract: Timing attacks have been a continuous threat to users' privacy in modern
browsers. To mitigate such attacks, existing approaches, such as Tor Browser
and Fermata, add jitters to the browser clock so that an attacker cannot
accurately measure an event. However, such defenses only raise the bar for an
attacker but do not fundamentally mitigate timing attacks, i.e., it just takes
longer than previous to launch a timing attack. In this paper, we propose a
novel approach, called deterministic browser, which can provably prevent timing
attacks in modern browsers. Borrowing from Physics, we introduce several
concepts, such as an observer and a reference frame. Specifically, a snippet of
JavaScript, i.e., an observer in JavaScript reference frame, will always obtain
the same, fixed timing information so that timing attacks are prevented; at
contrast, a user, i.e., an oracle observer, will perceive the JavaScript
differently and do not experience the performance slowdown. We have implemented
a prototype called DeterFox and our evaluation shows that the prototype can
defend against browser-related timing attacks. | [
1,
0,
0,
0,
0,
0
] |
Title: Can Transfer Entropy Infer Causality in Neuronal Circuits for Cognitive Processing?,
Abstract: Finding the causes to observed effects and establishing causal relationships
between events is (and has been) an essential element of science and
philosophy. Automated methods that can detect causal relationships would be
very welcome, but practical methods that can infer causality are difficult to
find, and the subject of ongoing research. While Shannon information only
detects correlation, there are several information-theoretic notions of
"directed information" that have successfully detected causality in some
systems, in particular in the neuroscience community. However, recent work has
shown that some directed information measures can sometimes inadequately
estimate the extent of causal relations, or even fail to identify existing
cause-effect relations between components of systems, especially if neurons
contribute in a cryptographic manner to influence the effector neuron. Here, we
test how often cryptographic logic emerges in an evolutionary process that
generates artificial neural circuits for two fundamental cognitive tasks:
motion detection and sound localization. Our results suggest that whether or
not transfer entropy measures of causality are misleading depends strongly on
the cognitive task considered. These results emphasize the importance of
understanding the fundamental logic processes that contribute to cognitive
processing, and quantifying their relevance in any given nervous system. | [
1,
0,
0,
0,
1,
0
] |
Title: Solvability of the Stokes Immersed Boundary Problem in Two Dimensions,
Abstract: We study coupled motion of a 1-D closed elastic string immersed in a 2-D
Stokes flow, known as the Stokes immersed boundary problem in two dimensions.
Using the fundamental solution of the Stokes equation and the Lagrangian
coordinate of the string, we write the problem into a contour dynamic
formulation, which is a nonlinear non-local equation solely keeping track of
evolution of the string configuration. We prove existence and uniqueness of
local-in-time solution starting from an arbitrary initial configuration that is
an $H^{5/2}$-function in the Lagrangian coordinate satisfying the so-called
well-stretched assumption. We also prove that when the initial string
configuration is sufficiently close to an equilibrium, which is an evenly
parameterized circular configuration, then global-in-time solution uniquely
exists and it will converge to an equilibrium configuration exponentially as
$t\rightarrow +\infty$. The technique in this paper may also apply to the
Stokes immersed boundary problem in three dimensions. | [
0,
0,
1,
0,
0,
0
] |
Title: Nonlinear Field Space Cosmology,
Abstract: We consider the FRW cosmological model in which the matter content of
universe (playing a role of inflaton or quintessence) is given by a novel
generalization of the massive scalar field. The latter is a scalar version of
the recently introduced Nonlinear Field Space Theory (NFST), where physical
phase space of a given field is assumed to be compactified at large energies.
