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Direct frequency-comb spectroscopy of $6S_{1/2}$-$8S_{1/2}$ transitions of atomic cesium | Direct frequency-comb spectroscopy is used to probe the absolute frequencies
of $6S_{1/2}$-$8S_{1/2}$ two-photon transitions of atomic cesium in hot vapor
environment. By utilizing the coherent control method of temporally splitting
the laser spectrum above and below the two-photon resonance frequency,
Doppler-free absorption is built in two spatially distinct locations and imaged
for high-precision spectroscopy. Theoretical analysis finds that these
transition lines are measured with uncertainty below $5\times10^{-10}$, mainly
contributed from laser-induced AC Stark shift.
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Aggregation and Disaggregation of Energetic Flexibility from Distributed Energy Resources | A variety of energy resources has been identified as being flexible in their
electric energy consumption or generation. This energetic flexibility can be
used for various purposes such as minimizing energy procurement costs or
providing ancillary services to power grids. To fully leverage the flexibility
available from distributed small-scale resources, their flexibility must be
quantified and aggregated.
This paper introduces a generic and scalable approach for flexible energy
systems to quantitatively describe and price their flexibility based on
zonotopic sets. The description proposed allows aggregators to efficiently pool
the flexibility of large numbers of systems and to make control and market
decisions on the aggregate level. In addition, an algorithm is presented that
distributes aggregate-level control decisions among the individual systems of
the pool in an economically fair and computationally efficient way. Finally, it
is shown how the zonotopic description of flexibility enables an efficient
computation of aggregate regulation power bid-curves.
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Mapping Walls of Indoor Environment using RGB-D Sensor | Inferring walls configuration of indoor environment could help robot
"understand" the environment better. This allows the robot to execute a task
that involves inter-room navigation, such as picking an object in the kitchen.
In this paper, we present a method to inferring walls configuration from a
moving RGB-D sensor. Our goal is to combine a simple wall configuration model
and fast wall detection method in order to get a system that works online, is
real-time, and does not need a Manhattan World assumption. We tested our
preliminary work, i.e. wall detection and measurement from moving RGB-D sensor,
with MIT Stata Center Dataset. The performance of our method is reported in
terms of accuracy and speed of execution.
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The Effects of Memory Replay in Reinforcement Learning | Experience replay is a key technique behind many recent advances in deep
reinforcement learning. Allowing the agent to learn from earlier memories can
speed up learning and break undesirable temporal correlations. Despite its
wide-spread application, very little is understood about the properties of
experience replay. How does the amount of memory kept affect learning dynamics?
Does it help to prioritize certain experiences? In this paper, we address these
questions by formulating a dynamical systems ODE model of Q-learning with
experience replay. We derive analytic solutions of the ODE for a simple
setting. We show that even in this very simple setting, the amount of memory
kept can substantially affect the agent's performance. Too much or too little
memory both slow down learning. Moreover, we characterize regimes where
prioritized replay harms the agent's learning. We show that our analytic
solutions have excellent agreement with experiments. Finally, we propose a
simple algorithm for adaptively changing the memory buffer size which achieves
consistently good empirical performance.
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Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning | We investigate the problem of dynamic portfolio optimization in
continuous-time, finite-horizon setting for a portfolio of two stocks and one
risk-free asset. The stocks follow the Cointelation model. The proposed
optimization methods are twofold. In what we call an Stochastic Differential
Equation approach, we compute the optimal weights using mean-variance criterion
and power utility maximization. We show that dynamically switching between
these two optimal strategies by introducing a triggering function can further
improve the portfolio returns. We contrast this with the machine learning
clustering methodology inspired by the band-wise Gaussian mixture model. The
first benefit of the machine learning over the Stochastic Differential Equation
approach is that we were able to achieve the same results though a simpler
channel. The second advantage is a flexibility to regime change.
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High-mass Starless Clumps in the inner Galactic Plane: the Sample and Dust Properties | We report a sample of 463 high-mass starless clump (HMSC) candidates within
$-60°<l<60°$ and $-1°<b<1°$. This sample has been singled out from
10861 ATLASGAL clumps. All of these sources are not associated with any known
star-forming activities collected in SIMBAD and young stellar objects
identified using color-based criteria. We also make sure that the HMSC
candidates have neither point sources at 24 and 70 \micron~nor strong extended
emission at 24 $\mu$m. Most of the identified HMSCs are infrared ($\le24$
$\mu$m) dark and some are even dark at 70 $\mu$m. Their distribution shows
crowding in Galactic spiral arms and toward the Galactic center and some
well-known star-forming complexes. Many HMSCs are associated with large-scale
filaments. Some basic parameters were attained from column density and dust
temperature maps constructed via fitting far-infrared and submillimeter
continuum data to modified blackbodies. The HMSC candidates have sizes, masses,
and densities similar to clumps associated with Class II methanol masers and
HII regions, suggesting they will evolve into star-forming clumps. More than
90% of the HMSC candidates have densities above some proposed thresholds for
forming high-mass stars. With dust temperatures and luminosity-to-mass ratios
significantly lower than that for star-forming sources, the HMSC candidates are
externally heated and genuinely at very early stages of high-mass star
formation. Twenty sources with equivalent radius $r_\mathrm{eq}<0.15$ pc and
mass surface density $\Sigma>0.08$ g cm$^{-2}$ could be possible high-mass
starless cores. Further investigations toward these HMSCs would undoubtedly
shed light on comprehensively understanding the birth of high-mass stars.
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Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics | Artificial neural networks that learn to perform Principal Component Analysis
(PCA) and related tasks using strictly local learning rules have been
previously derived based on the principle of similarity matching: similar pairs
of inputs should map to similar pairs of outputs. However, the operation of
these networks (and of similar networks) requires a fixed-point iteration to
determine the output corresponding to a given input, which means that dynamics
must operate on a faster time scale than the variation of the input. Further,
during these fast dynamics such networks typically "disable" learning, updating
synaptic weights only once the fixed-point iteration has been resolved. Here,
we derive a network for PCA-based dimensionality reduction that avoids this
fast fixed-point iteration. The key novelty of our approach is a modification
of the similarity matching objective to encourage near-diagonality of a
synaptic weight matrix. We then approximately invert this matrix using a Taylor
series approximation, replacing the previous fast iterations. In the offline
setting, our algorithm corresponds to a dynamical system, the stability of
which we rigorously analyze. In the online setting (i.e., with stochastic
gradients), we map our algorithm to a familiar neural network architecture and
give numerical results showing that our method converges at a competitive rate.
The computational complexity per iteration of our online algorithm is linear in
the total degrees of freedom, which is in some sense optimal.
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Exploring to learn visual saliency: The RL-IAC approach | The problem of object localization and recognition on autonomous mobile
robots is still an active topic. In this context, we tackle the problem of
learning a model of visual saliency directly on a robot. This model, learned
and improved on-the-fly during the robot's exploration provides an efficient
tool for localizing relevant objects within their environment. The proposed
approach includes two intertwined components. On the one hand, we describe a
method for learning and incrementally updating a model of visual saliency from
a depth-based object detector. This model of saliency can also be exploited to
produce bounding box proposals around objects of interest. On the other hand,
we investigate an autonomous exploration technique to efficiently learn such a
saliency model. The proposed exploration, called Reinforcement
Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's
exploration so that samples selected by the robot are likely to improve the
current model of saliency. We then demonstrate that such a saliency model
learned directly on a robot outperforms several state-of-the-art saliency
techniques, and that RL-IAC can drastically decrease the required time for
learning a reliable saliency model.
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Fast and Robust Shortest Paths on Manifolds Learned from Data | We propose a fast, simple and robust algorithm for computing shortest paths
and distances on Riemannian manifolds learned from data. This amounts to
solving a system of ordinary differential equations (ODEs) subject to boundary
conditions. Here standard solvers perform poorly because they require
well-behaved Jacobians of the ODE, and usually, manifolds learned from data
imply unstable and ill-conditioned Jacobians. Instead, we propose a fixed-point
iteration scheme for solving the ODE that avoids Jacobians. This enhances the
stability of the solver, while reduces the computational cost. In experiments
involving both Riemannian metric learning and deep generative models we
demonstrate significant improvements in speed and stability over both
general-purpose state-of-the-art solvers as well as over specialized solvers.
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Typed Graph Networks | Recently, the deep learning community has given growing attention to neural
architectures engineered to learn problems in relational domains. Convolutional
Neural Networks employ parameter sharing over the image domain, tying the
weights of neural connections on a grid topology and thus enforcing the
learning of a number of convolutional kernels. By instantiating trainable
neural modules and assembling them in varied configurations (apart from grids),
one can enforce parameter sharing over graphs, yielding models which can
effectively be fed with relational data. In this context, vertices in a graph
can be projected into a hyperdimensional real space and iteratively refined
over many message-passing iterations in an end-to-end differentiable
architecture. Architectures of this family have been referred to with several
definitions in the literature, such as Graph Neural Networks, Message-passing
Neural Networks, Relational Networks and Graph Networks. In this paper, we
revisit the original Graph Neural Network model and show that it generalises
many of the recent models, which in turn benefit from the insight of thinking
about vertex \textbf{types}. To illustrate the generality of the original
model, we present a Graph Neural Network formalisation, which partitions the
vertices of a graph into a number of types. Each type represents an entity in
the ontology of the problem one wants to learn. This allows - for instance -
one to assign embeddings to edges, hyperedges, and any number of global
attributes of the graph. As a companion to this paper we provide a
Python/Tensorflow library to facilitate the development of such architectures,
with which we instantiate the formalisation to reproduce a number of models
proposed in the current literature.
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The effect of different in-chain impurities on the magnetic properties of the spin chain compound SrCuO$_2$ probed by NMR | The S=1/2 Heisenberg spin chain compound SrCuO2 doped with different amounts
of nickel (Ni), palladium (Pd), zinc (Zn) and cobalt (Co) has been studied by
means of Cu nuclear magnetic resonance (NMR). Replacing only a few of the S=1/2
Cu ions with Ni, Pd, Zn or Co has a major impact on the magnetic properties of
the spin chain system. In the case of Ni, Pd and Zn an unusual line broadening
in the low temperature NMR spectra reveals the existence of an impurity-induced
local alternating magnetization (LAM), while exponentially decaying
spin-lattice relaxation rates $T_1^{-1}$ towards low temperatures indicate the
opening of spin gaps. A distribution of gap magnitudes is proven by a stretched
spin-lattice relaxation and a variation of $T_1^{-1}$ within the broad
resonance lines. These observations depend strongly on the impurity
concentration and therefore can be understood using the model of finite
segments of the spin 1/2 antiferromagnetic Heisenberg chain, i.e. pure chain
segmentation due to S = 0 impurities. This is surprising for Ni as it was
previously assumed to be a magnetic impurity with S = 1 which is screened by
the neighboring copper spins. In order to confirm the S = 0 state of the Ni, we
performed x-ray absorption spectroscopy (XAS) and compared the measurements to
simulated XAS spectra based on multiplet ligand-field theory. Furthermore, Zn
doping leads to much smaller effects on both the NMR spectra and the
spin-lattice relaxation rates, indicating that Zn avoids occupying Cu sites.
