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Title: Universal edge transport in interacting Hall systems,
Abstract: We study the edge transport properties of $2d$ interacting Hall systems,
displaying single-mode chiral edge currents. For this class of many-body
lattice models, including for instance the interacting Haldane model, we prove
the quantization of the edge charge conductance and the bulk-edge
correspondence. Instead, the edge Drude weight and the edge susceptibility are
interaction-dependent; nevertheless, they satisfy exact universal scaling
relations, in agreement with the chiral Luttinger liquid theory. Moreover,
charge and spin excitations differ in their velocities, giving rise to the
spin-charge separation phenomenon. The analysis is based on exact
renormalization group methods, and on a combination of lattice and emergent
Ward identities. The invariance of the emergent chiral anomaly under the
renormalization group flow plays a crucial role in the proof. | [
0,
1,
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] |
Title: Photonic Band Structure of Two-dimensional Atomic Lattices,
Abstract: Two-dimensional atomic arrays exhibit a number of intriguing quantum optical
phenomena, including subradiance, nearly perfect reflection of radiation and
long-lived topological edge states. Studies of emission and scattering of
photons in such lattices require complete treatment of the radiation pattern
from individual atoms, including long-range interactions. We describe a
systematic approach to perform the calculations of collective energy shifts and
decay rates in the presence of such long-range interactions for arbitrary
two-dimensional atomic lattices. As applications of our method, we investigate
the topological properties of atomic lattices both in free-space and near
plasmonic surfaces. | [
0,
1,
0,
0,
0,
0
] |
Title: Multifractal analysis of the time series of daily means of wind speed in complex regions,
Abstract: In this paper, we applied the multifractal detrended fluctuation analysis to
the daily means of wind speed measured by 119 weather stations distributed over
the territory of Switzerland. The analysis was focused on the inner time
fluctuations of wind speed, which could be more linked with the local
conditions of the highly varying topography of Switzerland. Our findings point
out to a persistent behaviour of all the measured wind speed series (indicated
by a Hurst exponent significantly larger than 0.5), and to a high
multifractality degree indicating a relative dominance of the large
fluctuations in the dynamics of wind speed, especially in the Swiss plateau,
which is comprised between the Jura and Alp mountain ranges. The study
represents a contribution to the understanding of the dynamical mechanisms of
wind speed variability in mountainous regions. | [
0,
0,
0,
1,
0,
0
] |
Title: Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning,
Abstract: We present a new deep meta reinforcement learner, which we call Deep Episodic
Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity
metric for a non-parametric model-based reinforcement learning algorithm. Our
model is trained end-to-end via back-propagation. Despite being trained using
the model-free Q-learning objective, we show that DEVI's model-based internal
structure provides `one-shot' transfer to changes in reward and transition
structure, even for tasks with very high-dimensional state spaces. | [
1,
0,
0,
1,
0,
0
] |
Title: Long-lived mesoscopic entanglement between two damped infinite harmonic chains,
Abstract: We consider two chains, each made of $N$ independent oscillators, immersed in
a common thermal bath and study the dynamics of their mutual quantum
correlations in the thermodynamic, large-$N$ limit. We show that dissipation
and noise due to the presence of the external environment are able to generate
collective quantum correlations between the two chains at the mesoscopic level.
The created collective quantum entanglement between the two many-body systems
turns out to be rather robust, surviving for asymptotically long times even for
non vanishing bath temperatures. | [
0,
1,
0,
0,
0,
0
] |
Title: Spectral Approach to Verifying Non-linear Arithmetic Circuits,
Abstract: This paper presents a fast and effective computer algebraic method for
analyzing and verifying non-linear integer arithmetic circuits using a novel
algebraic spectral model. It introduces a concept of algebraic spectrum, a
numerical form of polynomial expression; it uses the distribution of
coefficients of the monomials to determine the type of arithmetic function
under verification. In contrast to previous works, the proof of functional
correctness is achieved by computing an algebraic spectrum combined with a
local rewriting of word-level polynomials. The speedup is achieved by
propagating coefficients through the circuit using And-Inverter Graph (AIG)
datastructure. The effectiveness of the method is demonstrated with experiments
including standard and Booth multipliers, and other synthesized non-linear
arithmetic circuits up to 1024 bits containing over 12 million gates. | [
1,
0,
0,
0,
0,
0
] |
Title: Signal-based Bayesian Seismic Monitoring,
Abstract: Detecting weak seismic events from noisy sensors is a difficult perceptual
task. We formulate this task as Bayesian inference and propose a generative
model of seismic events and signals across a network of spatially distributed
stations. Our system, SIGVISA, is the first to directly model seismic
waveforms, allowing it to incorporate a rich representation of the physics
underlying the signal generation process. We use Gaussian processes over
wavelet parameters to predict detailed waveform fluctuations based on
historical events, while degrading smoothly to simple parametric envelopes in
regions with no historical seismicity. Evaluating on data from the western US,
we recover three times as many events as previous work, and reduce mean
location errors by a factor of four while greatly increasing sensitivity to
low-magnitude events. | [
1,
1,
0,
0,
0,
0
] |
Title: Complexity of Verifying Nonblockingness in Modular Supervisory Control,
Abstract: Complexity analysis becomes a common task in supervisory control. However,
many results of interest are spread across different topics. The aim of this
paper is to bring several interesting results from complexity theory and to
illustrate their relevance to supervisory control by proving new nontrivial
results concerning nonblockingness in modular supervisory control of discrete
event systems modeled by finite automata. | [
1,
0,
0,
0,
0,
0
] |
Title: Simulation and analysis of $γ$-Ni cellular growth during laser powder deposition of Ni-based superalloys,
Abstract: Cellular or dendritic microstructures that result as a function of additive
manufacturing solidification conditions in a Ni-based melt pool are simulated
in the present work using three-dimensional phase-field simulations. A
macroscopic thermal model is used to obtain the temperature gradient $G$ and
the solidification velocity $V$ which are provided as inputs to the phase-field
model. We extract the cell spacings, cell core compositions, and cell tip as
well as mushy zone temperatures from the simulated microstructures as a
function of $V$. Cell spacings are compared with different scaling laws that
correlate to the solidification conditions and approximated by $G^{-m}V^{-n}$.
Cell core compositions are compared with the analytical solutions of a dendrite
growth theory and found to be in good agreement. Through analysis of the mushy
zone, we extract a characteristic bridging plane, where the primary $\gamma$
phase coalesces across the intercellular liquid channels at a $\gamma$ fraction
between 0.6 and 0.7. The temperature and the $\gamma$ fraction in this plane
are found to decrease with increasing $V$. The simulated microstructural
features are significant as they can be used as inputs for the simulation of
subsequent heat treatment processes. | [
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1,
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] |
Title: The next-to-minimal weights of binary projective Reed-Muller codes,
Abstract: Projective Reed-Muller codes were introduced by Lachaud, in 1988 and their
dimension and minimum distance were determined by Serre and S{\o}rensen in
1991. In coding theory one is also interested in the higher Hamming weights, to
study the code performance. Yet, not many values of the higher Hamming weights
are known for these codes, not even the second lowest weight (also known as
next-to-minimal weight) is completely determined. In this paper we determine
all the values of the next-to-minimal weight for the binary projective
Reed-Muller codes, which we show to be equal to the next-to-minimal weight of
Reed-Muller codes in most, but not all, cases. | [
1,
0,
1,
0,
0,
0
] |
Title: The dynamical structure of political corruption networks,
Abstract: Corruptive behaviour in politics limits economic growth, embezzles public
funds, and promotes socio-economic inequality in modern democracies. We analyse
well-documented political corruption scandals in Brazil over the past 27 years,
focusing on the dynamical structure of networks where two individuals are
connected if they were involved in the same scandal. Our research reveals that
corruption runs in small groups that rarely comprise more than eight people, in
networks that have hubs and a modular structure that encompasses more than one
corruption scandal. We observe abrupt changes in the size of the largest
connected component and in the degree distribution, which are due to the
coalescence of different modules when new scandals come to light or when
governments change. We show further that the dynamical structure of political
corruption networks can be used for successfully predicting partners in future
scandals. We discuss the important role of network science in detecting and
mitigating political corruption. | [
1,
0,
0,
1,
0,
0
] |
Title: Engineering phonon leakage in nanomechanical resonators,
Abstract: We propose and experimentally demonstrate a technique for coupling phonons
out of an optomechanical crystal cavity. By designing a perturbation that
breaks a symmetry in the elastic structure, we selectively induce phonon
leakage without affecting the optical properties. It is shown experimentally
via cryogenic measurements that the proposed cavity perturbation causes loss of
phonons into mechanical waves on the surface of silicon, while leaving photon
lifetimes unaffected. This demonstrates that phonon leakage can be engineered
in on-chip optomechanical systems. We experimentally observe large fluctuations
in leakage rates that we attribute to fabrication disorder and verify this
using simulations. Our technique opens the way to engineering more complex
on-chip phonon networks utilizing guided mechanical waves to connect quantum
systems. | [
0,
1,
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0,
0,
0
] |
Title: A Local Faber-Krahn inequality and Applications to Schrödinger's Equation,
Abstract: We prove a local Faber-Krahn inequality for solutions $u$ to the Dirichlet
problem for $\Delta + V$ on an arbitrary domain $\Omega$ in $\mathbb{R}^n$.
Suppose a solution $u$ assumes a global maximum at some point $x_0 \in \Omega$
and $u(x_0)>0$. Let $T(x_0)$ be the smallest time at which a Brownian motion,
started at $x_0$, has exited the domain $\Omega$ with probability $\ge 1/2$.
For nice (e.g., convex) domains, $T(x_0) \asymp d(x_0,\partial\Omega)^2$ but we
make no assumption on the geometry of the domain. Our main result is that there
exists a ball $B$ of radius $\asymp T(x_0)^{1/2}$ such that $$ \| V
\|_{L^{\frac{n}{2}, 1}(\Omega \cap B)} \ge c_n > 0, $$ provided that $n \ge 3$.