For our analysis we choose the simple case of a field with the spherical phase
space and endow it with the generalized Hamiltonian analogous to the XXZ
Heisenberg model, normally describing a system of spins in condensed matter
physics. Subsequently, we study both the homogenous cosmological sector and
linear perturbations of such a test field. In the homogenous sector we find
that nonlinearity of the field phase space is becoming relevant for large
volumes of universe and then it can lead to a recollapse, and possibly also at
very high energies, leading to the phase of a bounce. Quantization of the field
is performed in the limit where nontrivial nature of its phase space can be
neglected, while there is a non-vanishing contribution from the Lorentz
symmetry breaking term of the Hamiltonian. As a result, in the leading order of
the XXZ anisotropy parameter, we find that the inflationary spectral index
remains unmodified with respect to the standard case but the total amplitude of
perturbations is subject to a correction. The Bunch-Davies vacuum state also
becomes appropriately corrected. The proposed new approach is bringing
cosmology and condensed matter physics closer together, which may turn out to
be beneficial for both disciplines. | [
0,
1,
0,
0,
0,
0
] |
Title: Futuristic Classification with Dynamic Reference Frame Strategy,
Abstract: Classification is one of the widely used analytical techniques in data
science domain across different business to associate a pattern which
contribute to the occurrence of certain event which is predicted with some
likelihood. This Paper address a lacuna of creating some time window before the
prediction actually happen to enable organizations some space to act on the
prediction. There are some really good state of the art machine learning
techniques to optimally identify the possible churners in either customer base
or employee base, similarly for fault prediction too if the prediction does not
come with some buffer time to act on the fault it is very difficult to provide
a seamless experience to the user. New concept of reference frame creation is
introduced to solve this problem in this paper | [
0,
0,
0,
1,
0,
0
] |
Title: On the normal centrosymmetric Nonnegative inverse eigenvalue problem,
Abstract: We give sufficient conditions of the nonnegative inverse eigenvalue problem
(NIEP) for normal centrosymmetric matrices. These sufficient conditions are
analogous to the sufficient conditions of the NIEP for normal matrices given by
Xu [16] and Julio, Manzaneda and Soto [2]. | [
0,
0,
1,
0,
0,
0
] |
Title: Spectral Dynamics of Learning Restricted Boltzmann Machines,
Abstract: The Restricted Boltzmann Machine (RBM), an important tool used in machine
learning in particular for unsupervized learning tasks, is investigated from
the perspective of its spectral properties. Starting from empirical
observations, we propose a generic statistical ensemble for the weight matrix
of the RBM and characterize its mean evolution. This let us show how in the
linear regime, in which the RBM is found to operate at the beginning of the
training, the statistical properties of the data drive the selection of the
unstable modes of the weight matrix. A set of equations characterizing the
non-linear regime is then derived, unveiling in some way how the selected modes
interact in later stages of the learning procedure and defining a deterministic
learning curve for the RBM. | [
1,
1,
0,
0,
0,
0
] |
Title: Local energy decay for Lipschitz wavespeeds,
Abstract: We prove a logarithmic local energy decay rate for the wave equation with a
wavespeed that is a compactly supported Lipschitz perturbation of unity. The
key is to establish suitable resolvent estimates at high and low energy for the
meromorphic continuation of the cutoff resolvent. The decay rate is the same as
that proved by Burq for a smooth perturbation of the Laplacian outside an
obstacle. | [
0,
0,
1,
0,
0,
0
] |
Title: Linear stability and stability of Lazarsfeld-Mukai bundles,
Abstract: Let $C$ be a smooth irreducible projective curve and let $(L,H^0(C,L))$ be a
complete and generated linear series on $C$. Denote by $M_L$ the kernel of the
evaluation map $H^0(C,L)\otimes\mathcal O_C\to L$. The exact sequence $0\to
M_L\to H^0(C,L)\otimes\mathcal O_C\to L\to 0$ fits into a commutative diagram
that we call the Butler's diagram. This diagram induces in a natural way a
multiplication map on global sections $m_W: W^{\vee}\otimes H^0(K_C)\to
H^0(S^{\vee}\otimes K_C)$, where $W\subseteq H^0(C,L)$ is a subspace and
$S^{\vee}$ is the dual of a subbundle $S\subset M_L$. When the subbundle $S$ is
a stable bundle, we show that the map $m_W$ is surjective. When $C$ is a
Brill-Noether general curve, we use the surjectivity of $m_W$ to give another
proof on the semistability of $M_L$, moreover we fill up a gap of an incomplete
argument by Butler: With the surjectivity of $m_W$ we give conditions to
determinate the stability of $M_L$, and such conditions implies the well known
stability conditions for $M_L$ stated precisely by Butler. Finally we obtain
the equivalence between the stability of $M_L$ and the linear stability of
$(L,H^0(L))$ on $\gamma$-gonal curves. | [
0,
0,
1,
0,
0,
0
] |
Title: Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped,
Abstract: Controllers in robotics often consist of expert-designed heuristics, which
can be hard to tune in higher dimensions. It is typical to use simulation to
learn these parameters, but controllers learned in simulation often don't
transfer to hardware. This necessitates optimization directly on hardware.