For magnetic Co impurities, $T_1^{-1}$ does not obey the gap like decrease, and
the low-temperature spectra get very broad. This could be related to the
increase of the Neel temperature which was observed by recent muSR and
susceptibility measurements, and is most likely an effect of the impurity spin
$S\neq0$.
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L-groups and the Langlands program for covering groups: a historical introduction | In this joint introduction to an Asterisque volume, we give a short
discussion of the historical developments in the study of nonlinear covering
groups, touching on their structure theory, representation theory and the
theory of automorphic forms. This serves as a historical motivation and sets
the scene for the papers in the volume. Our discussion is necessarily
subjective and will undoubtedly leave out the contributions of many authors, to
whom we apologize in earnest.
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Uncertainty Reduction for Stochastic Processes on Complex Networks | Many real-world systems are characterized by stochastic dynamical rules where
a complex network of interactions among individual elements probabilistically
determines their state. Even with full knowledge of the network structure and
of the stochastic rules, the ability to predict system configurations is
generally characterized by a large uncertainty. Selecting a fraction of the
nodes and observing their state may help to reduce the uncertainty about the
unobserved nodes. However, choosing these points of observation in an optimal
way is a highly nontrivial task, depending on the nature of the stochastic
process and on the structure of the underlying interaction pattern. In this
paper, we introduce a computationally efficient algorithm to determine
quasioptimal solutions to the problem. The method leverages network sparsity to
reduce computational complexity from exponential to almost quadratic, thus
allowing the straightforward application of the method to mid-to-large-size
systems. Although the method is exact only for equilibrium stochastic processes
defined on trees, it turns out to be effective also for out-of-equilibrium
processes on sparse loopy networks.
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Light propagation in Extreme Conditions - The role of optically clear tissues and scattering layers in optical biomedical imaging | The field of biomedical imaging has undergone a rapid growth in recent years,
mostly due to the implementation of ad-hoc designed experimental setups,
theoretical support methods and numerical reconstructions. Especially for
biological samples, the high number of scattering events occurring during the
photon propagation process limit the penetration depth and the possibility to
outperform direct imaging in thicker and not transparent samples. In this
thesis, we will examine theoretically and experimentally the scattering process
from two opposite points of view, focusing also on the continuous stimulus
offered by the will to tackle some specific challenges in the emerging optical
imaging science. Firstly, we will discuss the light propagation in diffusive
biological tissues considering the particular case of the presence of optically
transparent regions enclosed in a highly scattering environment. The correct
inclusion of this information, can ultimately lead to higher resolution
reconstruction, especially in neuroimaging. On the other hand, we will examine
the extreme case of the three-dimensional imaging of a totally hidden sample,
in which the phase has been scrambled by a random scattering layer. By making
use of appropriate numerical methods, we will prove how it is possible to
outperform such hidden reconstruction in a very efficient way, opening the path
toward the unexplored field of three-dimensional hidden imaging. Finally, we
will present how, the properties noticed while addressing these problems,
leaded us to the development of a novel alignment-free three-dimensional
tomographic technique that we refer to as Phase-Retrieved Tomography.
Ultimately, we used this technique for the study of the fluorescence
distribution in a three-dimensional spherical tumor model, the cancer cell
spheroid, one of the most important biological model for the study of such
disease.
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Design discussion on the ISDA Common Domain Model | A new initiative from the International Swaps and Derivatives Association
(ISDA) aims to establish a "Common Domain Model" (ISDA CDM): a new standard for
data and process representation across the full range of derivatives
instruments. Design of the ISDA CDM is at an early stage and the draft
definition contains considerable complexity. This paper contributes by offering
insight, analysis and discussion relating to key topics in the design space
such as data lineage, timestamps, consistency, operations, events, state and
state transitions.
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Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models | Hierarchical models are utilized in a wide variety of problems which are
characterized by task hierarchies, where predictions on smaller subtasks are
useful for trying to predict a final task. Typically, neural networks are first
trained for the subtasks, and the predictions of these networks are
subsequently used as additional features when training a model and doing
inference for a final task. In this work, we focus on improving learning for
such hierarchical models and demonstrate our method on the task of speaker
trait prediction. Speaker trait prediction aims to computationally identify
which personality traits a speaker might be perceived to have, and has been of
great interest to both the Artificial Intelligence and Social Science
communities. Persuasiveness prediction in particular has been of interest, as
persuasive speakers have a large amount of influence on our thoughts, opinions
and beliefs. In this work, we examine how leveraging the relationship between
related speaker traits in a hierarchical structure can help improve our ability
to predict how persuasive a speaker is. We present a novel algorithm that
allows us to backpropagate through this hierarchy. This hierarchical model
achieves a 25% relative error reduction in classification accuracy over current
state-of-the art methods on the publicly available POM dataset.
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Atomistic simulations of dislocation/precipitation interactions in Mg-Al alloys and implications for precipitation hardening | Atomistic simulations were carried out to analyze the interaction between $<
a>$ basal dislocations and precipitates in Mg-Al alloys and the associated
strengthening mechanisms.
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Recovering Dense Tissue Multispectral Signal from in vivo RGB Images | Hyperspectral/multispectral imaging (HSI/MSI) contains rich information
clinical applications, such as 1) narrow band imaging for vascular
visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and
clinical decision making [1]; 3) tissue classification and identification of
pathology [2]. The current systems which provide pixel-level HSI/MSI signal can
be generally divided into two types: spatial scanning and spectral scanning.
However, the trade-off between spatial/spectral resolution, the acquisition
time, and the hardware complexity hampers implementation in real-world
applications, especially intra-operatively. Acquiring high resolution images in
real-time is important for HSI/MSI in intra-operative imaging, to alleviate the
side effect caused by breathing, heartbeat, and other sources of motion.
Therefore, we developed an algorithm to recover a pixel-level MSI stack using
only the captured snapshot RGB images from a normal camera. We refer to this
technique as "super-spectral-resolution". The proposed method enables recovery
of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per
second (FPS) on a GPU. Multispectral data captured from porcine bowel and
sheep/rabbit uteri in vivo has been used for training, and the algorithm has
been validated using unseen in vivo animal experiments.
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Atomically thin gallium layers from solid-melt exfoliation | Among the large number of promising two-dimensional (2D) atomic layer
crystals, true metallic layers are rare. Through combined theoretical and
experimental approaches, we report on the stability and successful exfoliation
of atomically thin gallenene sheets, having two distinct atomic arrangements
along crystallographic twin directions of the parent alpha-gallium. Utilizing
the weak interface between solid and molten phases of gallium, a solid-melt
interface exfoliation technique is developed to extract these layers. Phonon
dispersion calculations show that gallenene can be stabilized with bulk gallium
lattice parameters. The electronic band structure of gallenene shows a
combination of partially filled Dirac cone and the non-linear dispersive band
near Fermi level suggesting that gallenene should behave as a metallic layer.
Furthermore it is observed that strong interaction of gallenene with other 2D
semiconductors induces semiconducting to metallic phase transitions in the
latter paving the way for using gallenene as interesting metallic contacts in
2D devices.
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Novel Universality Classes in Ferroelectric Liquid Crystals | Starting from a Langevin formulation of a thermally perturbed nonlinear
elastic model of the ferroelectric smectic-C$^*$ (SmC${*}$) liquid crystals in
the presence of an electric field, this article characterizes the hitherto
unexplored dynamical phase transition from a thermo-electrically forced
ferroelectric SmC${}^{*}$ phase to a chiral nematic liquid crystalline phase
and vice versa. The theoretical analysis is based on a combination of dynamic
renormalization (DRG) and numerical simulation of the emergent model. While the
DRG architecture predicts a generic transition to the Kardar-Parisi-Zhang (KPZ)
universality class at dynamic equilibrium, in agreement with recent
experiments, the numerical simulations of the model show simultaneous existence
of two phases, one a "subdiffusive" (SD) phase characterized by a dynamical
exponent value of 1, and the other a KPZ phase, characterized by a dynamical
exponent value of 1.5. The SD phase flows over to the KPZ phase with increased
external forcing, offering a new universality paradigm, hitherto unexplored in
the context of ferroelectric liquid crystals.
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Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies | We present a feature functional theory - binding predictor (FFT-BP) for the
protein-ligand binding affinity prediction. The underpinning assumptions of
FFT-BP are as follows: i) representability: there exists a microscopic feature
vector that can uniquely characterize and distinguish one protein-ligand
complex from another; ii) feature-function relationship: the macroscopic
features, including binding free energy, of a complex is a functional of
microscopic feature vectors; and iii) similarity: molecules with similar
microscopic features have similar macroscopic features, such as binding
affinity. Physical models, such as implicit solvent models and quantum theory,
are utilized to extract microscopic features, while machine learning algorithms
are employed to rank the similarity among protein-ligand complexes. A large
variety of numerical validations and tests confirms the accuracy and robustness
of the proposed FFT-BP model. The root mean square errors (RMSEs) of FFT-BP
blind predictions of a benchmark set of 100 complexes, the PDBBind v2007 core
set of 195 complexes and the PDBBind v2015 core set of 195 complexes are 1.99,
2.02 and 1.92 kcal/mol, respectively. Their corresponding Pearson correlation
coefficients are 0.75, 0.80, and 0.78, respectively.
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Combining and Steganography of 3D Face Textures | One of the serious issues in communication between people is hiding
information from others, and the best way for this, is deceiving them. Since
nowadays face images are mostly used in three dimensional format, in this paper
we are going to steganography 3D face images, detecting which by curious people
will be impossible. As in detecting face only its texture is important, we
separate texture from shape matrices, for eliminating half of the extra
information, steganography is done only for face texture, and for
reconstructing 3D face, we can use any other shape. Moreover, we will indicate
that, by using two textures, how two 3D faces can be combined. For a complete
description of the process, first, 2D faces are used as an input for building
3D faces, and then 3D textures are hidden within other images.
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Degree of sequentiality of weighted automata | Weighted automata (WA) are an important formalism to describe quantitative
properties. Obtaining equivalent deterministic machines is a longstanding
research problem. In this paper we consider WA with a set semantics, meaning
that the semantics is given by the set of weights of accepting runs. We focus
on multi-sequential WA that are defined as finite unions of sequential WA. The
problem we address is to minimize the size of this union. We call this minimum
the degree of sequentiality of (the relation realized by) the WA. For a given
positive integer k, we provide multiple characterizations of relations realized
by a union of k sequential WA over an infinitary finitely generated group: a
Lipschitz-like machine independent property, a pattern on the automaton (a new
twinning property) and a subclass of cost register automata. When possible, we
effectively translate a WA into an equivalent union of k sequential WA. We also
provide a decision procedure for our twinning property for commutative
computable groups thus allowing to compute the degree of sequentiality. Last,
we show that these results also hold for word transducers and that the
associated decision problem is Pspace-complete.
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On the orders of the non-Frattini elements of a finite group | Let $G$ be a finite group and let $p_1,\dots,p_n$ be distinct primes. If $G$
contains an element of order $p_1\cdots p_n,$ then there is an element in $G$
which is not contained in the Frattini subgroup of $G$ and whose order is
divisible by $p_1\cdots p_n.$
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Varieties of general type with small volumes | Generalize Kobayashi's example for the Noether inequality in dimension three,
we provide examples of n-folds of general type with small volumes.
