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1,401 | Facial Keypoints Detection | Detect facial keypoints is a critical element in face recognition. However,
there is difficulty to catch keypoints on the face due to complex influences
from original images, and there is no guidance to suitable algorithms. In this
paper, we study different algorithms that can be applied to locate keyponits.
Specifically: our framework (1)prepare the data for further investigation
(2)Using PCA and LBP to process the data (3) Apply different algorithms to
analysis data, including linear regression models, tree based model, neural
network and convolutional neural network, etc. Finally we will give our
conclusion and further research topic. A comprehensive set of experiments on
dataset demonstrates the effectiveness of our framework.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,402 | Transitions from a Kondo-like diamagnetic insulator into a modulated ferromagnetic metal in $\bm{\mathrm{FeGa}_{3-y}\mathrm{Ge}_y}$ | One initial and essential question of magnetism is whether the magnetic
properties of a material are governed by localized moments or itinerant
electrons. Here we expose the case for the weakly ferromagnetic system
FeGa$_{3-y}$Ge$_y$ wherein these two opposite models are reconciled, such that
the magnetic susceptibility is quantitatively explained by taking into account
the effects of spin-spin correlation. With the electron doping introduced by Ge
substitution, the diamagnetic insulating parent compound FeGa$_3$ becomes a
paramagnetic metal as early as at $ y=0.01 $, and turns into a weakly
ferromagnetic metal around the quantum critical point $ y=0.15 $. Within the
ferromagnetic regime of FeGa$_{3-y}$Ge$_y$, the magnetic properties are of a
weakly itinerant ferromagnetic nature, located in the intermediate regime
between the localized and the itinerant dominance. Our analysis implies a
potential universality for all itinerant-electron ferromagnets.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,403 | Sample, computation vs storage tradeoffs for classification using tensor subspace models | In this paper, we exhibit the tradeoffs between the (training) sample,
computation and storage complexity for the problem of supervised classification
using signal subspace estimation. Our main tool is the use of tensor subspaces,
i.e. subspaces with a Kronecker structure, for embedding the data into lower
dimensions. Among the subspaces with a Kronecker structure, we show that using
subspaces with a hierarchical structure for representing data leads to improved
tradeoffs. One of the main reasons for the improvement is that embedding data
into these hierarchical Kronecker structured subspaces prevents overfitting at
higher latent dimensions.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,404 | One-step Estimation of Networked Population Size with Anonymity Using Respondent-Driven Capture-Recapture and Hashing | Estimates of population size for hidden and hard-to-reach individuals are of
particular interest to health officials when health problems are concentrated
in such populations. Efforts to derive these estimates are often frustrated by
a range of factors including social stigma or an association with illegal
activities that ordinarily preclude conventional survey strategies. This paper
builds on and extends prior work that proposed a method to meet these
challenges. Here we describe a rigorous formalization of a one-step,
network-based population estimation procedure that can be employed under
conditions of anonymity. The estimation procedure is designed to be implemented
alongside currently accepted strategies for research with hidden populations.
Simulation experiments are described that test the efficacy of the method
across a range of implementation conditions and hidden population sizes. The
results of these experiments show that reliable population estimates can be
derived for hidden, networked population as large as 12,500 and perhaps larger
for one family of random graphs. As such, the method shows potential for
cost-effective implementation health and disease surveillance officials
concerned with hidden populations. Limitations and future work are discussed in
the concluding section.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,405 | Real single ion solvation free energies with quantum mechanical simulation | Single ion solvation free energies are one of the most important properties
of electrolyte solutions and yet there is ongoing debate about what these
values are. Only the values for neutral ion pairs are known. Here, we use DFT
interaction potentials with molecular dynamics simulation (DFT-MD) combined
with a modified version of the quasi-chemical theory (QCT) to calculate these
energies for the lithium and fluoride ions. A method to correct for the error
in the DFT functional is developed and very good agreement with the
experimental value for the lithium fluoride pair is obtained. Moreover, this
method partitions the energies into physically intuitive terms such as surface
potential, cavity and charging energies which are amenable to descriptions with
reduced models. Our research suggests that lithium's solvation free energy is
dominated by the free energetics of a charged hard sphere, whereas fluoride
exhibits significant quantum mechanical behavior that cannot be simply
described with a reduced model.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,406 | Crowdsourcing with Sparsely Interacting Workers | We consider estimation of worker skills from worker-task interaction data
(with unknown labels) for the single-coin crowd-sourcing binary classification
model in symmetric noise. We define the (worker) interaction graph whose nodes
are workers and an edge between two nodes indicates whether or not the two
workers participated in a common task. We show that skills are asymptotically
identifiable if and only if an appropriate limiting version of the interaction
graph is irreducible and has odd-cycles. We then formulate a weighted rank-one
optimization problem to estimate skills based on observations on an
irreducible, aperiodic interaction graph. We propose a gradient descent scheme
and show that for such interaction graphs estimates converge asymptotically to
the global minimum. We characterize noise robustness of the gradient scheme in
terms of spectral properties of signless Laplacians of the interaction graph.