However, collecting data on hardware can be expensive. This has led to a recent
interest in adapting data-efficient learning techniques to robotics. One
popular method is Bayesian Optimization (BO), a sample-efficient black-box
optimization scheme, but its performance typically degrades in higher
dimensions. We aim to overcome this problem by incorporating domain knowledge
to reduce dimensionality in a meaningful way, with a focus on bipedal
locomotion. In previous work, we proposed a transformation based on knowledge
of human walking that projected a 16-dimensional controller to a 1-dimensional
space. In simulation, this showed enhanced sample efficiency when optimizing
human-inspired neuromuscular walking controllers on a humanoid model. In this
paper, we present a generalized feature transform applicable to non-humanoid
robot morphologies and evaluate it on the ATRIAS bipedal robot -- in simulation
and on hardware. We present three different walking controllers; two are
evaluated on the real robot. Our results show that this feature transform
captures important aspects of walking and accelerates learning on hardware and
simulation, as compared to traditional BO. | [
1,
0,
0,
0,
0,
0
] |
Title: Comparison of multi-task convolutional neural network (MT-CNN) and a few other methods for toxicity prediction,
Abstract: Toxicity analysis and prediction are of paramount importance to human health
and environmental protection. Existing computational methods are built from a
wide variety of descriptors and regressors, which makes their performance
analysis difficult. For example, deep neural network (DNN), a successful
approach in many occasions, acts like a black box and offers little conceptual
elegance or physical understanding. The present work constructs a common set of
microscopic descriptors based on established physical models for charges,
surface areas and free energies to assess the performance of multi-task
convolutional neural network (MT-CNN) architectures and a few other approaches,
including random forest (RF) and gradient boosting decision tree (GBDT), on an
equal footing. Comparison is also given to convolutional neural network (CNN)
and non-convolutional deep neural network (DNN) algorithms. Four benchmark
toxicity data sets (i.e., endpoints) are used to evaluate various approaches.
Extensive numerical studies indicate that the present MT-CNN architecture is
able to outperform the state-of-the-art methods. | [
1,
0,
0,
1,
0,
0
] |
Title: SIFM: A network architecture for seamless flow mobility between LTE and WiFi networks - Analysis and Testbed Implementation,
Abstract: This paper deals with cellular (e.g. LTE) networks that selectively offload
the mobile data traffic onto WiFi (IEEE 802.11) networks to improve network
performance. We propose the Seamless Internetwork Flow Mobility (SIFM)
architecture that provides seamless flow-mobility support using concepts of
Software Defined Networking (SDN). The SDN paradigm decouples the control and
data plane, leading to a centralized network intelligence and state. The SIFM
architecture utilizes this aspect of SDN and moves the mobility decisions to a
centralized Flow Controller (FC). This provides a global network view while
making mobility decisions and also reduces the complexity at the PGW. We
implement and evaluate both basic PMIPv6 and the SIFM architectures by
incorporating salient LTE and WiFi network features in the ns-3 simulator.
Performance experiments validate that seamless mobility is achieved. Also, the
SIFM architecture shows an improved network performance when compared to the
base PMIPv6 architecture. A proof-of-concept prototype of the SIFM architecture
has been implemented on an experimental testbed. The LTE network is emulated by
integrating USRP B210x with the OpenLTE eNodeB and OpenLTE EPC. The WiFi
network is emulated using hostapd and dnsmasq daemons running on Ubuntu 12.04.
An off-the-shelf LG G2 mobile phone running Android 4.2.2 is used as the user
equipment. We demonstrate seamless mobility between the LTE network and the
WiFi network with the help of ICMP ping and a TCP chat application. | [
1,
0,
0,
0,
0,
0
] |
Title: Graph sampling with determinantal processes,
Abstract: We present a new random sampling strategy for k-bandlimited signals defined
on graphs, based on determinantal point processes (DPP). For small graphs, ie,
in cases where the spectrum of the graph is accessible, we exhibit a DPP
sampling scheme that enables perfect recovery of bandlimited signals. For large
graphs, ie, in cases where the graph's spectrum is not accessible, we
investigate, both theoretically and empirically, a sub-optimal but much faster
DPP based on loop-erased random walks on the graph. Preliminary experiments
show promising results especially in cases where the number of measurements
should stay as small as possible and for graphs that have a strong community
structure. Our sampling scheme is efficient and can be applied to graphs with
up to $10^6$ nodes. | [
1,
0,
0,
1,
0,
0
] |
Title: Grundy dominating sequences and zero forcing sets,
Abstract: In a graph $G$ a sequence $v_1,v_2,\dots,v_m$ of vertices is Grundy
dominating if for all $2\le i \le m$ we have $N[v_i]\not\subseteq
\cup_{j=1}^{i-1}N[v_j]$ and is Grundy total dominating if for all $2\le i \le
m$ we have $N(v_i)\not\subseteq \cup_{j=1}^{i-1}N(v_j)$. The length of the
longest Grundy (total) dominating sequence has been studied by several authors.
In this paper we introduce two similar concepts when the requirement on the
neighborhoods is changed to $N(v_i)\not\subseteq \cup_{j=1}^{i-1}N[v_j]$ or
$N[v_i]\not\subseteq \cup_{j=1}^{i-1}N(v_j)$. In the former case we establish a
strong connection to the zero forcing number of a graph, while we determine the
complexity of the decision problem in the latter case. We also study the
relationships among the four concepts, and discuss their computational
complexities. | [
0,
0,
1,
0,
0,
0
] |
Title: Comments on `High-dimensional simultaneous inference with the bootstrap',
Abstract: We provide comments on the article "High-dimensional simultaneous inference
with the bootstrap" by Ruben Dezeure, Peter Buhlmann and Cun-Hui Zhang. | [
0,
0,
0,
1,
0,
0
] |
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