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Translations in the exponential Orlicz space with Gaussian weight | We study the continuity of space translations on non-parametric exponential
families based on the exponential Orlicz space with Gaussian reference density.
| 0 | 0 | 1 | 1 | 0 | 0 |
Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime | In this study, we propose shrinkage methods based on {\it generalized ridge
regression} (GRR) estimation which is suitable for both multicollinearity and
high dimensional problems with small number of samples (large $p$, small $n$).
Also, it is obtained theoretical properties of the proposed estimators for
Low/High Dimensional cases. Furthermore, the performance of the listed
estimators is demonstrated by both simulation studies and real-data analysis,
and compare its performance with existing penalty methods. We show that the
proposed methods compare well to competing regularization techniques.
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A path integral approach to Bayesian inference in Markov processes | We formulate Bayesian updates in Markov processes by means of path integral
techniques and derive the imaginary-time Schrödinger equation with
likelihood to direct the inference incorporated as a potential for the
posterior probability distribution
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Strong-coupling charge density wave in a one-dimensional topological metal | Scanning tunnelling microscopy and low energy electron diffraction show a
dimerization-like reconstruction in the one-dimensional atomic chains on
Bi(114) at low temperatures. While one-dimensional systems are generally
unstable against such a distortion, its observation is not expected for this
particular surface, since there are several factors that should prevent it: One
is the particular spin texture of the Fermi surface, which resembles a
one-dimensional topological state, and spin protection should hence prevent the
formation of the reconstruction. The second is the very short nesting vector $2
k_F$, which is inconsistent with the observed lattice distortion. A
nesting-driven mechanism of the reconstruction is indeed excluded by the
absence of any changes in the electronic structure near the Fermi surface, as
observed by angle-resolved photoemission spectroscopy. However, distinct
changes in the electronic structure at higher binding energies are found to
accompany the structural phase transition. This, as well as the observed short
correlation length of the pairing distortion, suggest that the transition is of
the strong coupling type and driven by phonon entropy rather than electronic
entropy.
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$\mathfrak A$-principal Hopf hypersurfaces in complex quadrics | A real hypersurface in the complex quadric $Q^m=SO_{m+2}/SO_mSO_2$ is said to
be $\mathfrak A$-principal if its unit normal vector field is singular of type
$\mathfrak A$-principal everywhere. In this paper, we show that a $\mathfrak
A$-principal Hopf hypersurface in $Q^m$, $m\geq3$ is an open part of a tube
around a totally geodesic $Q^{m+1}$ in $Q^m$. We also show that such real
hypersurfaces are the only contact real hypersurfaces in $Q^m$. %, this answers
affirmatively a question posted by Berndt (cf. \cite{berndt1})}. The
classification for pseudo-Einstein real hypersurfaces in $Q^m$, $m\geq3$, is
also obtained.
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On the stochastic phase stability of Ti2AlC-Cr2AlC | The quest towards expansion of the MAX design space has been accelerated with
the recent discovery of several solid solution and ordered phases involving at
least two MAX end members. Going beyond the nominal MAX compounds enables not
only fine tuning of existing properties but also entirely new functionality.
This search, however, has been mostly done through painstaking experiments as
knowledge of the phase stability of the relevant systems is rather scarce. In
this work, we report the first attempt to evaluate the finite-temperature
pseudo-binary phase diagram of the Ti2AlC-Cr2AlC via first-principles-guided
Bayesian CALPHAD framework that accounts for uncertainties not only in ab
initio calculations and thermodynamic models but also in synthesis conditions
in reported experiments. The phase stability analyses are shown to have good
agreement with previous experiments. The work points towards a promising way of
investigating phase stability in other MAX Phase systems providing the
knowledge necessary to elucidate possible synthesis routes for MAX systems with
unprecedented properties.
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Note on Attacking Object Detectors with Adversarial Stickers | Deep learning has proven to be a powerful tool for computer vision and has
seen widespread adoption for numerous tasks. However, deep learning algorithms
are known to be vulnerable to adversarial examples. These adversarial inputs
are created such that, when provided to a deep learning algorithm, they are
very likely to be mislabeled. This can be problematic when deep learning is
used to assist in safety critical decisions. Recent research has shown that
classifiers can be attacked by physical adversarial examples under various
physical conditions. Given the fact that state-of-the-art objection detection
algorithms are harder to be fooled by the same set of adversarial examples,
here we show that these detectors can also be attacked by physical adversarial
examples. In this note, we briefly show both static and dynamic test results.
We design an algorithm that produces physical adversarial inputs, which can
fool the YOLO object detector and can also attack Faster-RCNN with relatively
high success rate based on transferability. Furthermore, our algorithm can
compress the size of the adversarial inputs to stickers that, when attached to
the targeted object, result in the detector either mislabeling or not detecting
the object a high percentage of the time. This note provides a small set of
results. Our upcoming paper will contain a thorough evaluation on other object
detectors, and will present the algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
All or Nothing Caching Games with Bounded Queries | We determine the value of some search games where our goal is to find all of
some hidden treasures using queries of bounded size. The answer to a query is
either empty, in which case we lose, or a location, which contains a treasure.
We prove that if we need to find $d$ treasures at $n$ possible locations with
queries of size at most $k$, then our chance of winning is $\frac{k^d}{\binom
nd}$ if each treasure is at a different location and
$\frac{k^d}{\binom{n+d-1}d}$ if each location might hide several treasures for
large enough $n$. Our work builds on some results by Csóka who has studied a
continuous version of this problem, known as Alpern's Caching Game; we also
prove that the value of Alpern's Caching Game is $\frac{k^d}{\binom{n+d-1}d}$
for integer $k$ and large enough $n$.
| 1 | 0 | 1 | 0 | 0 | 0 |
HATS-43b, HATS-44b, HATS-45b, and HATS-46b: Four Short Period Transiting Giant Planets in the Neptune-Jupiter Mass Range | We report the discovery of four short period extrasolar planets transiting
moderately bright stars from photometric measurements of the HATSouth network
coupled to additional spectroscopic and photometric follow-up observations.
While the planet masses range from 0.26 to 0.90 M$_J$, the radii are all
approximately a Jupiter radii, resulting in a wide range of bulk densities. The
orbital period of the planets range from 2.7d to 4.7d, with HATS-43b having an
orbit that appears to be marginally non-circular (e= 0.173$\pm$0.089). HATS-44
is notable for a high metallicity ([Fe/H]= 0.320$\pm$0.071). The host stars
spectral types range from late F to early K, and all of them are moderately
bright (13.3<V<14.4), allowing the execution of future detailed follow-up
observations. HATS-43b and HATS-46b, with expected transmission signals of 2350
ppm and 1500 ppm, respectively, are particularly well suited targets for
atmospheric characterisation via transmission spectroscopy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Well-balanced mesh-based and meshless schemes for the shallow-water equations | We formulate a general criterion for the exact preservation of the "lake at
rest" solution in general mesh-based and meshless numerical schemes for the
strong form of the shallow-water equations with bottom topography. The main
idea is a careful mimetic design for the spatial derivative operators in the
momentum flux equation that is paired with a compatible averaging rule for the
water column height arising in the bottom topography source term. We prove
consistency of the mimetic difference operators analytically and demonstrate
the well-balanced property numerically using finite difference and RBF-FD
schemes in the one- and two-dimensional cases.
| 0 | 1 | 1 | 0 | 0 | 0 |
An Enhanced Initial Margin Methodology to Manage Warehoused Credit Risk | The use of CVA to cover credit risk is widely spread, but has its
limitations. Namely, dealers face the problem of the illiquidity of instruments
used for hedging it, hence forced to warehouse credit risk. As a result,
dealers tend to offer a limited OTC derivatives market to highly risky
counterparties. Consequently, those highly risky entities rarely have access to
hedging services precisely when they need them most. In this paper we propose a
method to overcome this limitation. We propose to extend the CVA risk-neutral
framework to compute an initial margin (IM) specific to each counterparty,
which depends on the credit quality of the entity at stake, transforming the
effective credit rating of a given netting set to AAA, regardless of the credit
rating of the counterparty. By transforming CVA requirement into IM ones, as
proposed in this paper, an institution could rely on the existing mechanisms
for posting and calling of IM, hence ensuring the operational viability of this
new form of managing warehoused risk. The main difference with the currently
standard framework is the creation of a Specific Initial Margin, that depends
in the credit rating of the counterparty and the characteristics of the netting
set in question. In this paper we propose a methodology for such transformation
in a sound manner, and hence this method overcomes some of the limitations of
the CVA framework.
| 0 | 0 | 0 | 0 | 0 | 1 |
Ensemble Pruning based on Objection Maximization with a General Distributed Framework | Ensemble pruning, selecting a subset of individual learners from an original
ensemble, alleviates the deficiencies of ensemble learning on the cost of time
and space. Accuracy and diversity serve as two crucial factors while they
usually conflict with each other. To balance both of them, we formalize the
ensemble pruning problem as an objection maximization problem based on
information entropy. Then we propose an ensemble pruning method including a
centralized version and a distributed version, in which the latter is to speed
up the former's execution. At last, we extract a general distributed framework
for ensemble pruning, which can be widely suitable for most of existing
ensemble pruning methods and achieve less time consuming without much accuracy
decline. Experimental results validate the efficiency of our framework and
methods, particularly with regard to a remarkable improvement of the execution
speed, accompanied by gratifying accuracy performance.
| 0 | 0 | 0 | 1 | 0 | 0 |
Psychophysical laws as reflection of mental space properties | The paper is devoted to the relationship between psychophysics and physics of
mind. The basic trends in psychophysics development are briefly discussed with
special attention focused on Teghtsoonian's hypotheses. These hypotheses pose
the concept of the universality of inner psychophysics and enable to speak
about psychological space as an individual object with its own properties.
Turning to the two-component description of human behavior (I. Lubashevsky,
Physics of the Human Mind, Springer, 2017) the notion of mental space is
formulated and human perception of external stimuli is treated as the emergence
of the corresponding images in the mental space. On one hand, these images are
caused by external stimuli and their magnitude bears the information about the
intensity of the corresponding stimuli. On the other hand, the individual
structure of such images as well as their subsistence after emergence is
determined only by the properties of mental space on its own. Finally, the
mental operations of image comparison and their scaling are defined in a way
allowing for the bounded capacity of human cognition. As demonstrated, the
developed theory of stimulus perception is able to explain the basic
regularities of psychophysics, e.g., (i) the regression and range effects
leading to the overestimation of weak stimuli and the underestimation of strong
stimuli, (ii) scalar variability (Weber's and Ekman' laws), and (\textit{iii})
the sequential (memory) effects. As the final result, a solution to the
Fechner-Stevens dilemma is proposed. This solution posits that Fechner's
logarithmic law is not a consequences of Weber's law but stems from the
interplay of uncertainty in evaluating stimulus intensities and the multi-step
scaling required to overcome the stimulus incommensurability.
| 0 | 0 | 0 | 0 | 1 | 0 |
Shimura curves in the Prym locus | We study Shimura curves of PEL type in $\mathsf{A}_g$ generically contained
in the Prym locus. We study both the unramified Prym locus, obtained using
étale double covers, and the ramified Prym locus, corresponding to double
covers ramified at two points. In both cases we consider the family of all
double covers compatible with a fixed group action on the base curve. We
restrict to the case where the family is 1-dimensional and the quotient of the
base curve by the group is $\mathbb{P}^1$. We give a simple criterion for the
image of these families under the Prym map to be a Shimura curve. Using
computer algebra we check all the examples gotten in this way up to genus 28.