In the case $n = 2$, the above estimate fails and we obtain a substitute
result. The Laplacian may be replaced by a uniformly elliptic operator in
divergence form. This result both unifies and strenghtens a series of earlier
results. | [
0,
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1,
0,
0,
0
] |
Title: Semi-classical limit of the Levy-Lieb functional in Density Functional Theory,
Abstract: In a recent work, Bindini and De Pascale have introduced a regularization of
$N$-particle symmetric probabilities which preserves their one-particle
marginals. In this short note, we extend their construction to mixed quantum
fermionic states. This enables us to prove the convergence of the Levy-Lieb
functional in Density Functional Theory , to the corresponding multi-marginal
optimal transport in the semi-classical limit. Our result holds for mixed
states of any particle number $N$, with or without spin. | [
0,
1,
1,
0,
0,
0
] |
Title: A characterization of cellular motivic spectra,
Abstract: Let $ \alpha: \mathcal{C} \to \mathcal{D}$ be a symmetric monoidal functor
from a stable presentable symmetric monoidal $\infty$-category $\mathcal{C} $
compactly generated by the tensorunit to a stable presentable symmetric
monoidal $\infty$-category $ \mathcal{D} $ with compact tensorunit. Let $\beta:
\mathcal{D} \to \mathcal{C}$ be a right adjoint of $\alpha$ and $ \mathrm{X}:
\mathcal{B} \to \mathcal{D} $ a symmetric monoidal functor starting at a small
rigid symmetric monoidal $\infty$-category $ \mathcal{B}$. We construct a
symmetric monoidal equivalence between modules in the $\infty$-category of
functors $ \mathcal{B} \to \mathcal{C} $ over the $ \mathrm{E}_\infty$-algebra
$\beta \circ \mathrm{X} $ and the full subcategory of $\mathcal{D}$ compactly
generated by the essential image of $\mathrm{X}$. Especially for every motivic
$ \mathrm{E}_\infty$-ring spectrum $\mathrm{A}$ we obtain a symmetric monoidal
equivalence between the $\infty$-category of cellular motivic
$\mathrm{A}$-module spectra and modules in the $\infty$-category of functors
$\mathrm{QS}$ to spectra over some $ \mathrm{E}_\infty$-algebra, where
$\mathrm{QS}$ denotes the 0th space of the sphere spectrum. | [
0,
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1,
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] |
Title: The Impedance of Flat Metallic Plates with Small Corrugations,
Abstract: Summarizes recent work on the wakefields and impedances of flat, metallic
plates with small corrugations | [
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1,
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0,
0
] |
Title: Aerodynamic noise from rigid trailing edges with finite porous extensions,
Abstract: This paper investigates the effects of finite flat porous extensions to
semi-infinite impermeable flat plates in an attempt to control trailing-edge
noise through bio-inspired adaptations. Specifically the problem of sound
generated by a gust convecting in uniform mean steady flow scattering off the
trailing edge and permeable-impermeable junction is considered. This setup
supposes that any realistic trailing-edge adaptation to a blade would be
sufficiently small so that the turbulent boundary layer encapsulates both the
porous edge and the permeable-impermeable junction, and therefore the
interaction of acoustics generated at these two discontinuous boundaries is
important. The acoustic problem is tackled analytically through use of the
Wiener-Hopf method. A two-dimensional matrix Wiener-Hopf problem arises due to
the two interaction points (the trailing edge and the permeable-impermeable
junction). This paper discusses a new iterative method for solving this matrix
Wiener-Hopf equation which extends to further two-dimensional problems in
particular those involving analytic terms that exponentially grow in the upper
or lower half planes. This method is an extension of the commonly used "pole
removal" technique and avoids the needs for full matrix factorisation.
Convergence of this iterative method to an exact solution is shown to be
particularly fast when terms neglected in the second step are formally smaller
than all other terms retained. The final acoustic solution highlights the
effects of the permeable-impermeable junction on the generated noise, in
particular how this junction affects the far-field noise generated by
high-frequency gusts by creating an interference to typical trailing-edge
scattering. This effect results in partially porous plates predicting a lower
noise reduction than fully porous plates when compared to fully impermeable
plates. | [
0,
1,
1,
0,
0,
0
] |
Title: 99% of Parallel Optimization is Inevitably a Waste of Time,
Abstract: It is well known that many optimization methods, including SGD, SAGA, and
Accelerated SGD for over-parameterized models, do not scale linearly in the
parallel setting. In this paper, we present a new version of block coordinate
descent that solves this issue for a number of methods. The core idea is to
make the sampling of coordinate blocks on each parallel unit independent of the
others. Surprisingly, we prove that the optimal number of blocks to be updated
by each of $n$ units in every iteration is equal to $m/n$, where $m$ is the
total number of blocks. As an illustration, this means that when $n=100$
parallel units are used, $99\%$ of work is a waste of time. We demonstrate that
with $m/n$ blocks used by each unit the iteration complexity often remains the
same. Among other applications which we mention, this fact can be exploited in
the setting of distributed optimization to break the communication bottleneck.
Our claims are justified by numerical experiments which demonstrate almost a
perfect match with our theory on a number of datasets. | [
1,
0,
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] |
Title: Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors,
Abstract: While single measurement vector (SMV) models have been widely studied in
signal processing, there is a surging interest in addressing the multiple
measurement vectors (MMV) problem. In the MMV setting, more than one
measurement vector is available and the multiple signals to be recovered share
some commonalities such as a common support. Applications in which MMV is a
naturally occurring phenomenon include online streaming, medical imaging, and
video recovery. This work presents a stochastic iterative algorithm for the
support recovery of jointly sparse corrupted MMV. We present a variant of the
Sparse Randomized Kaczmarz algorithm for corrupted MMV and compare our proposed
method with an existing Kaczmarz type algorithm for MMV problems. We also
showcase the usefulness of our approach in the online (streaming) setting and
provide empirical evidence that suggests the robustness of the proposed method
to the distribution of the corruption and the number of corruptions occurring. | [
1,
0,
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0,
0
] |
Title: Exploiting ITO colloidal nanocrystals for ultrafast pulse generation,
Abstract: Dynamical materials that capable of responding to optical stimuli have always
been pursued for designing novel photonic devices and functionalities, of which
the response speed and amplitude as well as integration adaptability and energy
effectiveness are especially critical. Here we show ultrafast pulse generation
by exploiting the ultrafast and sensitive nonlinear dynamical processes in
tunably solution-processed colloidal epsilon-near-zero (ENZ) transparent
conducting oxide (TCO) nanocrystals (NCs), of which the potential respond
response speed is >2 THz and modulation depth is ~23% pumped at ~0.7 mJ/cm2,
benefiting from the highly confined geometry in addition to the ENZ enhancement
effect. These ENZ NCs may offer a scalable and printable material solution for
dynamic photonic and optoelectronic devices. | [
0,
1,
0,
0,
0,
0
] |
Title: Throughput Optimal Beam Alignment in Millimeter Wave Networks,
Abstract: Millimeter wave communications rely on narrow-beam transmissions to cope with
the strong signal attenuation at these frequencies, thus demanding precise beam
alignment between transmitter and receiver. The communication overhead incurred
to achieve beam alignment may become a severe impairment in mobile networks.
This paper addresses the problem of optimizing beam alignment acquisition, with
the goal of maximizing throughput. Specifically, the algorithm jointly
determines the portion of time devoted to beam alignment acquisition, as well
as, within this portion of time, the optimal beam search parameters, using the
framework of Markov decision processes. It is proved that a bisection search
algorithm is optimal, and that it outperforms exhaustive and iterative search
algorithms proposed in the literature. The duration of the beam alignment phase
is optimized so as to maximize the overall throughput. The numerical results
show that the throughput, optimized with respect to the duration of the beam
alignment phase, achievable under the exhaustive algorithm is 88.3% lower than
that achievable under the bisection algorithm. Similarly, the throughput
achievable by the iterative search algorithm for a division factor of 4 and 8
is, respectively, 12.8% and 36.4% lower than that achievable by the bisection
algorithm. | [
1,
0,
0,
0,
0,
0
] |
Title: Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling,
Abstract: In this work, we design a machine learning based method, online adaptive
primal support vector regression (SVR), to model the implied volatility surface
(IVS). The algorithm proposed is the first derivation and implementation of an
online primal kernel SVR. It features enhancements that allow efficient online
adaptive learning by embedding the idea of local fitness and budget maintenance
to dynamically update support vectors upon pattern drifts. For algorithm
acceleration, we implement its most computationally intensive parts in a Field
Programmable Gate Arrays hardware, where a 132x speedup over CPU is achieved
during online prediction. Using intraday tick data from the E-mini S&P 500
options market, we show that the Gaussian kernel outperforms the linear kernel
in regulating the size of support vectors, and that our empirical IVS algorithm
beats two competing online methods with regards to model complexity and
regression errors (the mean absolute percentage error of our algorithm is up to
13%). Best results are obtained at the center of the IVS grid due to its larger
number of adjacent support vectors than the edges of the grid. Sensitivity
analysis is also presented to demonstrate how hyper parameters affect the error
rates and model complexity. | [
1,
0,
0,
1,
0,
0
] |
Title: Hybrid control strategy for a semi active suspension system using fuzzy logic and bio-inspired chaotic fruit fly algorithm,
Abstract: This study proposes a control strategy for the efficient semi active
suspension systems utilizing a novel hybrid PID-fuzzy logic control scheme .In
the control architecture, we employ the Chaotic Fruit Fly Algorithm for PID
tuning since it can avoid local minima by chaotic search. A novel linguistic
rule based fuzzy logic controller is developed to aid the PID.A quarter car
model with a non-linear spring system is used to test the performance of the
proposed control approach. A road terrain is chosen where the comfort and
handling parameters are tested specifically in the regions of abrupt changes.
The results suggest that the suspension systems controlled by the hybrid
strategy has the potential to offer more comfort and handling by reducing the
peak acceleration and suspension distortion by 83.3 % and 28.57% respectively
when compared to the active suspension systems. Also, compared to the
performance of similar suspension control strategies optimized by stochastic
algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO)
and Bacterial Foraging Optimization (BFO), reductions in peak acceleration and
suspension distortion are found to be 25%, 32.3%, 54.6% and 23.35 %, 22.5%, 5.4
% respectively.The details of the solution methodology have been presented in
the paper. | [
1,
0,
0,
0,
0,
0
] |
Title: Coaction functors, II,
Abstract: In further study of the application of crossed-product functors to the
Baum-Connes Conjecture, Buss, Echterhoff, and Willett introduced various other
properties that crossed-product functors may have. Here we introduce and study
analogues of these properties for coaction functors, making sure that the
properties are preserved when the coaction functors are composed with the full
crossed product to make a crossed-product functor. The new properties for
coaction functors studied here are functoriality for generalized homomorphisms
and the correspondence property. We particularly study the connections with the
ideal property. The study of functoriality for generalized homomorphisms
requires a detailed development of the Fischer construction of maximalization
of coactions with regard to possibly degenerate homomorphisms into multiplier
algebras. We verify that all "KLQ" functors arising from large ideals of the
Fourier-Stieltjes algebra $B(G)$ have all the properties we study, and at the
opposite extreme we give an example of a coaction functor having none of the
properties. | [
0,
0,
1,
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0,
0
] |
Title: Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video,
Abstract: Manual annotations of temporal bounds for object interactions (i.e. start and
end times) are typical training input to recognition, localization and
detection algorithms. For three publicly available egocentric datasets, we
uncover inconsistencies in ground truth temporal bounds within and across
annotators and datasets. We systematically assess the robustness of
state-of-the-art approaches to changes in labeled temporal bounds, for object
interaction recognition. As boundaries are trespassed, a drop of up to 10% is
observed for both Improved Dense Trajectories and Two-Stream Convolutional
Neural Network.