We then demonstrate that a plug-in estimator based on the estimated skills
achieves state-of-art performance on a number of real-world datasets. Our
results have implications for rank-one matrix completion problem in that
gradient descent can provably recover $W \times W$ rank-one matrices based on
$W+1$ off-diagonal observations of a connected graph with a single odd-cycle.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,407 | Training deep learning based denoisers without ground truth data | Recent deep learning based denoisers often outperform state-of-the-art
conventional denoisers such as BM3D. They are typically trained to minimize the
mean squared error (MSE) between the output of a deep neural network and the
ground truth image. In deep learning based denoisers, it is important to use
high quality noiseless ground truth for high performance, but it is often
challenging or even infeasible to obtain such a clean image in application
areas such as hyperspectral remote sensing and medical imaging. We propose a
Stein's Unbiased Risk Estimator (SURE) based method for training deep neural
network denoisers only with noisy images. We demonstrated that our SURE based
method without ground truth was able to train deep neural network denoisers to
yield performance close to deep learning denoisers trained with ground truth
and to outperform state-of-the-art BM3D. Further improvements were achieved by
including noisy test images for training denoiser networks using our proposed
SURE based method.
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1,408 | Language Design and Renormalization | Here we consider some well-known facts in syntax from a physics perspective,
which allows us to establish some remarkable equivalences. Specifically, we
observe that the operation MERGE put forward by N. Chomsky in 1995 can be
interpreted as a physical information coarse-graining. Thus, MERGE in
linguistics entails information renormalization in physics, according to
different time scales. We make this point mathematically formal in terms of
language models, i.e., probability distributions over word sequences, widely
used in natural language processing as well as other ambits. In this setting,
MERGE corresponds to a 3-index probability tensor implementing a
coarse-graining, akin to a probabilistic context-free grammar. The probability
vectors of meaningful sentences are naturally given by stochastic tensor
networks (TN) that are mostly loop-free, such as Tree Tensor Networks and
Matrix Product States. These structures have short-ranged correlations in the
syntactic distance by construction and, because of the peculiarities of human
language, they are extremely efficient to manipulate computationally. We also
propose how to obtain such language models from probability distributions of
certain TN quantum states, which we show to be efficiently preparable by a
quantum computer. Moreover, using tools from entanglement theory, we use these
quantum states to prove classical lower bounds on the perplexity of the
probability distribution for a set of words in a sentence. Implications of
these results are discussed in the ambits of theoretical and computational
linguistics, artificial intelligence, programming languages, RNA and protein
sequencing, quantum many-body systems, and beyond. Our work shows how many of
the key linguistic ideas from the last century, including developments in
computational linguistics, fit perfectly with known physical concepts linked to
renormalization.
| 1 | 1 | 0 | 0 | 0 | 0 |
1,409 | On the geometry of the moduli space of sheaves supported on curves of genus two in a quadric surface | We study the moduli space of stable sheaves of Euler characteristic 2,
supported on curves of arithmetic genus 2 contained in a smooth quadric
surface. We show that this moduli space is rational. We compute its Betti
numbers and we give a classification of the stable sheaves involving locally
free resolutions.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,410 | Attention Solves Your TSP, Approximately | The development of efficient (heuristic) algorithms for practical
combinatorial optimization problems is costly, so we want to automatically
learn them instead. We show the feasibility of this approach on the important
Travelling Salesman Problem (TSP). We learn a heuristic algorithm that uses a
Neural Network policy to construct a tour. As an alternative to the Pointer
Network, our model is based entirely on (graph) attention layers and is
invariant to the input order of the nodes. We train the model efficiently using
REINFORCE with a simple and robust baseline based on a deterministic (greedy)
rollout of the best policy so far. We significantly improve over results from
previous works that consider learned heuristics for the TSP, reducing the
optimality gap for a single tour construction from 1.51% to 0.32% for instances
with 20 nodes, from 4.59% to 1.71% for 50 nodes and from 6.89% to 4.43% for 100
nodes. Additionally, we improve over a recent Reinforcement Learning framework
for two variants of the Vehicle Routing Problem (VRP).