We obtain 43 Shimura curves generically contained in the unramified Prym locus
and 9 families generically contained in the ramified Prym locus. Most of these
curves are not generically contained in the Jacobian locus.
| 0 | 0 | 1 | 0 | 0 | 0 |
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies | Data-driven anomaly detection methods suffer from the drawback of detecting
all instances that are statistically rare, irrespective of whether the detected
instances have real-world significance or not. In this paper, we are interested
in the problem of specifically detecting anomalous instances that are known to
have high real-world utility, while ignoring the low-utility statistically
anomalous instances. To this end, we propose a novel method called Latent
Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution.
This method uses the EM algorithm to simultaneously incorporate the Geometric
Entropy Minimization principle for identifying statistical anomalies, and the
Maximum Entropy Discrimination principle to incorporate utility labels, in
order to detect high-utility anomalies. We apply our method in both simulated
and real datasets to demonstrate that it has superior performance over existing
alternatives that independently pre-process with unsupervised anomaly detection
algorithms before classifying.
| 1 | 0 | 0 | 1 | 0 | 0 |
View-Invariant Recognition of Action Style Self-Dissimilarity | Self-similarity was recently introduced as a measure of inter-class
congruence for classification of actions. Herein, we investigate the dual
problem of intra-class dissimilarity for classification of action styles. We
introduce self-dissimilarity matrices that discriminate between same actions
performed by different subjects regardless of viewing direction and camera
parameters. We investigate two frameworks using these invariant style
dissimilarity measures based on Principal Component Analysis (PCA) and Fisher
Discriminant Analysis (FDA). Extensive experiments performed on IXMAS dataset
indicate remarkably good discriminant characteristics for the proposed
invariant measures for gender recognition from video data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Conducting Highly Principled Data Science: A Statistician's Job and Joy | Highly Principled Data Science insists on methodologies that are: (1)
scientifically justified, (2) statistically principled, and (3) computationally
efficient. An astrostatistics collaboration, together with some reminiscences,
illustrates the increased roles statisticians can and should play to ensure
this trio, and to advance the science of data along the way.
| 0 | 0 | 0 | 1 | 0 | 0 |
Regularizing nonlinear Schroedinger equations through partial off-axis variations | We study a class of focusing nonlinear Schroedinger-type equations derived
recently by Dumas, Lannes and Szeftel within the mathematical description of
high intensity laser beams [7]. These equations incorporate the possibility of
a (partial) off-axis variation of the group velocity of such laser beams
through a second order partial differential operator acting in some, but not
necessarily all, spatial directions. We study the well-posedness theory for
such models and obtain a regularizing effect, even in the case of only partial
off-axis dependence. This provides an answer to an open problem posed in [7].
| 0 | 0 | 1 | 0 | 0 | 0 |
On h-Lexicalized Restarting Automata | Following some previous studies on restarting automata, we introduce a
refined model - the h-lexicalized restarting automaton (h-RLWW). We argue that
this model is useful for expressing lexicalized syntax in computational
linguistics. We compare the input languages, which are the languages
traditionally considered in automata theory, to the so-called basic and
h-proper languages, which are (implicitly) used by categorial grammars, the
original tool for the description of lexicalized syntax. The basic and h-proper
languages allow us to stress several nice properties of h-lexicalized
restarting automata, and they are suitable for modeling the analysis by
reduction and, subsequently, for the development of categories of a lexicalized
syntax. Based on the fact that a two-way deterministic monotone restarting
automaton can be transformed into an equivalent deterministic monotone
RL-automaton in (Marcus) contextual form, we obtain a transformation from
monotone RLWW-automata that recognize the class CFL of context-free languages
as their input languages to deterministic monotone h-RLWW-automata that
recognize CFL through their h-proper languages. Through this transformation we
obtain automata with the complete correctness preserving property and an
infinite hierarchy within CFL, based on the size of the read/write window.
Additionally, we consider h-RLWW-automata that are allowed to perform multiple
rewrite steps per cycle, and we establish another infinite hierarchy above CFL
that is based on the number of rewrite steps that may be executed within a
cycle. The corresponding separation results and their proofs illustrate the
transparency of h-RLWW-automata that work with the (complete or cyclic)
correctness preserving property
| 1 | 0 | 0 | 0 | 0 | 0 |
Bayesian Nonparametric Inference for M/G/1 Queueing Systems | In this work, nonparametric statistical inference is provided for the
continuous-time M/G/1 queueing model from a Bayesian point of view. The
inference is based on observations of the inter-arrival and service times.
Beside other characteristics of the system, particular interest is in the
waiting time distribution which is not accessible in closed form. Thus, we use
an indirect statistical approach by exploiting the Pollaczek-Khinchine
transform formula for the Laplace transform of the waiting time distribution.
Due to this, an estimator is defined and its frequentist validation in terms of
posterior consistency and posterior normality is studied. It will turn out that
we can hereby make inference for the observables separately and compose the
results subsequently by suitable techniques.
| 0 | 0 | 1 | 1 | 0 | 0 |
Symbol Invariant of Partition and the Construction | The symbol is used to describe the Springer correspondence for the classical
groups. We propose equivalent definitions of symbols for rigid partitions in
the $B_n$, $C_n$, and $D_n$ theories uniformly. Analysing the new definition of
symbol in detail, we give rules to construct symbol of a partition, which are
easy to remember and to operate on. We introduce formal operations of a
partition, which reduce the difficulties in the proof of the construction
rules. According these rules, we give a closed formula of symbols for different
theories uniformly. As applications, previous results can be illustrated more
clearly by the construction rules of symbol.
| 0 | 0 | 1 | 0 | 0 | 0 |
Fairness in representation: quantifying stereotyping as a representational harm | While harms of allocation have been increasingly studied as part of the
subfield of algorithmic fairness, harms of representation have received
considerably less attention. In this paper, we formalize two notions of
stereotyping and show how they manifest in later allocative harms within the
machine learning pipeline. We also propose mitigation strategies and
demonstrate their effectiveness on synthetic datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Probing the possibility of hotspots on the central neutron star in HESS J1731-347 | The X-ray spectra of the neutron stars located in the centers of supernova
remnants Cas A and HESS J1731-347 are well fit with carbon atmosphere models.
These fits yield plausible neutron star sizes for the known or estimated
distances to these supernova remnants. The evidence in favor of the presence of
a pure carbon envelope at the neutron star surface is rather indirect and is
based on the assumption that the emission is generated uniformly by the entire
stellar surface. Although this assumption is supported by the absence of
pulsations, the observational upper limit on the pulsed fraction is not very
stringent. In an attempt to quantify this evidence, we investigate the
possibility that the observed spectrum of the neutron star in HESS J1731-347 is
a combination of the spectra produced in a hydrogen atmosphere of the hotspots
and of the cooler remaining part of the neutron star surface. The lack of
pulsations in this case has to be explained either by a sufficiently small
angle between the neutron star spin axis and the line of sight, or by a
sufficiently small angular distance between the hotspots and the neutron star
rotation poles. As the observed flux from a non-uniformly emitting neutron star
depends on the angular distribution of the radiation emerging from the
atmosphere, we have computed two new grids of pure carbon and pure hydrogen
atmosphere model spectra accounting for Compton scattering. Using new hydrogen
models, we have evaluated the probability of a geometry that leads to a pulsed
fraction below the observed upper limit to be about 8.2 %. Such a geometry thus
seems to be rather improbable but cannot be excluded at this stage.
| 0 | 1 | 0 | 0 | 0 | 0 |
Some Elementary Partition Inequalities and Their Implications | We prove various inequalities between the number of partitions with the bound
on the largest part and some restrictions on occurrences of parts. We explore
many interesting consequences of these partition inequalities. In particular,
we show that for $L\geq 1$, the number of partitions with $l-s \leq L$ and
$s=1$ is greater than the number of partitions with $l-s\leq L$ and $s>1$. Here
$l$ and $s$ are the largest part and the smallest part of the partition,
respectively.
| 0 | 0 | 1 | 0 | 0 | 0 |
The terrestrial late veneer from core disruption of a lunar-sized impactor | Overabundances in highly siderophile elements (HSEs) of Earth's mantle can be
explained by conveyance from a singular, immense (3000 km in a diameter) "Late
Veneer" impactor of chondritic composition, subsequent to lunar formation and
terrestrial core-closure. Such rocky objects of approximately lunar mass (about
0.01 M_E) ought to be differentiated, such that nearly all of their HSE payload
is sequestered into iron cores. Here, we analyze the mechanical and chemical
fate of the core of such a Late Veneer impactor, and trace how its HSEs are
suspended - and thus pollute - the mantle. For the statistically most-likely
oblique collision (about 45degree), the impactor's core elongates and
thereafter disintegrates into a metallic hail of small particles (about 10 m).
Some strike the orbiting Moon as sesquinary impactors, but most re-accrete to
Earth as secondaries with further fragmentation. We show that a single oblique
impactor provides an adequate amount of HSEs to the primordial terrestrial
silicate reservoirs via oxidation of (<m-sized) metal particles with a hydrous,
pre-impact, early Hadean Earth.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image | We consider a Bayesian model for inversion of observed amplitude variation
with offset (AVO) data into lithology/fluid classes, and study in particular
how the choice of prior distribution for the lithology/fluid classes influences
the inversion results. Two distinct prior distributions are considered, a
simple manually specified Markov random field prior with a first order
neighborhood and a Markov mesh model with a much larger neighborhood estimated
from a training image. They are chosen to model both horisontal connectivity
and vertical thickness distribution of the lithology/fluid classes, and are
compared on an offshore clastic oil reservoir in the North Sea. We combine both
priors with the same linearised Gaussian likelihood function based on a
convolved linearised Zoeppritz relation and estimate properties of the
resulting two posterior distributions by simulating from these distributions
with the Metropolis-Hastings algorithm.