We demonstrate that such disagreement stems from a limited understanding of
the distinct phases of an action, and propose annotating based on the Rubicon
Boundaries, inspired by a similarly named cognitive model, for consistent
temporal bounds of object interactions. Evaluated on a public dataset, we
report a 4% increase in overall accuracy, and an increase in accuracy for 55%
of classes when Rubicon Boundaries are used for temporal annotations. | [
1,
0,
0,
0,
0,
0
] |
Title: Dynamical transport measurement of the Luttinger parameter in helical edges states of 2D topological insulators,
Abstract: One-dimensional (1D) electron systems in the presence of Coulomb interaction
are described by Luttinger liquid theory. The strength of Coulomb interaction
in the Luttinger liquid, as parameterized by the Luttinger parameter K, is in
general difficult to measure. This is because K is usually hidden in powerlaw
dependencies of observables as a function of temperature or applied bias. We
propose a dynamical way to measure K on the basis of an electronic
time-of-flight experiment. We argue that the helical Luttinger liquid at the
edge of a 2D topological insulator constitutes a preeminently suited
realization of a 1D system to test our proposal. This is based on the
robustness of helical liquids against elastic backscattering in the presence of
time reversal symmetry. | [
0,
1,
0,
0,
0,
0
] |
Title: Distributed Average Tracking of Heterogeneous Physical Second-order Agents With No Input Signals Constraint,
Abstract: This paper addresses distributed average tracking of physical second-order
agents with heterogeneous nonlinear dynamics, where there is no constraint on
input signals. The nonlinear terms in agents' dynamics are heterogeneous,
satisfying a Lipschitz-like condition that will be defined later and is more
general than the Lipschitz condition. In the proposed algorithm, a control
input and a filter are designed for each agent. Each agent's filter has two
outputs and the idea is that the first output estimates the average of the
input signals and the second output estimates the average of the input
velocities asymptotically. In parallel, each agent's position and velocity are
driven to track, respectively, the first and the second outputs. Having
heterogeneous nonlinear terms in agents' dynamics necessitates designing the
filters for agents. Since the nonlinear terms in agents' dynamics can be
unbounded and the input signals are arbitrary, novel state-dependent
time-varying gains are employed in agents' filters and control inputs to
overcome these unboundedness effects. Finally the results are improved to
achieve the distributed average tracking for a group of double-integrator
agents, where there is no constraint on input signals and the filter is not
required anymore. Numerical simulations are also presented to illustrate the
theoretical results. | [
0,
0,
1,
0,
0,
0
] |
Title: Bound states of the two-dimensional Dirac equation for an energy-dependent hyperbolic Scarf potential,
Abstract: We study the two-dimensional massless Dirac equation for a potential that is
allowed to depend on the energy and on one of the spatial variables. After
determining a modified orthogonality relation and norm for such systems, we
present an application involving an energy-dependent version of the hyperbolic
Scarf potential. We construct closed-form bound state solutions of the
associated Dirac equation. | [
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1,
0,
0,
0,
0
] |
Title: Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild,
Abstract: Humans take advantage of real world symmetries for various tasks, yet
capturing their superb symmetry perception mechanism with a computational model
remains elusive. Motivated by a new study demonstrating the extremely high
inter-person accuracy of human perceived symmetries in the wild, we have
constructed the first deep-learning neural network for reflection and rotation
symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common
Object in COntext) dataset with nearly 11K consistent symmetry-labels from more
than 400 human observers. We employ novel methods to convert discrete human
labels into symmetry heatmaps, capture symmetry densely in an image and
quantitatively evaluate Sym-NET against multiple existing computer vision
algorithms. On CVPR 2013 symmetry competition testsets and unseen MS-COCO
photos, Sym-NET significantly outperforms all other competitors. Beyond
mathematically well-defined symmetries on a plane, Sym-NET demonstrates
abilities to identify viewpoint-varied 3D symmetries, partially occluded
symmetrical objects, and symmetries at a semantic level. | [
1,
0,
0,
1,
0,
0
] |
Title: A Dynamic Programming Principle for Distribution-Constrained Optimal Stopping,
Abstract: We consider an optimal stopping problem where a constraint is placed on the
distribution of the stopping time. Reformulating the problem in terms of
so-called measure-valued martingales allows us to transform the marginal
constraint into an initial condition and view the problem as a stochastic
control problem; we establish the corresponding dynamic programming principle. | [
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1,
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0
] |
Title: The length of excitable knots,
Abstract: The FitzHugh-Nagumo equation provides a simple mathematical model of cardiac
tissue as an excitable medium hosting spiral wave vortices. Here we present
extensive numerical simulations studying long-term dynamics of knotted vortex
string solutions for all torus knots up to crossing number 11. We demonstrate
that FitzHugh-Nagumo evolution preserves the knot topology for all the examples
presented, thereby providing a novel field theory approach to the study of
knots. Furthermore, the evolution yields a well-defined minimal length for each
knot that is comparable to the ropelength of ideal knots. We highlight the role
of the medium boundary in stabilizing the length of the knot and discuss the
implications beyond torus knots. By applying Moffatt's test we are able to show
that there is not a unique attractor within a given knot topology. | [
0,
1,
1,
0,
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0
] |
Title: Rescaling and other forms of unsupervised preprocessing introduce bias into cross-validation,
Abstract: Cross-validation of predictive models is the de-facto standard for model
selection and evaluation. In proper use, it provides an unbiased estimate of a
model's predictive performance. However, data sets often undergo a preliminary
data-dependent transformation, such as feature rescaling or dimensionality
reduction, prior to cross-validation. It is widely believed that such a
preprocessing stage, if done in an unsupervised manner that does not consider
the class labels or response values, has no effect on the validity of
cross-validation. In this paper, we show that this belief is not true.
Preliminary preprocessing can introduce either a positive or negative bias into
the estimates of model performance. Thus, it may lead to sub-optimal choices of
model parameters and invalid inference. In light of this, the scientific
community should re-examine the use of preliminary preprocessing prior to
cross-validation across the various application domains. By default, all data
transformations, including unsupervised preprocessing stages, should be learned
only from the training samples, and then merely applied to the validation and
testing samples. | [
1,
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1,
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0
] |
Title: Causal Inference on Discrete Data via Estimating Distance Correlations,
Abstract: In this paper, we deal with the problem of inferring causal directions when
the data is on discrete domain. By considering the distribution of the cause
$P(X)$ and the conditional distribution mapping cause to effect $P(Y|X)$ as
independent random variables, we propose to infer the causal direction via
comparing the distance correlation between $P(X)$ and $P(Y|X)$ with the
distance correlation between $P(Y)$ and $P(X|Y)$. We infer "$X$ causes $Y$" if
the dependence coefficient between $P(X)$ and $P(Y|X)$ is smaller. Experiments
are performed to show the performance of the proposed method. | [
0,
0,
0,
1,
0,
0
] |
Title: A Class of Exponential Sequences with Shift-Invariant Discriminators,
Abstract: The discriminator of an integer sequence s = (s(i))_{i>=0}, introduced by
Arnold, Benkoski, and McCabe in 1985, is the function D_s(n) that sends n to
the least integer m such that the numbers s(0), s(1), ..., s(n-1) are pairwise
incongruent modulo m. In this note we present a class of exponential sequences
that have the special property that their discriminators are shift-invariant,
i.e., that the discriminator of the sequence is the same even if the sequence
is shifted by any positive constant. | [
1,
0,
1,
0,
0,
0
] |
Title: Contraction par Frobenius et modules de Steinberg,
Abstract: For a reductive group G defined over an algebraically closed field of
positive characteristic, we show that the Frobenius contraction functor of
G-modules is right adjoint to the Frobenius twist of the modules tensored with
the Steinberg module twice. It follows that the Frobenius contraction functor
preserves injectivity, good filtrations, but not semisiplicity. | [
0,
0,
1,
0,
0,
0
] |
Title: SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility,
Abstract: We present an approach towards robust lane tracking for assisted and
autonomous driving, particularly under poor visibility. Autonomous detection of
lane markers improves road safety, and purely visual tracking is desirable for
widespread vehicle compatibility and reducing sensor intrusion, cost, and
energy consumption. However, visual approaches are often ineffective because of
a number of factors, including but not limited to occlusion, poor weather
conditions, and paint wear-off. Our method, named SafeDrive, attempts to
improve visual lane detection approaches in drastically degraded visual
conditions without relying on additional active sensors. In scenarios where
visual lane detection algorithms are unable to detect lane markers, the
proposed approach uses location information of the vehicle to locate and access
alternate imagery of the road and attempts detection on this secondary image.
Subsequently, by using a combination of feature-based and pixel-based
alignment, an estimated location of the lane marker is found in the current
scene. We demonstrate the effectiveness of our system on actual driving data
from locations in the United States with Google Street View as the source of
alternate imagery. | [
1,
0,
0,
0,
0,
0
] |
Title: Exponential Integrators in Time-Dependent Density Functional Calculations,
Abstract: The integrating factor and exponential time differencing methods are
implemented and tested for solving the time-dependent Kohn--Sham equations.
Popular time propagation methods used in physics, as well as other robust
numerical approaches, are compared to these exponential integrator methods in
order to judge the relative merit of the computational schemes. We determine an
improvement in accuracy of multiple orders of magnitude when describing
dynamics driven primarily by a nonlinear potential. For cases of dynamics
driven by a time-dependent external potential, the accuracy of the exponential
integrator methods are less enhanced but still match or outperform the best of
the conventional methods tested. | [
0,
1,
0,
0,
0,
0
] |
Title: A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams,
Abstract: In a world of global trading, maritime safety, security and efficiency are
crucial issues. We propose a multi-task deep learning framework for vessel
monitoring using Automatic Identification System (AIS) data streams. We combine
recurrent neural networks with latent variable modeling and an embedding of AIS
messages to a new representation space to jointly address key issues to be
dealt with when considering AIS data streams: massive amount of streaming data,
noisy data and irregular timesampling. We demonstrate the relevance of the
proposed deep learning framework on real AIS datasets for a three-task setting,
namely trajectory reconstruction, anomaly detection and vessel type
identification. | [
0,
0,
0,
1,
0,
0
] |
Title: The CCI30 Index,
Abstract: We describe the design of the CCI30 cryptocurrency index. | [
0,
0,
0,
0,
0,
1
] |
Title: An Effective Training Method For Deep Convolutional Neural Network,
Abstract: In this paper, we propose the nonlinearity generation method to speed up and
stabilize the training of deep convolutional neural networks. The proposed
method modifies a family of activation functions as nonlinearity generators
(NGs). NGs make the activation functions linear symmetric for their inputs to
lower model capacity, and automatically introduce nonlinearity to enhance the
capacity of the model during training. The proposed method can be considered an
unusual form of regularization: the model parameters are obtained by training a
relatively low-capacity model, that is relatively easy to optimize at the