The influence of the prior on the marginal posterior probabilities for the
lithology/fluid classes is clearly observable, but modest. The importance of
the prior on the connectivity properties in the posterior realisations,
however, is much stronger. The larger neighborhood of the Markov mesh prior
enables it to identify and model connectivity and curvature much better than
what can be done by the first order neighborhood Markov random field prior. As
a result, we conclude that the posterior realisations based on the Markov mesh
prior appear with much higher lateral connectivity, which is geologically
plausible.
| 0 | 0 | 0 | 1 | 0 | 0 |
Correct Brillouin zone and electronic structure of BiPd | A promising route to the realization of Majorana fermions is in
non-centrosymmetric superconductors, in which spin-orbit-coupling lifts the
spin degeneracy of both bulk and surface bands. A detailed assessment of the
electronic structure is critical to evaluate their suitability for this through
establishing the topological properties of the electronic structure. This
requires correct identification of the time-reversal-invariant momenta. One
such material is BiPd, a recently rediscovered non-centrosymmetric
superconductor which can be grown in large, high-quality single crystals and
has been studied by several groups using angular resolved photoemission to
establish its surface electronic structure. Many of the published electronic
structure studies on this material are based on a reciprocal unit cell which is
not the actual Brillouin zone of the material. We show here the consequences of
this for the electronic structures and show how the inferred topological nature
of the material is affected.
| 0 | 1 | 0 | 0 | 0 | 0 |
Even faster sorting of (not only) integers | In this paper we introduce RADULS2, the fastest parallel sorter based on
radix algorithm. It is optimized to process huge amounts of data making use of
modern multicore CPUs. The main novelties include: extremely optimized
algorithm for handling tiny arrays (up to about a hundred of records) that
could appear even billions times as subproblems to handle and improved
processing of larger subarrays with better use of non-temporal memory stores.
| 1 | 0 | 0 | 0 | 0 | 0 |
Evidential Deep Learning to Quantify Classification Uncertainty | Deterministic neural nets have been shown to learn effective predictors on a
wide range of machine learning problems. However, as the standard approach is
to train the network to minimize a prediction loss, the resultant model remains
ignorant to its prediction confidence. Orthogonally to Bayesian neural nets
that indirectly infer prediction uncertainty through weight uncertainties, we
propose explicit modeling of the same using the theory of subjective logic. By
placing a Dirichlet distribution on the class probabilities, we treat
predictions of a neural net as subjective opinions and learn the function that
collects the evidence leading to these opinions by a deterministic neural net
from data. The resultant predictor for a multi-class classification problem is
another Dirichlet distribution whose parameters are set by the continuous
output of a neural net. We provide a preliminary analysis on how the
peculiarities of our new loss function drive improved uncertainty estimation.
We observe that our method achieves unprecedented success on detection of
out-of-distribution queries and endurance against adversarial perturbations.
| 0 | 0 | 0 | 1 | 0 | 0 |
Neutron interference in the Earth's gravitational field | This work relates to the famous experiments, performed in 1975 and 1979 by
Werner et al., measuring neutron interference and neutron Sagnac effects in the
earth's gravitational field. Employing the method of Stodolsky in its weak
field approximation, explicit expressions are derived for the two phase shifts,
which turn out to be in agreement with the experiments and with the previously
obtained expressions derived from semi-classical arguments: these expressions
are simply modified by relativistic correction factors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Solve For Shortest Paths Problem Within Logarithm Runtime | The Shortest Paths Problem (SPP) is no longer unresolved. Just for a large
scalar of instance on this problem, even we cannot know if an algorithm
achieves the computing. Those cutting-edge methods are still in the low
performance. If we go to a strategy the best-first-search to deal with
computing, it is awkward that the technical barrier from another field: the
database, which with the capable of Online Oriented. In this paper, we will
introduce such a synthesis to solve for SPP which comprises various modules
therein including such database leads to finish the task in a logarithm
runtime.
Through experiments taken on three typical instances on mega-scalar data for
transaction in a common laptop, we show off a totally robust, tractable and
practical applicability for other projects.
| 1 | 0 | 0 | 0 | 0 | 0 |
A short proof of the error term in Simpson's rule | In this paper we present a short and elementary proof for the error in
Simpson's rule.
| 0 | 0 | 1 | 0 | 0 | 0 |
Neutrino mass and dark energy constraints from redshift-space distortions | Cosmology in the near future promises a measurement of the sum of neutrino
masses, a fundamental Standard Model parameter, as well as
substantially-improved constraints on the dark energy. We use the shape of the
BOSS redshift-space galaxy power spectrum, in combination with CMB and
supernova data, to constrain the neutrino masses and the dark energy. Essential
to this calculation are several recent advances in non-linear cosmological
perturbation theory, including FFT methods, redshift space distortions, and
scale-dependent growth. Our 95% confidence upper bound of 200 meV on the sum of
masses degrades substantially to 770 meV when the dark energy equation of state
and its first derivative are also allowed to vary, representing a significant
challenge to current constraints. We also study the impact of additional galaxy
bias parameters, finding that a velocity bias or a more complicated
scale-dependent density bias shift the preferred neutrino mass values 20%-30%
lower while minimally impacting the other cosmological parameters.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dielectrophoretic assembly of liquid-phase-exfoliated TiS3 nanoribbons for photodetecting applications | Liquid-phase-exfoliation is a technique capable of producing large quantities
of two-dimensional material in suspension. Despite many efforts in the
optimization of the exfoliation process itself not much has been done towards
the integration of liquid-phase-exfoliated materials in working solid-state
devices. In this article, we use dielectrophoresis to direct the assembly of
liquid-phase-exfoliated TiS3 nanoribbons between two gold electrodes to produce
photodetectors working in the visible. Through electrical and optical
measurements we characterize the responsivity of the device and we find values
as large as 3.8 mA/W, which improve of more than one order of magnitude on the
state-of-the-art for devices based on liquid-phase-exfoliated two-dimensional
materials assembled by drop-casting or ink-jet methods.
| 0 | 1 | 0 | 0 | 0 | 0 |
Motion optimization and parameter identification for a human and lower-back exoskeleton model | Designing an exoskeleton to reduce the risk of low-back injury during lifting
is challenging. Computational models of the human-robot system coupled with
predictive movement simulations can help to simplify this design process. Here,
we present a study that models the interaction between a human model actuated
by muscles and a lower-back exoskeleton. We provide a computational framework
for identifying the spring parameters of the exoskeleton using an optimal
control approach and forward-dynamics simulations. This is applied to generate
dynamically consistent bending and lifting movements in the sagittal plane. Our
computations are able to predict motions and forces of the human and
exoskeleton that are within the torque limits of a subject. The identified
exoskeleton could also yield a considerable reduction of the peak lower-back
torques as well as the cumulative lower-back load during the movements. This
work is relevant to the research communities working on human-robot
interaction, and can be used as a basis for a better human-centered design
process.
| 1 | 0 | 0 | 0 | 0 | 0 |
A dataset for Computer-Aided Detection of Pulmonary Embolism in CTA images | Todays, researchers in the field of Pulmonary Embolism (PE) analysis need to
use a publicly available dataset to assess and compare their methods. Different
systems have been designed for the detection of pulmonary embolism (PE), but
none of them have used any public datasets. All papers have used their own
private dataset. In order to fill this gap, we have collected 5160 slices of
computed tomography angiography (CTA) images acquired from 20 patients, and
after labeling the image by experts in this field, we provided a reliable
dataset which is now publicly available. In some situation, PE detection can be
difficult, for example when it occurs in the peripheral branches or when
patients have pulmonary diseases (such as parenchymal disease). Therefore, the
efficiency of CAD systems highly depends on the dataset. In the given dataset,
66% of PE are located in peripheral branches, and different pulmonary diseases
are also included.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comment on Ben-Amotz and Honig, "Average entropy dissipation in irreversible mesoscopic processes," Phys. Rev. Lett. 96, 020602 (2006) | We point out that most of the classical thermodynamics results in the paper
have been known in the literature, see Kestin and Woods, for quite some time
and are not new, contrary to what the authors imply. As shown by Kestin, these
results are valid for quasistatic irreversible processes only and not for
arbitrary irreversible processes as suggested in the paper. Thus, the
application to the Jarzynski process is limited.
| 0 | 1 | 0 | 0 | 0 | 0 |
High-transmissivity Silicon Visible-wavelength Metasurface Designs based on Truncated-cone Nanoantennae | High-transmissivity all-dielectric metasurfaces have recently attracted
attention towards the realization of ultra-compact optical devices and systems.
Silicon based metasurfaces, in particular, are highly promising considering the
possibility of monolithic integration with VLSI circuits. Realization of
silicon based metasurfaces operational in the visible wavelengths remains a
challenge. A numerical study of silicon metasurfaces based on stepped truncated
cone shaped nanoantenna elements is presented. Metasurfaces based on the
stepped conical geometry can be designed for operation in the 700nm to 800nm
wavelength window and achieve full cycle phase response (0 to pi with an
improved transmittance in comparison with previously reported cylindrical
geometry [1]. A systematic parameter study of the influence of various
geometrical parameters on the achievable amplitude and phase coverage is
reported.
| 0 | 1 | 0 | 0 | 0 | 0 |
Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data | We proposed a probabilistic approach to joint modeling of participants'
reliability and humans' regularity in crowdsourced affective studies.
Reliability measures how likely a subject will respond to a question seriously;
and regularity measures how often a human will agree with other
seriously-entered responses coming from a targeted population.
Crowdsourcing-based studies or experiments, which rely on human self-reported
affect, pose additional challenges as compared with typical crowdsourcing
studies that attempt to acquire concrete non-affective labels of objects. The
reliability of participants has been massively pursued for typical
non-affective crowdsourcing studies, whereas the regularity of humans in an
affective experiment in its own right has not been thoroughly considered. It
has been often observed that different individuals exhibit different feelings
on the same test question, which does not have a sole correct response in the
first place. High reliability of responses from one individual thus cannot
conclusively result in high consensus across individuals. Instead, globally
testing consensus of a population is of interest to investigators. Built upon
the agreement multigraph among tasks and workers, our probabilistic model
differentiates subject regularity from population reliability. We demonstrate
the method's effectiveness for in-depth robust analysis of large-scale
crowdsourced affective data, including emotion and aesthetic assessments
collected by presenting visual stimuli to human subjects.
| 1 | 0 | 0 | 1 | 0 | 0 |
Quantum key distribution protocol with pseudorandom bases | Quantum key distribution (QKD) offers a way for establishing
information-theoretically secure communications. An important part of QKD
technology is a high-quality random number generator (RNG) for quantum states
preparation and for post-processing procedures. In the present work, we
consider a novel class of prepare-and-measure QKD protocols, utilizing
additional pseudorandomness in the preparation of quantum states. We study one
of such protocols and analyze its security against the intercept-resend attack.
We demonstrate that, for single-photon sources, the considered protocol gives
better secret key rates than the BB84 and the asymmetric BB84 protocol.
However, the protocol strongly requires single-photon sources.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep clustering of longitudinal data | Deep neural networks are a family of computational models that have led to a
dramatical improvement of the state of the art in several domains such as
image, voice or text analysis. These methods provide a framework to model
complex, non-linear interactions in large datasets, and are naturally suited to
the analysis of hierarchical data such as, for instance, longitudinal data with
the use of recurrent neural networks. In the other hand, cohort studies have
become a tool of importance in the research field of epidemiology. In such
studies, variables are measured repeatedly over time, to allow the practitioner
to study their temporal evolution as trajectories, and, as such, as
longitudinal data. This paper investigates the application of the advanced
modelling techniques provided by the deep learning framework in the analysis of
the longitudinal data provided by cohort studies. Methods: A method for
visualizing and clustering longitudinal dataset is proposed, and compared to
other widely used approaches to the problem on both real and simulated
datasets. Results: The proposed method is shown to be coherent with the
preexisting procedures on simple tasks, and to outperform them on more complex
tasks such as the partitioning of longitudinal datasets into non-spherical
clusters. Conclusion: Deep artificial neural networks can be used to visualize
longitudinal data in a low dimensional manifold that is much simpler to
interpret than traditional longitudinal plots are. Consequently, practitioners
should start considering the use of deep artificial neural networks for the
analysis of their longitudinal data in studies to come.
| 0 | 0 | 0 | 1 | 0 | 0 |
Economic Implications of Blockchain Platforms | In an economy with asymmetric information, the smart contract in the
blockchain protocol mitigates uncertainty. Since, as a new trading platform,
the blockchain triggers segmentation of market and differentiation of agents in
both the sell and buy sides of the market, it recomposes the asymmetric
information and generates spreads in asset price and quality between itself and
a traditional platform. We show that marginal innovation and sophistication of
the smart contract have non-monotonic effects on the trading value in the
blockchain platform, its fundamental value, the price of cryptocurrency, and
consumers' welfare. Moreover, a blockchain manager who controls the level of
the innovation of the smart contract has an incentive to keep it lower than the
first best when the underlying information asymmetry is not severe, leading to
welfare loss for consumers.
| 0 | 0 | 0 | 0 | 0 | 1 |
Proof Theory and Ordered Groups | Ordering theorems, characterizing when partial orders of a group extend to
total orders, are used to generate hypersequent calculi for varieties of
lattice-ordered groups (l-groups). These calculi are then used to provide new
proofs of theorems arising in the theory of ordered groups. More precisely: an
analytic calculus for abelian l-groups is generated using an ordering theorem
for abelian groups; a calculus is generated for l-groups and new decidability
proofs are obtained for the equational theory of this variety and extending
finite subsets of free groups to right orders; and a calculus for representable
l-groups is generated and a new proof is obtained that free groups are
orderable.
| 0 | 0 | 1 | 0 | 0 | 0 |
DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features | As the foundation of driverless vehicle and intelligent robots, Simultaneous
Localization and Mapping(SLAM) has attracted much attention these days.
However, non-geometric modules of traditional SLAM algorithms are limited by
data association tasks and have become a bottleneck preventing the development
of SLAM. To deal with such problems, many researchers seek to Deep Learning for
help. But most of these studies are limited to virtual datasets or specific
environments, and even sacrifice efficiency for accuracy. Thus, they are not
practical enough.
We propose DF-SLAM system that uses deep local feature descriptors obtained
by the neural network as a substitute for traditional hand-made features.
Experimental results demonstrate its improvements in efficiency and stability.
DF-SLAM outperforms popular traditional SLAM systems in various scenes,
including challenging scenes with intense illumination changes. Its versatility
and mobility fit well into the need for exploring new environments. Since we
adopt a shallow network to extract local descriptors and remain others the same
as original SLAM systems, our DF-SLAM can still run in real-time on GPU.
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A-NICE-MC: Adversarial Training for MCMC | Existing Markov Chain Monte Carlo (MCMC) methods are either based on
general-purpose and domain-agnostic schemes which can lead to slow convergence,
or hand-crafting of problem-specific proposals by an expert. We propose
A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to
produce samples with desired properties. First, we propose an efficient
likelihood-free adversarial training method to train a Markov chain and mimic a
given data distribution. Then, we leverage flexible volume preserving flows to
obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to
train efficient Markov chains to sample from a prescribed posterior
distribution by iteratively improving the quality of both the model and the
samples. A-NICE-MC provides the first framework to automatically design
efficient domain-specific MCMC proposals. Empirical results demonstrate that
A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of
deep neural networks, and is able to significantly outperform competing methods
such as Hamiltonian Monte Carlo.
| 1 | 0 | 0 | 1 | 0 | 0 |
Starobinsky-like Inflation, Supercosmology and Neutrino Masses in No-Scale Flipped SU(5) | We embed a flipped ${\rm SU}(5) \times {\rm U}(1)$ GUT model in a no-scale
supergravity framework, and discuss its predictions for cosmic microwave
background observables, which are similar to those of the Starobinsky model of
inflation. Measurements of the tilt in the spectrum of scalar perturbations in
the cosmic microwave background, $n_s$, constrain significantly the model
parameters. We also discuss the model's predictions for neutrino masses, and
pay particular attention to the behaviours of scalar fields during and after
inflation, reheating and the GUT phase transition. We argue in favor of strong
reheating in order to avoid excessive entropy production which could dilute the
generated baryon asymmetry.
| 0 | 1 | 0 | 0 | 0 | 0 |
'Viral' Turing Machines, Computation from Noise and Combinatorial Hierarchies | The interactive computation paradigm is reviewed and a particular example is
extended to form the stochastic analog of a computational process via a
transcription of a minimal Turing Machine into an equivalent asynchronous
Cellular Automaton with an exponential waiting times distribution of effective
transitions. Furthermore, a special toolbox for analytic derivation of
recursive relations of important statistical and other quantities is introduced
in the form of an Inductive Combinatorial Hierarchy.
| 1 | 1 | 0 | 0 | 0 | 0 |
Estimating model evidence using ensemble-based data assimilation with localization - The model selection problem | IIn recent years, there has been a growing interest in applying data
assimilation (DA) methods, originally designed for state estimation, to the
model selection problem. In this setting, Carrassi et al. (2017) introduced the
contextual formulation of model evidence (CME) and showed that CME can be
efficiently computed using a hierarchy of ensemble-based DA procedures.
Although Carrassi et al. (2017) analyzed the DA methods most commonly used for
operational atmospheric and oceanic prediction worldwide, they did not study
these methods in conjunction with localization to a specific domain. Yet any
application of ensemble DA methods to realistic geophysical models requires the
implementation of some form of localization. The present study extends the
theory for estimating CME to ensemble DA methods with domain localization. The
domain-localized CME (DL-CME) developed herein is tested for model selection
with two models: (i) the Lorenz 40-variable mid-latitude atmospheric dynamics
model (L95); and (ii) the simplified global atmospheric SPEEDY model. The CME
is compared to the root-mean-square-error (RMSE) as a metric for model
selection. The experiments show that CME improves systematically over the RMSE,
and that this skill improvement is further enhanced by applying localization in
the estimate of the CME, using the DL-CME. The potential use and range of
applications of the CME and DL-CME as a model selection metric are also
discussed.
| 0 | 0 | 0 | 1 | 0 | 0 |
An efficient spectral-Galerkin approximation and error analysis for Maxwell transmission eigenvalue problems in spherical geometries | We propose and analyze an efficient spectral-Galerkin approximation for the
Maxwell transmission eigenvalue problem in spherical geometry. Using a vector
spherical harmonic expansion, we reduce the problem to a sequence of equivalent
one-dimensional TE and TM modes that can be solved individually in parallel.
For the TE mode, we derive associated generalized eigenvalue problems and
corresponding pole conditions. Then we introduce weighted Sobolev spaces based
on the pole condition and prove error estimates for the generalized eigenvalue
problem. The TM mode is a coupled system with four unknown functions, which is
challenging for numerical calculation. To handle it, we design an effective
algorithm using Legendre-type vector basis functions. Finally, we provide some
numerical experiments to validate our theoretical results and demonstrate the
efficiency of the algorithms.
| 0 | 0 | 1 | 0 | 0 | 0 |
Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables | Mendelian randomization uses genetic variants to make causal inferences about
the effect of a risk factor on an outcome. With fine-mapped genetic data, there
may be hundreds of genetic variants in a single gene region any of which could
be used to assess this causal relationship. However, using too many genetic
variants in the analysis can lead to spurious estimates and inflated Type 1
error rates. But if only a few genetic variants are used, then the majority of
the data is ignored and estimates are highly sensitive to the particular choice
of variants. We propose an approach based on summarized data only (genetic
association and correlation estimates) that uses principal components analysis
to form instruments. This approach has desirable theoretical properties: it
takes the totality of data into account and does not suffer from numerical
instabilities. It also has good properties in simulation studies: it is not
particularly sensitive to varying the genetic variants included in the analysis
or the genetic correlation matrix, and it does not have greatly inflated Type 1
error rates. Overall, the method gives estimates that are not so precise as
those from variable selection approaches (such as using a conditional analysis
or pruning approach to select variants), but are more robust to seemingly
arbitrary choices in the variable selection step. Methods are illustrated by an
example using genetic associations with testosterone for 320 genetic variants
to assess the effect of sex hormone-related pathways on coronary artery disease
risk, in which variable selection approaches give inconsistent inferences.
| 0 | 0 | 0 | 1 | 0 | 0 |
Building competitive direct acoustics-to-word models for English conversational speech recognition | Direct acoustics-to-word (A2W) models in the end-to-end paradigm have
received increasing attention compared to conventional sub-word based automatic
speech recognition models using phones, characters, or context-dependent hidden
Markov model states. This is because A2W models recognize words from speech
without any decoder, pronunciation lexicon, or externally-trained language
model, making training and decoding with such models simple. Prior work has
shown that A2W models require orders of magnitude more training data in order
to perform comparably to conventional models. Our work also showed this
accuracy gap when using the English Switchboard-Fisher data set. This paper
describes a recipe to train an A2W model that closes this gap and is at-par
with state-of-the-art sub-word based models. We achieve a word error rate of
8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder
or language model. We find that model initialization, training data order, and
regularization have the most impact on the A2W model performance. Next, we
present a joint word-character A2W model that learns to first spell the word
and then recognize it. This model provides a rich output to the user instead of
simple word hypotheses, making it especially useful in the case of words unseen
or rarely-seen during training.
| 1 | 0 | 0 | 1 | 0 | 0 |
Convolutional Dictionary Learning: Acceleration and Convergence | Convolutional dictionary learning (CDL or sparsifying CDL) has many
applications in image processing and computer vision. There has been growing
interest in developing efficient algorithms for CDL, mostly relying on the
augmented Lagrangian (AL) method or the variant alternating direction method of
multipliers (ADMM). When their parameters are properly tuned, AL methods have
shown fast convergence in CDL. However, the parameter tuning process is not
trivial due to its data dependence and, in practice, the convergence of AL
methods depends on the AL parameters for nonconvex CDL problems. To moderate
these problems, this paper proposes a new practically feasible and convergent
Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The
BPG-M-based CDL is investigated with different block updating schemes and
majorization matrix designs, and further accelerated by incorporating some
momentum coefficient formulas and restarting techniques. All of the methods
investigated incorporate a boundary artifacts removal (or, more generally,
sampling) operator in the learning model. Numerical experiments show that,
without needing any parameter tuning process, the proposed BPG-M approach
converges more stably to desirable solutions of lower objective values than the
existing state-of-the-art ADMM algorithm and its memory-efficient variant do.