beginning, with only a few iterations, and these parameters are reused for the
initialization of a higher-capacity model. We derive the upper and lower bounds
of variance of the weight variation, and show that the initial symmetric
structure of NGs helps stabilize training. We evaluate the proposed method on
different frameworks of convolutional neural networks over two object
recognition benchmark tasks (CIFAR-10 and CIFAR-100). Experimental results
showed that the proposed method allows us to (1) speed up the convergence of
training, (2) allow for less careful weight initialization, (3) improve or at
least maintain the performance of the model at negligible extra computational
cost, and (4) easily train a very deep model. | [
1,
0,
0,
1,
0,
0
] |
Title: Investigation of Monaural Front-End Processing for Robust ASR without Retraining or Joint-Training,
Abstract: In recent years, monaural speech separation has been formulated as a
supervised learning problem, which has been systematically researched and shown
the dramatical improvement of speech intelligibility and quality for human
listeners. However, it has not been well investigated whether the methods can
be employed as the front-end processing and directly improve the performance of
a machine listener, i.e., an automatic speech recognizer, without retraining or
joint-training the acoustic model. In this paper, we explore the effectiveness
of the independent front-end processing for the multi-conditional trained ASR
on the CHiME-3 challenge. We find that directly feeding the enhanced features
to ASR can make 36.40% and 11.78% relative WER reduction for the GMM-based and
DNN-based ASR respectively. We also investigate the affect of noisy phase and
generalization ability under unmatched noise condition. | [
1,
0,
0,
0,
0,
0
] |
Title: Average treatment effects in the presence of unknown interference,
Abstract: We investigate large-sample properties of treatment effect estimators under
unknown interference in randomized experiments. The inferential target is a
generalization of the average treatment effect estimand that marginalizes over
potential spillover effects. We show that estimators commonly used to estimate
treatment effects under no-interference are consistent for the generalized
estimand for several common experimental designs under limited but otherwise
arbitrary and unknown interference. The rates of convergence depend on the rate
at which the amount of interference grows and the degree to which it aligns
with dependencies in treatment assignment. Importantly for practitioners, the
results imply that if one erroneously assumes that units do not interfere in a
setting with limited, or even moderate, interference, standard estimators are
nevertheless likely to be close to an average treatment effect if the sample is
sufficiently large. | [
0,
0,
1,
1,
0,
0
] |
Title: CN rings in full protoplanetary disks around young stars as probes of disk structure,
Abstract: Bright ring-like structure emission of the CN molecule has been observed in
protoplanetary disks. We investigate whether such structures are due to the
morphology of the disk itself or if they are instead an intrinsic feature of CN
emission. With the intention of using CN as a diagnostic, we also address to
which physical and chemical parameters CN is most sensitive. A set of disk
models were run for different stellar spectra, masses, and physical structures
via the 2D thermochemical code DALI. An updated chemical network that accounts
for the most relevant CN reactions was adopted. Ring-shaped emission is found
to be a common feature of all adopted models; the highest abundance is found in
the upper outer regions of the disk, and the column density peaks at 30-100 AU
for T Tauri stars with standard accretion rates. Higher mass disks generally
show brighter CN. Higher UV fields, such as those appropriate for T Tauri stars
with high accretion rates or for Herbig Ae stars or for higher disk flaring,
generally result in brighter and larger rings. These trends are due to the main
formation paths of CN, which all start with vibrationally excited H2*
molecules, that are produced through far ultraviolet (FUV) pumping of H2. The
model results compare well with observed disk-integrated CN fluxes and the
observed location of the CN ring for the TW Hya disk. CN rings are produced
naturally in protoplanetary disks and do not require a specific underlying disk
structure such as a dust cavity or gap. The strong link between FUV flux and CN
emission can provide critical information regarding the vertical structure of
the disk and the distribution of dust grains which affects the UV penetration,
and could help to break some degeneracies in the SED fitting. In contrast with
C2H or c-C3H2, the CN flux is not very sensitive to carbon and oxygen
depletion. | [
0,
1,
0,
0,
0,
0
] |
Title: Does mitigating ML's impact disparity require treatment disparity?,
Abstract: Following related work in law and policy, two notions of disparity have come
to shape the study of fairness in algorithmic decision-making. Algorithms
exhibit treatment disparity if they formally treat members of protected
subgroups differently; algorithms exhibit impact disparity when outcomes differ
across subgroups, even if the correlation arises unintentionally. Naturally, we
can achieve impact parity through purposeful treatment disparity. In one thread
of technical work, papers aim to reconcile the two forms of parity proposing
disparate learning processes (DLPs). Here, the learning algorithm can see group
membership during training but produce a classifier that is group-blind at test
time. In this paper, we show theoretically that: (i) When other features
correlate to group membership, DLPs will (indirectly) implement treatment
disparity, undermining the policy desiderata they are designed to address; (ii)
When group membership is partly revealed by other features, DLPs induce
within-class discrimination; and (iii) In general, DLPs provide a suboptimal
trade-off between accuracy and impact parity. Based on our technical analysis,
we argue that transparent treatment disparity is preferable to occluded methods
for achieving impact parity. Experimental results on several real-world
datasets highlight the practical consequences of applying DLPs vs. per-group
thresholds. | [
1,
0,
0,
1,
0,
0
] |
Title: A commuting-vector-field approach to some dispersive estimates,
Abstract: We prove the pointwise decay of solutions to three linear equations: (i) the
transport equation in phase space generalizing the classical Vlasov equation,
(ii) the linear Schrodinger equation, (iii) the Airy (linear KdV) equation. The
usual proofs use explicit representation formulae, and either obtain
$L^1$---$L^\infty$ decay through directly estimating the fundamental solution
in physical space, or by studying oscillatory integrals coming from the
representation in Fourier space. Our proof instead combines "vector field"
commutators that capture the inherent symmetries of the relevant equations with
conservation laws for mass and energy to get space-time weighted energy
estimates. Combined with a simple version of Sobolev's inequality this gives
pointwise decay as desired. In the case of the Vlasov and Schrodinger equations
we can recover sharp pointwise decay; in the Schrodinger case we also show how
to obtain local energy decay as well as Strichartz-type estimates. For the Airy
equation we obtain a local energy decay that is almost sharp from the scaling
point of view, but nonetheless misses the classical estimates by a gap. This
work is inspired by the work of Klainerman on $L^2$---$L^\infty$ decay of wave
equations, as well as the recent work of Fajman, Joudioux, and Smulevici on
decay of mass distributions for the relativistic Vlasov equation. | [
0,
0,
1,
0,
0,
0
] |
Title: Robust and Efficient Parametric Spectral Estimation in Atomic Force Microscopy,
Abstract: An atomic force microscope (AFM) is capable of producing ultra-high
resolution measurements of nanoscopic objects and forces. It is an
indispensable tool for various scientific disciplines such as molecular
engineering, solid-state physics, and cell biology. Prior to a given
experiment, the AFM must be calibrated by fitting a spectral density model to
baseline recordings. However, since AFM experiments typically collect large
amounts of data, parameter estimation by maximum likelihood can be
prohibitively expensive. Thus, practitioners routinely employ a much faster
least-squares estimation method, at the cost of substantially reduced
statistical efficiency. Additionally, AFM data is often contaminated by
periodic electronic noise, to which parameter estimates are highly sensitive.
This article proposes a two-stage estimator to address these issues.
Preliminary parameter estimates are first obtained by a variance-stabilizing
procedure, by which the simplicity of least-squares combines with the
efficiency of maximum likelihood. A test for spectral periodicities then
eliminates high-impact outliers, considerably and robustly protecting the
second-stage estimator from the effects of electronic noise. Simulation and
experimental results indicate that a two- to ten-fold reduction in mean squared
error can be expected by applying our methodology. | [
0,
0,
0,
1,
0,
0
] |
Title: Wall modeling via function enrichment: extension to detached-eddy simulation,
Abstract: We extend the approach of wall modeling via function enrichment to
detached-eddy simulation. The wall model aims at using coarse cells in the
near-wall region by modeling the velocity profile in the viscous sublayer and
log-layer. However, unlike other wall models, the full Navier-Stokes equations
are still discretely fulfilled, including the pressure gradient and convective
term. This is achieved by enriching the elements of the high-order
discontinuous Galerkin method with the law-of-the-wall. As a result, the
Galerkin method can "choose" the optimal solution among the polynomial and
enrichment shape functions. The detached-eddy simulation methodology provides a
suitable turbulence model for the coarse near-wall cells. The approach is
applied to wall-modeled LES of turbulent channel flow in a wide range of
Reynolds numbers. Flow over periodic hills shows the superiority compared to an
equilibrium wall model under separated flow conditions. | [
0,
1,
0,
0,
0,
0
] |
Title: Existence and uniqueness of periodic solution of nth-order Equations with delay in Banach space having Fourier type,
Abstract: The aim of this work is to study the existence of a periodic solutions of
nth-order differential equations with delay d dt x(t) + d 2 dt 2 x(t) + d 3 dt
3 x(t) + ... + d n dt n x(t) = Ax(t) + L(xt) + f (t). Our approach is based on
the M-boundedness of linear operators, Fourier type, B s p,q-multipliers and
Besov spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: Data-driven Job Search Engine Using Skills and Company Attribute Filters,
Abstract: According to a report online, more than 200 million unique users search for
jobs online every month. This incredibly large and fast growing demand has
enticed software giants such as Google and Facebook to enter this space, which
was previously dominated by companies such as LinkedIn, Indeed and
CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine",
"Google For Jobs" while Facebook released "Facebook Jobs" within their
platform. These current job search engines and platforms allow users to search
for jobs based on general narrow filters such as job title, date posted,
experience level, company and salary. However, they have severely limited
filters relating to skill sets such as C++, Python, and Java and company
related attributes such as employee size, revenue, technographics and
micro-industries. These specialized filters can help applicants and companies
connect at a very personalized, relevant and deeper level. In this paper we
present a framework that provides an end-to-end "Data-driven Jobs Search
Engine". In addition, users can also receive potential contacts of recruiters
and senior positions for connection and networking opportunities. The high
level implementation of the framework is described as follows: 1) Collect job
postings data in the United States, 2) Extract meaningful tokens from the
postings data using ETL pipelines, 3) Normalize the data set to link company
names to their specific company websites, 4) Extract and ranking the skill
sets, 5) Link the company names and websites to their respective company level
attributes with the EVERSTRING Company API, 6) Run user-specific search queries
on the database to identify relevant job postings and 7) Rank the job search
results. This framework offers a highly customizable and highly targeted search
experience for end users. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Learning for Computational Chemistry,
Abstract: The rise and fall of artificial neural networks is well documented in the
scientific literature of both computer science and computational chemistry. Yet
almost two decades later, we are now seeing a resurgence of interest in deep
learning, a machine learning algorithm based on multilayer neural networks.