Compared to the ADMM approaches, the BPG-M method using a multi-block updating
scheme is particularly useful in single-threaded CDL algorithm handling large
datasets, due to its lower memory requirement and no polynomial computational
complexity. Image denoising experiments show that, for relatively strong
additive white Gaussian noise, the filters learned by BPG-M-based CDL
outperform those trained by the ADMM approach.
| 1 | 0 | 1 | 0 | 0 | 0 |
The late-time light curve of the type Ia supernova SN 2011fe | We present late-time optical $R$-band imaging data from the Palomar Transient
Factory (PTF) for the nearby type Ia supernova SN 2011fe. The stacked PTF light
curve provides densely sampled coverage down to $R\simeq22$ mag over 200 to 620
days past explosion. Combining with literature data, we estimate the
pseudo-bolometric light curve for this event from 200 to 1600 days after
explosion, and constrain the likely near-infrared contribution. This light
curve shows a smooth decline consistent with radioactive decay, except over
~450 to ~600 days where the light curve appears to decrease faster than
expected based on the radioactive isotopes presumed to be present, before
flattening at around 600 days. We model the 200-1600d pseudo-bolometric light
curve with the luminosity generated by the radioactive decay chains of
$^{56}$Ni, $^{57}$Ni and $^{55}$Co, and find it is not consistent with models
that have full positron trapping and no infrared catastrophe (IRC); some
additional energy escape other than optical/near-IR photons is required.
However, the light curve is consistent with models that allow for positron
escape (reaching 75% by day 500) and/or an IRC (with 85% of the flux emerging
in non-optical wavelengths by day 600). The presence of the $^{57}$Ni decay
chain is robustly detected, but the $^{55}$Co decay chain is not formally
required, with an upper mass limit estimated at 0.014 M$_{\odot}$. The
measurement of the $^{57}$Ni/$^{56}$Ni mass ratio is subject to significant
systematic uncertainties, but all of our fits require a high ratio >0.031 (>1.3
in solar abundances).
| 0 | 1 | 0 | 0 | 0 | 0 |
Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors | In this article, we consider Markov chain Monte Carlo(MCMC) algorithms for
exploring the intractable posterior density associated with Bayesian probit
linear mixed models under improper priors on the regression coefficients and
variance components. In particular, we construct the two-block Gibbs sampler
using the data augmentation (DA) techniques. Furthermore, we prove geometric
ergodicity of the Gibbs sampler, which is the foundation for building central
limit theorems for MCMC based estimators and subsequent inferences. The
conditions for geometric convergence are similar to those guaranteeing
posterior propriety. We also provide conditions for posterior propriety when
the design matrices take commonly observed forms. In general, the Haar
parameter expansion for DA (PX- DA) algorithm is an improvement of the DA
algorithm and it has been shown that it is theoretically at least as good as
the DA algorithm. Here we construct a Haar PX-DA algorithm, which has
essentially the same computational cost as the two-block Gibbs sampler.
| 0 | 0 | 1 | 1 | 0 | 0 |
The complexity of the Multiple Pattern Matching Problem for random strings | We generalise a multiple string pattern matching algorithm, recently proposed
by Fredriksson and Grabowski [J. Discr. Alg. 7, 2009], to deal with arbitrary
dictionaries on an alphabet of size $s$. If $r_m$ is the number of words of
length $m$ in the dictionary, and $\phi(r) = \max_m \ln(s\, m\, r_m)/m$, the
complexity rate for the string characters to be read by this algorithm is at
most $\kappa_{{}_\textrm{UB}}\, \phi(r)$ for some constant
$\kappa_{{}_\textrm{UB}}$. On the other side, we generalise the classical lower
bound of Yao [SIAM J. Comput. 8, 1979], for the problem with a single pattern,
to deal with arbitrary dictionaries, and determine it to be at least
$\kappa_{{}_\textrm{LB}}\, \phi(r)$. This proves the optimality of the
algorithm, improving and correcting previous claims.
| 1 | 0 | 0 | 0 | 0 | 0 |
Functoriality and uniformity in Hrushovski's groupoid-cover correspondence | The correspondence between definable connected groupoids in a theory $T$ and
internal generalised imaginary sorts of $T$, established by Hrushovski in
["Groupoids, imaginaries and internal covers," Turkish Journal of Mathematics,
2012], is here extended in two ways: First, it is shown that the correspondence
is in fact an equivalence of categories, with respect to appropriate notions of
morphism. Secondly, the equivalence of categories is shown to vary uniformly in
definable families, with respect to an appropriate relativisation of these
categories. Some elaboration on Hrushovki's original constructions are also
included.
| 0 | 0 | 1 | 0 | 0 | 0 |
Simple Conditions for Metastability of Continuous Markov Chains | A family $\{Q_{\beta}\}_{\beta \geq 0}$ of Markov chains is said to exhibit
$\textit{metastable mixing}$ with $\textit{modes}$
$S_{\beta}^{(1)},\ldots,S_{\beta}^{(k)}$ if its spectral gap (or some other
mixing property) is very close to the worst conductance
$\min(\Phi_{\beta}(S_{\beta}^{(1)}), \ldots, \Phi_{\beta}(S_{\beta}^{(k)}))$ of
its modes. We give simple sufficient conditions for a family of Markov chains
to exhibit metastability in this sense, and verify that these conditions hold
for a prototypical Metropolis-Hastings chain targeting a mixture distribution.
Our work differs from existing work on metastability in that, for the class of
examples we are interested in, it gives an asymptotically exact formula for the
spectral gap (rather than a bound that can be very far from sharp) while at the
same time giving technical conditions that are easier to verify for many
statistical examples. Our bounds from this paper are used in a companion paper
to compare the mixing times of the Hamiltonian Monte Carlo algorithm and a
random walk algorithm for multimodal target distributions.
| 0 | 0 | 0 | 1 | 0 | 0 |
Deterministic preparation of highly non-classical macroscopic quantum states | We present a scheme to deterministically prepare non-classical quantum states
of a massive mirror including highly non-Gaussian states exhibiting sizeable
negativity of the Wigner function. This is achieved by exploiting the
non-linear light-matter interaction in an optomechanical cavity by driving the
system with optimally designed frequency patterns. Our scheme reveals to be
resilient against mechanical and optical damping, as well as mechanical thermal
noise and imperfections in the driving scheme. Our proposal thus opens a
promising route for table-top experiments to explore and exploit macroscopic
quantum phenomena.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Local Optimizers of Acquisition Functions in Bayesian Optimization | Bayesian optimization is a sample-efficient method for finding a global
optimum of an expensive-to-evaluate black-box function. A global solution is
found by accumulating a pair of query point and corresponding function value,
repeating these two procedures: (i) learning a surrogate model for the
objective function using the data observed so far; (ii) the maximization of an
acquisition function to determine where next to query the objective function.
Convergence guarantees are only valid when the global optimizer of the
acquisition function is found and selected as the next query point. In
practice, however, local optimizers of acquisition functions are also used,
since searching the exact optimizer of the acquisition function is often a
non-trivial or time-consuming task. In this paper we present an analysis on the
behavior of local optimizers of acquisition functions, in terms of
instantaneous regrets over global optimizers. We also present the performance
analysis when multi-started local optimizers are used to find the maximum of
the acquisition function. Numerical experiments confirm the validity of our
theoretical analysis.
| 1 | 0 | 0 | 1 | 0 | 0 |
One Password: An Encryption Scheme for Hiding Users' Register Information | In recent years, the attack which leverages register information (e.g.
accounts and passwords) leaked from 3rd party applications to try other
applications is popular and serious. We call this attack "database collision".
Traditionally, people have to keep dozens of accounts and passwords for
different applications to prevent this attack. In this paper, we propose a
novel encryption scheme for hiding users' register information and preventing
this attack. Specifically, we first hash the register information using
existing safe hash function. Then the hash string is hidden, instead a
coefficient vector is stored for verification. Coefficient vectors of the same
register information are generated randomly for different applications. Hence,
the original information is hardly cracked by dictionary based attack or
database collision in practice. Using our encryption scheme, each user only
needs to keep one password for dozens of applications.
| 1 | 0 | 0 | 0 | 0 | 0 |
The RoPES project with HARPS and HARPS-N. I. A system of super-Earths orbiting the moderately active K-dwarf HD 176986 | We report the discovery of a system of two super-Earths orbiting the
moderately active K-dwarf HD 176986. This work is part of the RoPES RV program
of G- and K-type stars, which combines radial velocities (RVs) from the HARPS
and HARPS-N spectrographs to search for short-period terrestrial planets. HD
176986 b and c are super-Earth planets with masses of 5.74 and 9.18
M$_{\oplus}$, orbital periods of 6.49 and 16.82 days, and distances of 0.063
and 0.119 AU in orbits that are consistent with circular. The host star is a
K2.5 dwarf, and despite its modest level of chromospheric activity (log(R'hk) =
- 4.90 +- 0.04), it shows a complex activity pattern. Along with the discovery
of the planets, we study the magnetic cycle and rotation of the star. HD 176986
proves to be suitable for testing the available RV analysis technique and
further our understanding of stellar activity.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the possibility of developing quasi-cw high-power high-pressure laser on 4p-4s transition of ArI with electron beam - optical pumping: quenching of 4s (3P2) lower laser level | A new electron beam-optical procedure is proposed for quasi-cw pumping of
high-pressure large-volume He-Ar laser on 4p[1/2]1 - 4s[3/2]2 argon atom
transition at the wavelength of 912.5 nm. It consists of creation and
maintenance of a necessary density of 4s[3/2]2 metastable state in the gain
medium by a fast electron beam and subsequent optically pumping of the upper
laser level via the classical three-level scheme using a laser diode.
Absorption probing is used to study collisional quenching of Ar* metastable in
electron-beam-excited high-pressure He-Ar mixtures with a low content of argon.
The rate constants for plasma-chemical reactions Ar*+He+Ar-Ar2*+He (3.6 +-
0.4)x10-33 cm6/s, Ar+2He-HeAr*+He (4.4 +- 0.9)x10-36 cm6/s and
Ar*+He-Products+He (2.4 +- 0.3)x10-15 cm3/s are for the first time measured.
| 0 | 1 | 0 | 0 | 0 | 0 |
An Adaptive Characteristic-wise Reconstruction WENOZ scheme for Gas Dynamic Euler Equations | Due to its excellent shock-capturing capability and high resolution, the WENO
scheme family has been widely used in varieties of compressive flow simulation.
However, for problems containing strong shocks and contact discontinuities,
such as the Lax shock tube problem, the WENO scheme still produces numerical
oscillations. To avoid such numerical oscillations, the characteristic-wise
construction method should be applied. Compared to component-wise
reconstruction, characteristic-wise reconstruction leads to much more
computational cost and thus is not suite for large scale simulation such as
direct numeric simulation of turbulence. In this paper, an adaptive
characteristic-wise reconstruction WENO scheme, i.e. the AdaWENO scheme, is
proposed to improve the computational efficiency of the characteristic-wise
reconstruction method. The new scheme performs characteristic-wise
reconstruction near discontinuities while switching to component-wise
reconstruction for smooth regions. Meanwhile, a new calculation strategy for
the WENO smoothness indicators is implemented to reduce over-all computational
cost. Several one dimensional and two dimensional numerical tests are performed
to validate and evaluate the AdaWENO scheme. Numerical results show that
AdaWENO maintains essentially non-oscillatory flow field near discontinuities
as the characteristic-wise reconstruction method. Besieds, compared to the
component-wise reconstruction, AdaWENO is about 40\% faster which indicates its
excellent efficiency.
| 0 | 1 | 0 | 0 | 0 | 0 |
A level set-based structural optimization code using FEniCS | This paper presents an educational code written using FEniCS, based on the
level set method, to perform compliance minimization in structural
optimization. We use the concept of distributed shape derivative to compute a
descent direction for the compliance, which is defined as a shape functional.
The use of the distributed shape derivative is facilitated by FEniCS, which
allows to handle complicated partial differential equations with a simple
implementation. The code is written for compliance minimization in the
framework of linearized elasticity, and can be easily adapted to tackle other
functionals and partial differential equations. We also provide an extension of
the code for compliant mechanisms. We start by explaining how to compute shape
derivatives, and discuss the differences between the distributed and boundary
expressions of the shape derivative. Then we describe the implementation in
details, and show the application of this code to some classical benchmarks of
topology optimization. The code is available at
this http URL, and the main file is also given in
the appendix.
| 0 | 0 | 1 | 0 | 0 | 0 |
Transaction Support over Redis: An Overview | This document outlines the approach to supporting cross-node transactions
over a Redis cluster.
| 1 | 0 | 0 | 0 | 0 | 0 |
Subgraphs and motifs in a dynamic airline network | How does the small-scale topological structure of an airline network behave
as the network evolves? To address this question, we study the dynamic and
spatial properties of small undirected subgraphs using 15 years of data on
Southwest Airlines' domestic route service. We find that this real-world
network has much in common with random graphs, and describe a possible
power-law scaling between subgraph counts and the number of edges in the
network, that appears to be quite robust to changes in network density and
size. We use analytic formulae to identify statistically over- and
under-represented subgraphs, known as motifs and anti-motifs, and discover the
existence of substantial topology transitions. We propose a simple
subgraph-based node ranking measure, that is not always highly correlated with
standard node centrality, and can identify important nodes relative to specific
topologies, and investigate the spatial "distribution" of the triangle subgraph
using graphical tools. Our results have implications for the way in which
subgraphs can be used to analyze real-world networks.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Glitch Phenomenon | The Principle of the Glitch states that for any device which makes a discrete
decision based upon a continuous range of possible inputs, there are inputs for
which it will take arbitrarily long to reach a decision. The appropriate
mathematical setting for studying this principle is described. This involves
defining the concept of continuity for mappings on sets of functions. It can
then be shown that the glitch principle follows from the continuous behavior of
the device.
| 1 | 0 | 1 | 0 | 0 | 0 |
Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel computing methods | In this article we perform an asymptotic analysis of Bayesian parallel
density estimators which are based on logspline density estimation. The
parallel estimator we introduce is in the spirit of a kernel density estimator
introduced in recent studies. We provide a numerical procedure that produces
the density estimator itself in place of the sampling algorithm. We then derive
an error bound for the mean integrated squared error for the full data
posterior density estimator. We also investigate the parameters that arise from
logspline density estimation and the numerical approximation procedure. Our
investigation identifies specific choices of parameters for logspline density
estimation that result in the error bound scaling appropriately in relation to
these choices.
| 0 | 0 | 1 | 1 | 0 | 0 |
Tunable Weyl and Dirac states in the nonsymmorphic compound $\rm\mathbf{CeSbTe}$ | Recent interest in topological semimetals has lead to the proposal of many
new topological phases that can be realized in real materials. Next to Dirac
and Weyl systems, these include more exotic phases based on manifold band
degeneracies in the bulk electronic structure. The exotic states in topological
semimetals are usually protected by some sort of crystal symmetry and the
introduction of magnetic order can influence these states by breaking time
reversal symmetry. Here we show that we can realize a rich variety of different
topological semimetal states in a single material, $\rm CeSbTe$. This compound
can exhibit different types of magnetic order that can be accessed easily by
applying a small field. It allows, therefore, for tuning the electronic
structure and can drive it through a manifold of topologically distinct phases,
such as the first nonsymmorphic magnetic topological material with an
eight-fold band crossing at a high symmetry point. Our experimental results are
backed by a full magnetic group theory analysis and ab initio calculations.
This discovery introduces a realistic and promising platform for studying the
interplay of magnetism and topology.
| 0 | 1 | 0 | 0 | 0 | 0 |
Personalised Query Suggestion for Intranet Search with Temporal User Profiling | Recent research has shown the usefulness of using collective user interaction
data (e.g., query logs) to recommend query modification suggestions for
Intranet search. However, most of the query suggestion approaches for Intranet
search follow an "one size fits all" strategy, whereby different users who
submit an identical query would get the same query suggestion list. This is
problematic, as even with the same query, different users may have different
topics of interest, which may change over time in response to the user's
interaction with the system. We address the problem by proposing a personalised
query suggestion framework for Intranet search. For each search session, we
construct two temporal user profiles: a click user profile using the user's
clicked documents and a query user profile using the user's submitted queries.
We then use the two profiles to re-rank the non-personalised query suggestion
list returned by a state-of-the-art query suggestion method for Intranet
search. Experimental results on a large-scale query logs collection show that
our personalised framework significantly improves the quality of suggested
queries.
| 1 | 0 | 0 | 0 | 0 | 0 |
Navigability of Random Geometric Graphs in the Universe and Other Spacetimes | Random geometric graphs in hyperbolic spaces explain many common structural
and dynamical properties of real networks, yet they fail to predict the correct
values of the exponents of power-law degree distributions observed in real
networks. In that respect, random geometric graphs in asymptotically de Sitter
spacetimes, such as the Lorentzian spacetime of our accelerating universe, are
more attractive as their predictions are more consistent with observations in
real networks. Yet another important property of hyperbolic graphs is their
navigability, and it remains unclear if de Sitter graphs are as navigable as
hyperbolic ones. Here we study the navigability of random geometric graphs in
three Lorentzian manifolds corresponding to universes filled only with dark
energy (de Sitter spacetime), only with matter, and with a mixture of dark
energy and matter as in our universe. We find that these graphs are navigable
only in the manifolds with dark energy. This result implies that, in terms of
navigability, random geometric graphs in asymptotically de Sitter spacetimes
are as good as random hyperbolic graphs. It also establishes a connection
between the presence of dark energy and navigability of the discretized causal
structure of spacetime, which provides a basis for a different approach to the
dark energy problem in cosmology.
| 1 | 1 | 0 | 0 | 0 | 0 |
Learning Geometric Concepts with Nasty Noise | We study the efficient learnability of geometric concept classes -
specifically, low-degree polynomial threshold functions (PTFs) and
intersections of halfspaces - when a fraction of the data is adversarially
corrupted. We give the first polynomial-time PAC learning algorithms for these
concept classes with dimension-independent error guarantees in the presence of
nasty noise under the Gaussian distribution. In the nasty noise model, an
omniscient adversary can arbitrarily corrupt a small fraction of both the
unlabeled data points and their labels. This model generalizes well-studied
noise models, including the malicious noise model and the agnostic (adversarial
label noise) model. Prior to our work, the only concept class for which
efficient malicious learning algorithms were known was the class of
origin-centered halfspaces.
Specifically, our robust learning algorithm for low-degree PTFs succeeds
under a number of tame distributions -- including the Gaussian distribution
and, more generally, any log-concave distribution with (approximately) known
low-degree moments. For LTFs under the Gaussian distribution, we give a
polynomial-time algorithm that achieves error $O(\epsilon)$, where $\epsilon$
is the noise rate. At the core of our PAC learning results is an efficient
algorithm to approximate the low-degree Chow-parameters of any bounded function
in the presence of nasty noise. To achieve this, we employ an iterative
spectral method for outlier detection and removal, inspired by recent work in
robust unsupervised learning. Our aforementioned algorithm succeeds for a range
of distributions satisfying mild concentration bounds and moment assumptions.
The correctness of our robust learning algorithm for intersections of
halfspaces makes essential use of a novel robust inverse independence lemma
that may be of broader interest.
| 1 | 0 | 0 | 0 | 0 | 0 |
The relationship between the number of editorial board members and the scientific output of universities in the chemistry field | Editorial board members, who are considered the gatekeepers of scientific
journals, play an important role in academia, and may directly or indirectly
affect the scientific output of a university. In this article, we used the
quantile regression method among a sample of 1,387 university in chemistry to
characterize the correlation between the number of editorial board members and
the scientific output of their universities. Furthermore, we used time-series
data and the Granger causality test to explore the causal relationship between
the number of editorial board members and the number of articles of some top
universities. Our results suggest that the number of editorial board members is
positively and significantly related to the scientific output (as measured by
the number of articles, total number of citations, citations per paper, and h
index) of their universities. However, the Granger causality test results
suggest that the causal relationship between the number of editorial board
members and the number of articles of some top universities is not obvious.
Combining these findings with the results of qualitative interviews with
editorial board members, we discuss the causal relationship between the number
of editorial board members and the scientific output of their universities.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Non-standard Standard Model | This paper examines the Standard Model under the strong-electroweak gauge
group $SU_S(3)\times U_{EW}(2)$ subject to the condition $u_{EW}(2)\not\cong
su_I(2)\oplus u_Y(1)$. Physically, the condition ensures that all electroweak
gauge bosons interact with each other prior to symmetry breaking --- as one
might expect from $U(2)$ invariance. This represents a crucial shift in the
notion of physical gauge bosons: Unlike the Standard Model which posits a
change of Lie algebra basis induced by spontaneous symmetry breaking, here the
basis is unaltered and $A,\,Z^0,\,W^\pm$ represent (modulo $U_{EW}(2)$ gauge
transformations) the physical bosons both \emph{before} and after spontaneous
symmetry breaking.
Our choice of $u_{EW}(2)$ basis requires some modification of the matter
field sector of the Standard Model. Careful attention to the product group
structure calls for strong-electroweak degrees of freedom in the
$(\mathbf{3},\mathbf{2})$ and the $(\mathbf{3},\overline{\mathbf{2}})$ of
$SU_S(3)\times U_{EW}(2)$ that possess integer electric charge just like
leptons. These degrees of freedom play the role of quarks, and they lead to a
modified Lagrangian that nevertheless reproduces transition rates and cross
sections equivalent to the Standard Model.
The close resemblance between quark and lepton electroweak doublets in this
picture suggests a mechanism for a phase transition between quarks and leptons
that stems from the product structure of the gauge group. Our hypothesis is
that the strong and electroweak bosons see each other as a source of
decoherence. In effect, leptons get identified with the $SU_S(3)$-trace of
quark representations. This mechanism allows for possible extensions of the
Standard Model that don't require large inclusive multiplets of matter fields.
As an example, we propose and investigate a model that turns out to have some
promising cosmological implications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Robust functional regression model for marginal mean and subject-specific inferences | We introduce flexible robust functional regression models, using various
heavy-tailed processes, including a Student $t$-process. We propose efficient
algorithms in estimating parameters for the marginal mean inferences and in
predicting conditional means as well interpolation and extrapolation for the
subject-specific inferences. We develop bootstrap prediction intervals for
conditional mean curves. Numerical studies show that the proposed model
provides robust analysis against data contamination or distribution
misspecification, and the proposed prediction intervals maintain the nominal
confidence levels. A real data application is presented as an illustrative
example.
| 0 | 0 | 0 | 1 | 0 | 0 |
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