Within the last few years, we have seen the transformative impact of deep
learning in many domains, particularly in speech recognition and computer
vision, to the extent that the majority of expert practitioners in those field
are now regularly eschewing prior established models in favor of deep learning
models. In this review, we provide an introductory overview into the theory of
deep neural networks and their unique properties that distinguish them from
traditional machine learning algorithms used in cheminformatics. By providing
an overview of the variety of emerging applications of deep neural networks, we
highlight its ubiquity and broad applicability to a wide range of challenges in
the field, including QSAR, virtual screening, protein structure prediction,
quantum chemistry, materials design and property prediction. In reviewing the
performance of deep neural networks, we observed a consistent outperformance
against non-neural networks state-of-the-art models across disparate research
topics, and deep neural network based models often exceeded the "glass ceiling"
expectations of their respective tasks. Coupled with the maturity of
GPU-accelerated computing for training deep neural networks and the exponential
growth of chemical data on which to train these networks on, we anticipate that
deep learning algorithms will be a valuable tool for computational chemistry. | [
1,
0,
0,
1,
0,
0
] |
Title: Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning,
Abstract: Predictive geometric models deliver excellent results for many Machine
Learning use cases. Despite their undoubted performance, neural predictive
algorithms can show unexpected degrees of instability and variance,
particularly when applied to large datasets. We present an approach to measure
changes in geometric models with respect to both output consistency and
topological stability. Considering the example of a recommender system using
word2vec, we analyze the influence of single data points, approximation methods
and parameter settings. Our findings can help to stabilize models where needed
and to detect differences in informational value of data points on a large
scale. | [
0,
0,
0,
1,
0,
0
] |
Title: Perpetual points: New tool for localization of co-existing attractors in dynamical systems,
Abstract: Perpetual points (PPs) are special critical points for which the magnitude of
acceleration describing dynamics drops to zero, while the motion is still
possible (stationary points are excluded), e.g. considering the motion of the
particle in the potential field, at perpetual point it has zero acceleration
and non-zero velocity. We show that using PPs we can trace all the stable fixed
points in the system, and that the structure of trajectories leading from
former points to stable equilibria may be similar to orbits obtained from
unstable stationary points. Moreover, we argue that the concept of perpetual
points may be useful in tracing unexpected attractors (hidden or rare
attractors with small basins of attraction). We show potential applicability of
this approach by analysing several representative systems of physical
significance, including the damped oscillator, pendula and the Henon map. We
suggest that perpetual points may be a useful tool for localization of
co-existing attractors in dynamical systems. | [
0,
1,
0,
0,
0,
0
] |
Title: Loss Functions in Restricted Parameter Spaces and Their Bayesian Applications,
Abstract: A squared error loss remains the most commonly used loss function for
constructing a Bayes estimator of the parameter of interest. It, however, can
lead to sub-optimal solutions when a parameter is defined on a restricted
space. It can also be an inappropriate choice in the context when an
overestimation and/or underestimation results in severe consequences and a more
conservative estimator is preferred. We advocate a class of loss functions for
parameters defined on restricted spaces which infinitely penalize boundary
decisions like the squared error loss does on the real line. We also recall
several properties of loss functions such as symmetry, convexity and
invariance. We propose generalizations of the squared error loss function for
parameters defined on the positive real line and on an interval. We provide
explicit solutions for corresponding Bayes estimators and discuss multivariate
extensions. Three well-known Bayesian estimation problems are used to
demonstrate inferential benefits the novel Bayes estimators can provide in the
context of restricted estimation. | [
0,
0,
1,
1,
0,
0
] |
Title: Colored Image Encryption and Decryption Using Chaotic Lorenz System and DCT2,
Abstract: In this paper, a scheme for the encryption and decryption of colored images
by using the Lorenz system and the discrete cosine transform in two dimensions
(DCT2) is proposed. Although chaos is random, it has deterministic features
that can be used for encryption; further, the same sequences can be produced at
the transmitter and receiver under the same initial conditions. Another
property of DCT2 is that the energy is concentrated in some elements of the
coefficients. These two properties are used to efficiently encrypt and recover
the image at the receiver by using three different keys with three different
predefined number of shifts for each instance of key usage. Simulation results
and statistical analysis show that the scheme high performance in weakening the
correlation between the pixels of the image that resulted from the inverse of
highest energy values of DCT2 that form 99.9 % of the energy as well as those
of the difference image. | [
1,
0,
0,
0,
0,
0
] |
Title: Self-Gluing formula of the monopole invariant and its application,
Abstract: Given a $4$-manifold $\hat{M}$ and two homeomorphic surfaces $\Sigma_1,
\Sigma_2$ smoothly embedded in $\hat{M}$ with genus more than 1, we remove the
neighborhoods of the surfaces and obtain a new $4$-manifold $M$ from gluing two
boundaries $S^1 \times \Sigma_1$ and $S^1 \times \Sigma_1.$ In this artice, we
prove a gluing formula which describes the relation of the Seiberg-Witten
invariants of $M$ and $\hat{M}.$ Moreover, we show the application of the
formula on the existence condition of the symplectic structure on a family of
$4$-manfolds under some conditions. | [
0,
0,
1,
0,
0,
0
] |
Title: Harmonic quasi-isometric maps II : negatively curved manifolds,
Abstract: We prove that a quasi-isometric map, and more generally a coarse embedding,
between pinched Hadamard manifolds is within bounded distance from a unique
harmonic map. | [
0,
0,
1,
0,
0,
0
] |
Title: Platform independent profiling of a QCD code,
Abstract: The supercomputing platforms available for high performance computing based
research evolve at a great rate. However, this rapid development of novel
technologies requires constant adaptations and optimizations of the existing
codes for each new machine architecture. In such context, minimizing time of
efficiently porting the code on a new platform is of crucial importance. A
possible solution for this common challenge is to use simulations of the
application that can assist in detecting performance bottlenecks. Due to
prohibitive costs of classical cycle-accurate simulators, coarse-grain
simulations are more suitable for large parallel and distributed systems. We
present a procedure of implementing the profiling for openQCD code [1] through
simulation, which will enable the global reduction of the cost of profiling and
optimizing this code commonly used in the lattice QCD community. Our approach
is based on well-known SimGrid simulator [2], which allows for fast and
accurate performance predictions of HPC codes. Additionally, accurate
estimations of the program behavior on some future machines, not yet accessible
to us, are anticipated. | [
1,
1,
0,
0,
0,
0
] |
Title: J0906+6930: a radio-loud quasar in the early Universe,
Abstract: Radio-loud high-redshift quasars (HRQs), although only a few of them are
known to date, are crucial for the studies of the growth of supermassive black
holes (SMBHs) and the evolution of active galactic nuclei (AGN) at early
cosmological epochs. Radio jets offer direct evidence of SMBHs, and their radio
structures can be studied with the highest angular resolution using Very Long
Baseline Interferometry (VLBI). Here we report on the observations of three
HRQs (J0131-0321, J0906+6930, J1026+2542) at z>5 using the Korean VLBI Network
and VLBI Exploration of Radio Astrometry Arrays (together known as KaVA) with
the purpose of studying their pc-scale jet properties. The observations were
carried out at 22 and 43 GHz in 2016 January among the first-batch open-use
experiments of KaVA. The quasar J0906+6930 was detected at 22 GHz but not at 43
GHz. The other two sources were not detected and upper limits to their compact
radio emission are given. Archival VLBI imaging data and single-dish 15-GHz
monitoring light curve of J0906+6930 were also acquired as complementary
information. J0906+6930 shows a moderate-level variability at 15 GHz. The radio
image is characterized by a core-jet structure with a total detectable size of
~5 pc in projection. The brightness temperature, 1.9x10^{11} K, indicates
relativistic beaming of the jet. The radio properties of J0906+6930 are
consistent with a blazar. Follow-up VLBI observations will be helpful for
determining its structural variation. | [
0,
1,
0,
0,
0,
0
] |
Title: Accelerating Innovation Through Analogy Mining,
Abstract: The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations. | [
1,
0,
0,
1,
0,
0
] |
Title: Private Information Retrieval from MDS Coded Data with Colluding Servers: Settling a Conjecture by Freij-Hollanti et al.,
Abstract: A $(K, N, T, K_c)$ instance of the MDS-TPIR problem is comprised of $K$
messages and $N$ distributed servers. Each message is separately encoded
through a $(K_c, N)$ MDS storage code. A user wishes to retrieve one message,
as efficiently as possible, while revealing no information about the desired
message index to any colluding set of up to $T$ servers. The fundamental limit
on the efficiency of retrieval, i.e., the capacity of MDS-TPIR is known only at
the extremes where either $T$ or $K_c$ belongs to $\{1,N\}$. The focus of this
work is a recent conjecture by Freij-Hollanti, Gnilke, Hollanti and Karpuk
which offers a general capacity expression for MDS-TPIR. We prove that the
conjecture is false by presenting as a counterexample a PIR scheme for the
setting $(K, N, T, K_c) = (2,4,2,2)$, which achieves the rate $3/5$, exceeding
the conjectured capacity, $4/7$. Insights from the counterexample lead us to
capacity characterizations for various instances of MDS-TPIR including all
cases with $(K, N, T, K_c) = (2,N,T,N-1)$, where $N$ and $T$ can be arbitrary. | [
1,
0,
0,
0,
0,
0
] |
Title: Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data,
Abstract: The prevalence of online media has attracted researchers from various domains
to explore human behavior and make interesting predictions. In this research,
we leverage heterogeneous social media data collected from various online
platforms to predict Taiwan's 2016 presidential election. In contrast to most
existing research, we take a "signal" view of heterogeneous information and
adopt the Kalman filter to fuse multiple signals into daily vote predictions
for the candidates. We also consider events that influenced the election in a
quantitative manner based on the so-called event study model that originated in
the field of financial research. We obtained the following interesting
findings. First, public opinions in online media dominate traditional polls in
Taiwan election prediction in terms of both predictive power and timeliness.
But offline polls can still function on alleviating the sample bias of online
opinions. Second, although online signals converge as election day approaches,
the simple Facebook "Like" is consistently the strongest indicator of the
election result. Third, most influential events have a strong connection to
cross-strait relations, and the Chou Tzu-yu flag incident followed by the
apology video one day before the election increased the vote share of Tsai
Ing-Wen by 3.66%. This research justifies the predictive power of online media
in politics and the advantages of information fusion. The combined use of the
Kalman filter and the event study method contributes to the data-driven
political analytics paradigm for both prediction and attribution purposes. | [
1,
0,
0,
1,
0,
0
] |
Title: The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments,
Abstract: Technological advancements in the field of mobile devices and wearable
sensors have helped overcome obstacles in the delivery of care, making it
possible to deliver behavioral treatments anytime and anywhere. Increasingly
the delivery of these treatments is triggered by predictions of risk or
engagement which may have been impacted by prior treatments. Furthermore the
treatments are often designed to have an impact on individuals over a span of
time during which subsequent treatments may be provided.
Here we discuss our work on the design of a mobile health smoking cessation
experimental study in which two challenges arose. First the randomizations to
treatment should occur at times of stress and second the outcome of interest
accrues over a period that may include subsequent treatment. To address these
challenges we develop the "stratified micro-randomized trial," in which each
individual is randomized among treatments at times determined by predictions
constructed from outcomes to prior treatment and with randomization
probabilities depending on these outcomes. We define both conditional and
marginal proximal treatment effects. Depending on the scientific goal these
effects may be defined over a period of time during which subsequent treatments
may be provided. We develop a primary analysis method and associated sample
size formulae for testing these effects. | [
0,
0,
0,
1,
0,
0
] |
Title: Multiplicative Convolution of Real Asymmetric and Real Antisymmetric Matrices,
Abstract: The singular values of products of standard complex Gaussian random matrices,
or sub-blocks of Haar distributed unitary matrices, have the property that
their probability distribution has an explicit, structured form referred to as
a polynomial ensemble. It is furthermore the case that the corresponding
bi-orthogonal system can be determined in terms of Meijer G-functions, and the
correlation kernel given as an explicit double contour integral. It has
recently been shown that the Hermitised product $X_M \cdots X_2 X_1A X_1^T
X_2^T \cdots X_M^T$, where each $X_i$ is a standard real complex Gaussian
matrix, and $A$ is real anti-symmetric shares exhibits analogous properties.
Here we use the theory of spherical functions and transforms to present a
theory which, for even dimensions, includes these properties of the latter
product as a special case. As an example we show that the theory also allows
for a treatment of this class of Hermitised product when the $X_i$ are chosen
as sub-blocks of Haar distributed real orthogonal matrices. | [
0,
0,
1,
0,
0,
0
] |
Title: It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal],
Abstract: In this position paper, we question the current practice of calculating
evaluation metrics for recommender systems as single numbers (e.g. precision
p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express
only average effectiveness over a usually rather long period (e.g. a year or
even longer), which provides only a vague and static view of the data. We
propose that recommender-system researchers should instead calculate metrics
for time-series such as weeks or months, and plot the results in e.g. a line
chart. This way, results show how algorithms' effectiveness develops over time,
and hence the results allow drawing more meaningful conclusions about how an
algorithm will perform in the future. In this paper, we explain our reasoning,
provide an example to illustrate our reasoning and present suggestions for what
the community should do next. | [
1,
0,
0,
0,
0,
0
] |
Title: Anomalous metals -- failed superconductors,
Abstract: The observation of metallic ground states in a variety of two-dimensional
electronic systems poses a fundamental challenge for the theory of electron
fluids. Here, we analyze evidence for the existence of a regime, which we call
the "anomalous metal regime," in diverse 2D superconducting systems driven
through a quantum superconductor to metal transition (QSMT) by tuning physical
parameters such as the magnetic field, the gate voltage in the case of systems
with a MOSFET geometry, or the degree of disorder. The principal
phenomenological observation is that in the anomalous metal, as a function of
decreasing temperature, the resistivity first drops as if the system were
approaching a superconducting ground state, but then saturates at low
temperatures to a value that can be orders of magnitude smaller than the Drude
value. The anomalous metal also shows a giant positive magneto-resistance.
Thus, it behaves as if it were a "failed superconductor." This behavior is
observed in a broad range of parameters. We moreover exhibit, by theoretical
solution of a model of superconducting grains embedded in a metallic matrix,
that as a matter of principle such anomalous metallic behavior can occur in the
neighborhood of a QSMT. However, we also argue that the robustness and
ubiquitous nature of the observed phenomena are difficult to reconcile with any
existing theoretical treatment, and speculate about the character of a more
fundamental theoretical framework. | [
0,
1,
0,
0,
0,
0
] |
Title: Computational determination of the largest lattice polytope diameter,
Abstract: A lattice (d, k)-polytope is the convex hull of a set of points in dimension
d whose coordinates are integers between 0 and k. Let {\delta}(d, k) be the
largest diameter over all lattice (d, k)-polytopes. We develop a computational
framework to determine {\delta}(d, k) for small instances. We show that
{\delta}(3, 4) = 7 and {\delta}(3, 5) = 9; that is, we verify for (d, k) = (3,
4) and (3, 5) the conjecture whereby {\delta}(d, k) is at most (k + 1)d/2 and
is achieved, up to translation, by a Minkowski sum of lattice vectors. | [
1,
0,
0,
0,
0,
0
] |
Title: A high resolution ion microscope for cold atoms,
Abstract: We report on an ion-optical system that serves as a microscope for ultracold
ground state and Rydberg atoms. The system is designed to achieve a
magnification of up to 1000 and a spatial resolution in the 100 nm range,
thereby surpassing many standard imaging techniques for cold atoms. The
microscope consists of four electrostatic lenses and a microchannel plate in
conjunction with a delay line detector in order to achieve single particle
sensitivity with high temporal and spatial resolution. We describe the design
process of the microscope including ion-optical simulations of the imaging
system and characterize aberrations and the resolution limit. Furthermore, we
present the experimental realization of the microscope in a cold atom setup and
investigate its performance by patterned ionization with a structure size down
to 2.7 {\mu}m. The microscope meets the requirements for studying various
many-body effects, ranging from correlations in cold quantum gases up to
Rydberg molecule formation. | [
0,
1,
0,
0,
0,
0
] |
Title: Finite groups with systems of $K$-$\frak{F}$-subnormal subgroups,
Abstract: Let $\frak {F}$ be a class of group. A subgroup $A$ of a finite group $G$ is
said to be $K$-$\mathfrak{F}$-subnormal in $G$ if there is a subgroup chain
$$A=A_{0} \leq A_{1} \leq \cdots \leq A_{n}=G$$ such that either $A_{i-1}
\trianglelefteq A_{i}$ or $A_{i}/(A_{i-1})_{A_{i}} \in \mathfrak{F}$ for all
$i=1, \ldots , n$. A formation $\frak {F}$ is said to be $K$-lattice provided
in every finite group $G$ the set of all its $K$-$\mathfrak{F}$-subnormal
subgroups forms a sublattice of the lattice of all subgroups of $G$.
In this paper we consider some new applications of the theory of $K$-lattice
formations. In particular, we prove the following
Theorem A. Let $\mathfrak{F}$ be a hereditary $K$-lattice saturated formation
containing all nilpotent groups.
(i) If every $\mathfrak{F}$-critical subgroup $H$ of $G$ is
$K$-$\mathfrak{F}$-subnormal in $G$ with $H/F(H)\in {\mathfrak{F}}$, then
$G/F(G)\in {\mathfrak{F}}$.
(ii) If every Schmidt subgroup of $G$ is $K$-$\mathfrak{F}$-subnormal in $G$,
then $G/G_{\mathfrak{F}}$ is abelian. | [
0,
0,
1,
0,
0,
0
] |
Title: Actions Speak Louder Than Goals: Valuing Player Actions in Soccer,
Abstract: Assessing the impact of the individual actions performed by soccer players
during games is a crucial aspect of the player recruitment process.
Unfortunately, most traditional metrics fall short in addressing this task as
they either focus on rare events like shots and goals alone or fail to account
for the context in which the actions occurred. This paper introduces a novel
advanced soccer metric for valuing any type of individual player action on the
pitch, be it with or without the ball. Our metric values each player action
based on its impact on the game outcome while accounting for the circumstances
under which the action happened. When applied to on-the-ball actions like
passes, dribbles, and shots alone, our metric identifies Argentine forward
Lionel Messi, French teenage star Kylian Mbappé, and Belgian winger Eden
Hazard as the most effective players during the 2016/2017 season. | [
0,
0,
0,
1,
0,
0
] |
Title: On the missing link between pressure drop, viscous dissipation, and the turbulent energy spectrum,
Abstract: After decades of experimental, theoretical, and numerical research in fluid
dynamics, many aspects of turbulence remain poorly understood. The main reason
for this is often attributed to the multiscale nature of turbulent flows, which
poses a formidable challenge. There are, however, properties of these flows
whose roles and inter-connections have never been clarified fully. In this
article, we present a new connection between the pressure drop, viscous
dissipation, and the turbulent energy spectrum, which, to the best of our
knowledge, has never been established prior to our work. We use this finding to
show analytically that viscous dissipation in laminar pipe flows cannot
increase the temperature of the fluid, and to also reproduce qualitatively
Nikuradse's experimental results involving pressure drops in turbulent flows in
rough pipes. | [
0,
1,
0,
0,
0,
0
] |
Title: State Space Reduction for Reachability Graph of CSM Automata,
Abstract: Classical CTL temporal logics are built over systems with interleaving model
concurrency. Many attempts are made to fight a state space explosion problem
(for instance, compositional model checking). There are some methods of
reduction of a state space based on independence of actions. However, in CSM
model, which is based on coincidences rather than on interleaving, independence
of actions cannot be defined. Therefore a state space reduction basing on
identical temporal consequences rather than on independence of action is
proposed. The new reduction is not as good as for interleaving systems, because
all successors of a state (in depth of two levels) must be obtained before a
reduction may be applied. This leads to reduction of space required for
representation of a state space, but not in time of state space construction.
Yet much savings may occur in regular state spaces for CSM systems. | [
1,
0,
0,
0,
0,
0
] |
Title: Generating Query Suggestions to Support Task-Based Search,
Abstract: We address the problem of generating query suggestions to support users in
completing their underlying tasks (which motivated them to search in the first
place). Given an initial query, these query suggestions should provide a
coverage of possible subtasks the user might be looking for. We propose a
probabilistic modeling framework that obtains keyphrases from multiple sources
and generates query suggestions from these keyphrases. Using the test suites of
the TREC Tasks track, we evaluate and analyze each component of our model. | [
1,
0,
0,
0,
0,
0
] |
Title: Symmetry and the Geometric Phase in Ultracold Hydrogen-Exchange Reactions,
Abstract: Quantum reactive scattering calculations are reported for the ultracold
hydrogen-exchange reaction and its non-reactive atom-exchange isotopic
counterparts, proceeding from excited rotational states. It is shown that while
the geometric phase (GP) does not necessarily control the reaction to all final
states one can always find final states where it does. For the isotopic
counterpart reactions these states can be used to make a measurement of the GP
effect by separately measuring the even and odd symmetry contributions, which
experimentally requires nuclear-spin final-state resolution. This follows from
symmetry considerations that make the even and odd identical-particle exchange
symmetry wavefunctions which include the GP locally equivalent to the opposite
symmetry wavefunctions which do not. This equivalence reflects the important
role discrete symmetries play in ultracold chemistry generally and highlights
the key role ultracold reactions can play in understanding fundamental aspects
of chemical reactivity. | [
0,
1,
0,
0,
0,
0
] |
Title: Parallel transport in principal 2-bundles,
Abstract: A nice differential-geometric framework for (non-abelian) higher gauge theory
is provided by principal 2-bundles, i.e. categorified principal bundles. Their
total spaces are Lie groupoids, local trivializations are kinds of Morita
equivalences, and connections are Lie-2-algebra-valued 1-forms. In this
article, we construct explicitly the parallel transport of a connection on a
principal 2-bundle. Parallel transport along a path is a Morita equivalence
between the fibres over the end points, and parallel transport along a surface
is an intertwiner between Morita equivalences. We prove that our constructions
fit into the general axiomatic framework for categorified parallel transport
and surface holonomy. | [
0,
0,
1,
0,
0,
0
] |
Title: Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit,
Abstract: Observations of astrophysical objects such as galaxies are limited by various
sources of random and systematic noise from the sky background, the optical
system of the telescope and the detector used to record the data. Conventional
deconvolution techniques are limited in their ability to recover features in
imaging data by the Shannon-Nyquist sampling theorem. Here we train a
generative adversarial network (GAN) on a sample of $4,550$ images of nearby
galaxies at $0.01<z<0.02$ from the Sloan Digital Sky Survey and conduct
$10\times$ cross validation to evaluate the results. We present a method using
a GAN trained on galaxy images that can recover features from artificially
degraded images with worse seeing and higher noise than the original with a
performance which far exceeds simple deconvolution. The ability to better
recover detailed features such as galaxy morphology from low-signal-to-noise
and low angular resolution imaging data significantly increases our ability to
study existing data sets of astrophysical objects as well as future
observations with observatories such as the Large Synoptic Sky Telescope (LSST)
and the Hubble and James Webb space telescopes. | [
0,
1,
0,
1,
0,
0
] |
Title: Trends in European flood risk over the past 150 years,
Abstract: Flood risk changes in time and is influenced by both natural and
socio-economic trends and interactions. In Europe, previous studies of
historical flood losses corrected for demographic and economic growth
("normalized") have been limited in temporal and spatial extent, leading to an
incomplete representation in trends of losses over time. In this study we
utilize a gridded reconstruction of flood exposure in 37 European countries and
a new database of damaging floods since 1870. Our results indicate that since
1870 there has been an increase in annually inundated area and number of
persons affected, contrasted by a substantial decrease in flood fatalities,
after correcting for change in flood exposure. For more recent decades we also
found a considerable decline in financial losses per year. We estimate,
however, that there is large underreporting of smaller floods beyond most
recent years, and show that underreporting has a substantial impact on observed
trends. | [
0,
0,
0,
1,
0,
0
] |
Title: Synthesis and analysis in total variation regularization,
Abstract: We generalize the bridge between analysis and synthesis estimators by Elad,
Milanfar and Rubinstein (2007) to rank deficient cases. This is a starting
point for the study of the connection between analysis and synthesis for total
variation regularized estimators. In particular, the case of first order total
variation regularized estimators over general graphs and their synthesis form
are studied.
We give a definition of the discrete graph derivative operator based on the
notion of line graph and provide examples of the synthesis form of
$k^{\text{th}}$ order total variation regularized estimators over a range of
graphs. | [
0,
0,
1,
1,
0,
0
] |
Title: Knowledge Transfer for Melanoma Screening with Deep Learning,
Abstract: Knowledge transfer impacts the performance of deep learning -- the state of
the art for image classification tasks, including automated melanoma screening.
Deep learning's greed for large amounts of training data poses a challenge for
medical tasks, which we can alleviate by recycling knowledge from models
trained on different tasks, in a scheme called transfer learning. Although much
of the best art on automated melanoma screening employs some form of transfer
learning, a systematic evaluation was missing. Here we investigate the presence
of transfer, from which task the transfer is sourced, and the application of
fine tuning (i.e., retraining of the deep learning model after transfer). We
also test the impact of picking deeper (and more expensive) models. Our results
favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an
AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated. | [
1,
0,
0,
0,
0,
0
] |
Title: Large odd order character sums and improvements of the Pólya-Vinogradov inequality,
Abstract: For a primitive Dirichlet character $\chi$ modulo $q$, we define
$M(\chi)=\max_{t } |\sum_{n \leq t} \chi(n)|$. In this paper, we study this
quantity for characters of a fixed odd order $g\geq 3$. Our main result
provides a further improvement of the classical Pólya-Vinogradov inequality
in this case. More specifically, we show that for any such character $\chi$ we
have $$M(\chi)\ll_{\varepsilon} \sqrt{q}(\log q)^{1-\delta_g}(\log\log
q)^{-1/4+\varepsilon},$$ where $\delta_g := 1-\frac{g}{\pi}\sin(\pi/g)$. This
improves upon the works of Granville and Soundararajan and of Goldmakher.
Furthermore, assuming the Generalized Riemann hypothesis (GRH) we prove that $$
M(\chi) \ll \sqrt{q} \left(\log_2 q\right)^{1-\delta_g} \left(\log_3
q\right)^{-\frac{1}{4}}\left(\log_4 q\right)^{O(1)}, $$ where $\log_j$ is the
$j$-th iterated logarithm. We also show unconditionally that this bound is best
possible (up to a power of $\log_4 q$). One of the key ingredients in the proof
of the upper bounds is a new Halász-type inequality for logarithmic mean
values of completely multiplicative functions, which might be of independent
interest. | [
0,
0,
1,
0,
0,
0
] |
Title: Estimation under group actions: recovering orbits from invariants,
Abstract: Motivated by geometric problems in signal processing, computer vision, and
structural biology, we study a class of orbit recovery problems where we
observe very noisy copies of an unknown signal, each acted upon by a random
element of some group (such as Z/p or SO(3)). The goal is to recover the orbit
of the signal under the group action in the high-noise regime. This generalizes
problems of interest such as multi-reference alignment (MRA) and the
reconstruction problem in cryo-electron microscopy (cryo-EM). We obtain
matching lower and upper bounds on the sample complexity of these problems in
high generality, showing that the statistical difficulty is intricately
determined by the invariant theory of the underlying symmetry group.
In particular, we determine that for cryo-EM with noise variance $\sigma^2$
and uniform viewing directions, the number of samples required scales as
$\sigma^6$. We match this bound with a novel algorithm for ab initio
reconstruction in cryo-EM, based on invariant features of degree at most 3. We
further discuss how to recover multiple molecular structures from heterogeneous
cryo-EM samples. | [
1,
0,
1,
0,
0,
0
] |
Title: Crystal field excitations from $\mathrm{Yb^{3+}}$ ions at defective sites in highly stuffed $\rm Yb_2Ti_2O_7$,
Abstract: The pyrochlore magnet $\rm Yb_2Ti_2O_7$ has been proposed as a quantum spin
ice candidate, a spin liquid state expected to display emergent quantum
electrodynamics with gauge photons among its elementary excitations. However,
$\rm Yb_2Ti_2O_7$'s ground state is known to be very sensitive to its precise
stoichiometry. Powder samples, produced by solid state synthesis at relatively
low temperatures, tend to be stoichiometric, while single crystals grown from
the melt tend to display weak "stuffing" wherein $\mathrm{\sim 2\%}$ of the
$\mathrm{Yb^{3+}}$, normally at the $A$ site of the $A_2B_2O_7$ pyrochlore
structure, reside as well at the $B$ site. In such samples $\mathrm{Yb^{3+}}$
ions should exist in defective environments at low levels, and be subjected to
crystalline electric fields (CEFs) very different from those at the
stoichiometric $A$ sites. New neutron scattering measurements of
$\mathrm{Yb^{3+}}$ in four compositions of $\rm Yb_{2+x}Ti_{2-x}O_{7-y}$, show
the spectroscopic signatures for these defective $\mathrm{Yb^{3+}}$ ions and
explicitly demonstrate that the spin anisotropy of the $\mathrm{Yb^{3+}}$
moment changes from XY-like for stoichiometric $\mathrm{Yb^{3+}}$, to
Ising-like for "stuffed" $B$ site $\mathrm{Yb^{3+}}$, or for $A$ site
$\mathrm{Yb^{3+}}$ in the presence of an oxygen vacancy. | [
0,
1,
0,
0,
0,
0
] |
Title: HOUDINI: Lifelong Learning as Program Synthesis,
Abstract: We present a neurosymbolic framework for the lifelong learning of algorithmic
tasks that mix perception and procedural reasoning. Reusing high-level concepts
across domains and learning complex procedures are key challenges in lifelong
learning. We show that a program synthesis approach that combines gradient
descent with combinatorial search over programs can be a more effective
response to these challenges than purely neural methods. Our framework, called
HOUDINI, represents neural networks as strongly typed, differentiable
functional programs that use symbolic higher-order combinators to compose a
library of neural functions. Our learning algorithm consists of: (1) a symbolic
program synthesizer that performs a type-directed search over parameterized
programs, and decides on the library functions to reuse, and the architectures
to combine them, while learning a sequence of tasks; and (2) a neural module
that trains these programs using stochastic gradient descent. We evaluate
HOUDINI on three benchmarks that combine perception with the algorithmic tasks
of counting, summing, and shortest-path computation. Our experiments show that
HOUDINI transfers high-level concepts more effectively than traditional
transfer learning and progressive neural networks, and that the typed
representation of networks significantly accelerates the search. | [
1,
0,
0,
1,
0,
0
] |
Title: Robust parameter determination in epidemic models with analytical descriptions of uncertainties,
Abstract: Compartmental equations are primary tools in disease spreading studies. Their
predictions are accurate for large populations but disagree with empirical and
simulated data for finite populations, where uncertainties become a relevant
factor. Starting from the agent-based approach, we investigate the role of
uncertainties and autocorrelation functions in SIS epidemic model, including
their relationship with epidemiological variables. We find new differential
equations that take uncertainties into account. The findings provide improved
predictions to the SIS model and it can offer new insights for emerging
diseases. | [
0,
0,
0,
0,
1,
0
] |
Title: Unified Halo-Independent Formalism From Convex Hulls for Direct Dark Matter Searches,
Abstract: Using the Fenchel-Eggleston theorem for convex hulls (an extension of the
Caratheodory theorem), we prove that any likelihood can be maximized by either
a dark matter 1- speed distribution $F(v)$ in Earth's frame or 2- Galactic
velocity distribution $f^{\rm gal}(\vec{u})$, consisting of a sum of delta
functions. The former case applies only to time-averaged rate measurements and
the maximum number of delta functions is $({\mathcal N}-1)$, where ${\mathcal
N}$ is the total number of data entries. The second case applies to any
harmonic expansion coefficient of the time-dependent rate and the maximum
number of terms is ${\mathcal N}$. Using time-averaged rates, the
aforementioned form of $F(v)$ results in a piecewise constant unmodulated halo
function $\tilde\eta^0_{BF}(v_{\rm min})$ (which is an integral of the speed
distribution) with at most $({\mathcal N}-1)$ downward steps. The authors had
previously proven this result for likelihoods comprised of at least one
extended likelihood, and found the best-fit halo function to be unique. This
uniqueness, however, cannot be guaranteed in the more general analysis applied
to arbitrary likelihoods. Thus we introduce a method for determining whether
there exists a unique best-fit halo function, and provide a procedure for
constructing either a pointwise confidence band, if the best-fit halo function
is unique, or a degeneracy band, if it is not. Using measurements of modulation
amplitudes, the aforementioned form of $f^{\rm gal}(\vec{u})$, which is a sum
of Galactic streams, yields a periodic time-dependent halo function
$\tilde\eta_{BF}(v_{\rm min}, t)$ which at any fixed time is a piecewise
constant function of $v_{\rm min}$ with at most ${\mathcal N}$ downward steps.
In this case, we explain how to construct pointwise confidence and degeneracy
bands from the time-averaged halo function. Finally, we show that requiring an
isotropic ... | [
0,
1,
0,
0,
0,
0
] |
Title: Encrypted accelerated least squares regression,
Abstract: Information that is stored in an encrypted format is, by definition, usually
not amenable to statistical analysis or machine learning methods. In this paper
we present detailed analysis of coordinate and accelerated gradient descent
algorithms which are capable of fitting least squares and penalised ridge
regression models, using data encrypted under a fully homomorphic encryption
scheme. Gradient descent is shown to dominate in terms of encrypted
computational speed, and theoretical results are proven to give parameter
bounds which ensure correctness of decryption. The characteristics of encrypted
computation are empirically shown to favour a non-standard acceleration
technique. This demonstrates the possibility of approximating conventional
statistical regression methods using encrypted data without compromising
privacy. | [
1,
0,
0,
1,
0,
0
] |
Title: Unified Model of Chaotic Inflation and Dynamical Supersymmetry Breaking,
Abstract: The large hierarchy between the Planck scale and the weak scale can be
explained by the dynamical breaking of supersymmetry in strongly coupled gauge
theories. Similarly, the hierarchy between the Planck scale and the energy
scale of inflation may also originate from strong dynamics, which dynamically
generate the inflaton potential. We present a model of the hidden sector which
unifies these two ideas, i.e., in which the scales of inflation and
supersymmetry breaking are provided by the dynamics of the same gauge group.
The resultant inflation model is chaotic inflation with a fractional power-law
potential in accord with the upper bound on the tensor-to-scalar ratio. The
supersymmetry breaking scale can be much smaller than the inflation scale, so
that the solution to the large hierarchy problem of the weak scale remains
intact. As an intrinsic feature of our model, we find that the sgoldstino,
which might disturb the inflationary dynamics, is automatically stabilized
during inflation by dynamically generated corrections in the strongly coupled
sector. This renders our model a field-theoretical realization of what is
sometimes referred to as sgoldstino-less inflation. | [
0,
1,
0,
0,
0,
0
] |
Title: Sparse Data Driven Mesh Deformation,
Abstract: Example-based mesh deformation methods are powerful tools for realistic shape
editing. However, existing techniques typically combine all the example
deformation modes, which can lead to overfitting, i.e. using a overly
complicated model to explain the user-specified deformation. This leads to
implausible or unstable deformation results, including unexpected global
changes outside the region of interest. To address this fundamental limitation,
we propose a sparse blending method that automatically selects a smaller number
of deformation modes to compactly describe the desired deformation. This along
with a suitably chosen deformation basis including spatially localized
deformation modes leads to significant advantages, including more meaningful,
reliable, and efficient deformations because fewer and localized deformation
modes are applied. To cope with large rotations, we develop a simple but
effective representation based on polar decomposition of deformation gradients,
which resolves the ambiguity of large global rotations using an
as-consistent-as-possible global optimization. This simple representation has a
closed form solution for derivatives, making it efficient for sparse localized
representation and thus ensuring interactive performance. Experimental results
show that our method outperforms state-of-the-art data-driven mesh deformation
methods, for both quality of results and efficiency. | [
1,
0,
0,
0,
0,
0
] |
Title: Consistent nonparametric change point detection combining CUSUM and marked empirical processes,
Abstract: A weakly dependent time series regression model with multivariate covariates
and univariate observations is considered, for which we develop a procedure to
detect whether the nonparametric conditional mean function is stable in time
against change point alternatives. Our proposal is based on a modified CUSUM
type test procedure, which uses a sequential marked empirical process of
residuals. We show weak convergence of the considered process to a centered
Gaussian process under the null hypothesis of no change in the mean function
and a stationarity assumption. This requires some sophisticated arguments for
sequential empirical processes of weakly dependent variables. As a consequence
we obtain convergence of Kolmogorov-Smirnov and Cramér-von Mises type test
statistics. The proposed procedure acquires a very simple limiting distribution
and nice consistency properties, features from which related tests are lacking.
We moreover suggest a bootstrap version of the procedure and discuss its
applicability in the case of unstable variances. | [
0,
0,
1,
1,
0,
0
] |
Title: Nonlinear electric field effect on perpendicular magnetic anisotropy in Fe/MgO interfaces,
Abstract: The electric field effect on magnetic anisotropy was studied in an ultrathin
Fe(001) monocrystalline layer sandwiched between Cr buffer and MgO tunnel
barrier layers, mainly through post-annealing temperature and measurement
temperature dependences. A large coefficient of the electric field effect of
more than 200 fJ/Vm was observed in the negative range of electric field, as
well as an areal energy density of perpendicular magnetic anisotropy (PMA) of
around 600 uJ/m2. More interestingly, nonlinear behavior, giving rise to a
local minimum around +100 mV/nm, was observed in the electric field dependence
of magnetic anisotropy, being independent of the post-annealing and measurement
temperatures. The insensitivity to both the interface conditions and the
temperature of the system suggests that the nonlinear behavior is attributed to
an intrinsic origin such as an inherent electronic structure in the Fe/MgO
interface. The present study can contribute to the progress in theoretical
studies, such as ab initio calculations, on the mechanism of the electric field
effect on PMA. | [
0,
1,
0,
0,
0,
0
] |
Title: A Weighted Model Confidence Set: Applications to Local and Mixture Model Confidence Sets,
Abstract: This article provides a weighted model confidence set, whenever underling
model has been misspecified and some part of support of random variable $X$
conveys some important information about underling true model. Application of
such weighted model confidence set for local and mixture model confidence sets
have been given. Two simulation studies have been conducted to show practical
application of our findings. | [
0,
0,
0,
1,
0,
0
] |
Title: Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization,
Abstract: The cooperative hierarchical structure is a common and significant data
structure observed in, or adopted by, many research areas, such as: text mining
(author-paper-word) and multi-label classification (label-instance-feature).
Renowned Bayesian approaches for cooperative hierarchical structure modeling
are mostly based on topic models. However, these approaches suffer from a
serious issue in that the number of hidden topics/factors needs to be fixed in
advance and an inappropriate number may lead to overfitting or underfitting.
One elegant way to resolve this issue is Bayesian nonparametric learning, but
existing work in this area still cannot be applied to cooperative hierarchical
structure modeling.
In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP)
to fill this gap. Each node in a cooperative hierarchical structure is assigned
a Dirichlet process to model its weights on the infinite hidden factors/topics.
Together with measure inheritance from hierarchical Dirichlet process, two
kinds of measure cooperation, i.e., superposition and maximization, are defined
to capture the many-to-many relationships in the cooperative hierarchical
structure. Furthermore, two constructive representations for CHDP, i.e.,
stick-breaking and international restaurant process, are designed to facilitate
the model inference. Experiments on synthetic and real-world data with
cooperative hierarchical structures demonstrate the properties and the ability
of CHDP for cooperative hierarchical structure modeling and its potential for
practical application scenarios. | [
1,
0,
0,
1,
0,
0
] |
Title: The extended law of star formation: the combined role of gas and stars,
Abstract: We present a model for the origin of the extended law of star formation in
which the surface density of star formation ($\Sigma_{\rm SFR}$) depends not
only on the local surface density of the gas ($\Sigma_{g}$), but also on the
stellar surface density ($\Sigma_{*}$), the velocity dispersion of the stars,
and on the scaling laws of turbulence in the gas. We compare our model with the
spiral, face-on galaxy NGC 628 and show that the dependence of the star
formation rate on the entire set of physical quantities for both gas and stars
can help explain both the observed general trends in the
$\Sigma_{g}-\Sigma_{\rm SFR}$ and $\Sigma_{*}-\Sigma_{\rm SFR}$ relations, but
also, and equally important, the scatter in these relations at any value of
$\Sigma_{g}$ and $\Sigma_{*}$. Our results point out to the crucial role played
by existing stars along with the gaseous component in setting the conditions
for large scale gravitational instabilities and star formation in galactic
disks. | [
0,
1,
0,
0,
0,
0
] |
Title: Local and global similarity of holomorphic matrices,
Abstract: R. Guralnick (Linear Algebra Appl. 99, 85-96, 1988) proved that two
holomorphic matrices on a noncompact connected Riemann surface, which are
locally holomorphically similar, are globally holomorphically similar. We
generalize this to (possibly, non-smooth) one-dimensional Stein spaces. For
Stein spaces of arbitrary dimension, we prove that global $\mathcal C^\infty$
similarity implies global holomorphic similarity, whereas global continuous
similarity is not sufficient. | [
0,
0,
1,
0,
0,
0
] |
Title: WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models,
Abstract: Learning sparse linear models with two-way interactions is desirable in many
application domains such as genomics. l1-regularised linear models are popular
to estimate sparse models, yet standard implementations fail to address
specifically the quadratic explosion of candidate two-way interactions in high
dimensions, and typically do not scale to genetic data with hundreds of
thousands of features. Here we present WHInter, a working set algorithm to
solve large l1-regularised problems with two-way interactions for binary design
matrices. The novelty of WHInter stems from a new bound to efficiently identify
working sets while avoiding to scan all features, and on fast computations
inspired from solutions to the maximum inner product search problem. We apply
WHInter to simulated and real genetic data and show that it is more scalable
and two orders of magnitude faster than the state of the art. | [
0,
0,
0,
1,
1,
0
] |
Title: $\aleph_1$ and the modal $μ$-calculus,
Abstract: For a regular cardinal $\kappa$, a formula of the modal $\mu$-calculus is
$\kappa$-continuous in a variable x if, on every model, its interpretation as a
unary function of x is monotone and preserves unions of $\kappa$-directed sets.
We define the fragment $C_{\aleph_1}(x)$ of the modal $\mu$-calculus and prove
that all the formulas in this fragment are $\aleph_1$-continuous. For each
formula $\phi(x)$ of the modal $\mu$-calculus, we construct a formula $\psi(x)
\in C_{\aleph_1 }(x)$ such that $\phi(x)$ is $\kappa$-continuous, for some
$\kappa$, if and only if $\phi(x)$ is equivalent to $\psi(x)$. Consequently, we
prove that (i) the problem whether a formula is $\kappa$-continuous for some
$\kappa$ is decidable, (ii) up to equivalence, there are only two fragments
determined by continuity at some regular cardinal: the fragment
$C_{\aleph_0}(x)$ studied by Fontaine and the fragment $C_{\aleph_1}(x)$. We
apply our considerations to the problem of characterizing closure ordinals of
formulas of the modal $\mu$-calculus. An ordinal $\alpha$ is the closure
ordinal of a formula $\phi(x)$ if its interpretation on every model converges
to its least fixed-point in at most $\alpha$ steps and if there is a model
where the convergence occurs exactly in $\alpha$ steps. We prove that
$\omega_1$, the least uncountable ordinal, is such a closure ordinal. Moreover
we prove that closure ordinals are closed under ordinal sum. Thus, any formal
expression built from 0, 1, $\omega$, $\omega_1$ by using the binary operator
symbol + gives rise to a closure ordinal. | [
1,
0,
1,
0,
0,
0
] |
Title: Optimal Service Elasticity in Large-Scale Distributed Systems,
Abstract: A fundamental challenge in large-scale cloud networks and data centers is to
achieve highly efficient server utilization and limit energy consumption, while
providing excellent user-perceived performance in the presence of uncertain and
time-varying demand patterns. Auto-scaling provides a popular paradigm for
automatically adjusting service capacity in response to demand while meeting
performance targets, and queue-driven auto-scaling techniques have been widely
investigated in the literature. In typical data center architectures and cloud
environments however, no centralized queue is maintained, and load balancing
algorithms immediately distribute incoming tasks among parallel queues. In
these distributed settings with vast numbers of servers, centralized
queue-driven auto-scaling techniques involve a substantial communication
overhead and major implementation burden, or may not even be viable at all.
Motivated by the above issues, we propose a joint auto-scaling and load
balancing scheme which does not require any global queue length information or
explicit knowledge of system parameters, and yet provides provably near-optimal
service elasticity. We establish the fluid-level dynamics for the proposed
scheme in a regime where the total traffic volume and nominal service capacity
grow large in proportion. The fluid-limit results show that the proposed scheme
achieves asymptotic optimality in terms of user-perceived delay performance as
well as energy consumption. Specifically, we prove that both the waiting time
of tasks and the relative energy portion consumed by idle servers vanish in the
limit. At the same time, the proposed scheme operates in a distributed fashion
and involves only constant communication overhead per task, thus ensuring
scalability in massive data center operations. | [
1,
0,
1,
0,
0,
0
] |
Title: High brightness electron beam for radiation therapy: A new approach,
Abstract: I propose to use high brightness electron beam with 1 to 100 MeV energy as
tool to combat tumor or cancerous tissues in deep part of body. The method is
to directly deliver the electron beam to the tumor site via a small tube that
connected to a high brightness electron beam accelerator that is commonly
available around the world. Here I gave a basic scheme on the principle, I
believe other issues people raises will be solved easily for those who are
interested in solving the problems. | [
0,
1,
0,
0,
0,
0
] |
Title: Translating Terminological Expressions in Knowledge Bases with Neural Machine Translation,
Abstract: Our work presented in this paper focuses on the translation of terminological
expressions represented in semantically structured resources, like ontologies
or knowledge graphs. The challenge of translating ontology labels or
terminological expressions represented in knowledge bases lies in the highly
specific vocabulary and the lack of contextual information, which can guide a
machine translation system to translate ambiguous words into the targeted
domain. Due to these challenges, we evaluate the translation quality of
domain-specific expressions in the medical and financial domain with
statistical (SMT) as well as with neural machine translation (NMT) methods and
experiment domain adaptation of the translation models with terminological
expressions only. Furthermore, we perform experiments on the injection of
external terminological expressions into the translation systems. Through these
experiments, we observed a significant advantage in domain adaptation for the
domain-specific resource in the medical and financial domain and the benefit of
subword models over word-based NMT models for terminology translation. | [
1,
0,
0,
0,
0,
0
] |
Title: Phase Transitions in the Pooled Data Problem,
Abstract: In this paper, we study the pooled data problem of identifying the labels
associated with a large collection of items, based on a sequence of pooled
tests revealing the counts of each label within the pool. In the noiseless
setting, we identify an exact asymptotic threshold on the required number of
tests with optimal decoding, and prove a phase transition between complete
success and complete failure. In addition, we present a novel noisy variation
of the problem, and provide an information-theoretic framework for
characterizing the required number of tests for general random noise models.
Our results reveal that noise can make the problem considerably more difficult,
with strict increases in the scaling laws even at low noise levels. Finally, we
demonstrate similar behavior in an approximate recovery setting, where a given
number of errors is allowed in the decoded labels. | [
1,
0,
0,
1,
0,
0
] |
Title: Model Risk Measurement under Wasserstein Distance,
Abstract: The paper proposes a new approach to model risk measurement based on the
Wasserstein distance between two probability measures. It formulates the
theoretical motivation resulting from the interpretation of fictitious
adversary of robust risk management. The proposed approach accounts for all
alternative models and incorporates the economic reality of the fictitious
adversary. It provides practically feasible results that overcome the
restriction and the integrability issue imposed by the nominal model. The
Wasserstein approach suits for all types of model risk problems, ranging from
the single-asset hedging risk problem to the multi-asset allocation problem.
The robust capital allocation line, accounting for the correlation risk, is not
achievable with other non-parametric approaches. | [
0,
0,
0,
0,
0,
1
] |
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