| 0 | 0 | 0 | 1 | 0 | 0 |
1,411 | A Distributed Online Pricing Strategy for Demand Response Programs | We study a demand response problem from utility (also referred to as
operator)'s perspective with realistic settings, in which the utility faces
uncertainty and limited communication. Specifically, the utility does not know
the cost function of consumers and cannot have multiple rounds of information
exchange with consumers. We formulate an optimization problem for the utility
to minimize its operational cost considering time-varying demand response
targets and responses of consumers. We develop a joint online learning and
pricing algorithm. In each time slot, the utility sends out a price signal to
all consumers and estimates the cost functions of consumers based on their
noisy responses. We measure the performance of our algorithm using regret
analysis and show that our online algorithm achieves logarithmic regret with
respect to the operating horizon. In addition, our algorithm employs linear
regression to estimate the aggregate response of consumers, making it easy to
implement in practice. Simulation experiments validate the theoretic results
and show that the performance gap between our algorithm and the offline
optimality decays quickly.
| 1 | 0 | 1 | 0 | 0 | 0 |
1,412 | Highly Nonlinear and Low Confinement Loss Photonic Crystal Fiber Using GaP Slot Core | This paper presents a triangular lattice photonic crystal fiber with very
high nonlinear coefficient. Finite element method (FEM) is used to scrutinize
different optical properties of proposed highly nonlinear photonic crystal
fiber (HNL-PCF). The HNL-PCF exhibits a high nonlinearity up to $10\times10^{4}
W^{-1}km^{-1}$ over the wavelength of 1500 nm to 1700 nm. Moreover, proposed
HNL-PCF shows a very low confinement loss of $10^{-3} dB/km$ at 1550 nm
wavelength. Furthermore, chromatic dispersion, dispersion slope, effective area
etc. are also analyzed thoroughly. The proposed fiber will be a suitable
candidate for broadband dispersion compensation, sensor devices and
supercontinuum generation.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,413 | Is Epicurus the father of Reinforcement Learning? | The Epicurean Philosophy is commonly thought as simplistic and hedonistic.
Here I discuss how this is a misconception and explore its link to
Reinforcement Learning. Based on the letters of Epicurus, I construct an
objective function for hedonism which turns out to be equivalent of the
Reinforcement Learning objective function when omitting the discount factor. I
then discuss how Plato and Aristotle 's views that can be also loosely linked
to Reinforcement Learning, as well as their weaknesses in relationship to it.
Finally, I emphasise the close affinity of the Epicurean views and the Bellman
equation.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,414 | Low-Precision Floating-Point Schemes for Neural Network Training | The use of low-precision fixed-point arithmetic along with stochastic
rounding has been proposed as a promising alternative to the commonly used
32-bit floating point arithmetic to enhance training neural networks training
in terms of performance and energy efficiency. In the first part of this paper,
the behaviour of the 12-bit fixed-point arithmetic when training a
convolutional neural network with the CIFAR-10 dataset is analysed, showing
that such arithmetic is not the most appropriate for the training phase. After
that, the paper presents and evaluates, under the same conditions, alternative
low-precision arithmetics, starting with the 12-bit floating-point arithmetic.
These two representations are then leveraged using local scaling in order to
increase accuracy and get closer to the baseline 32-bit floating-point
arithmetic. Finally, the paper introduces a simplified model in which both the
outputs and the gradients of the neural networks are constrained to
power-of-two values, just using 7 bits for their representation. The evaluation
demonstrates a minimal loss in accuracy for the proposed Power-of-Two neural
network, avoiding the use of multiplications and divisions and thereby,
significantly reducing the training time as well as the energy consumption and
memory requirements during the training and inference phases.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,415 | Deep Person Re-Identification with Improved Embedding and Efficient Training | Person re-identification task has been greatly boosted by deep convolutional
neural networks (CNNs) in recent years. The core of which is to enlarge the
inter-class distinction as well as reduce the intra-class variance. However, to
achieve this, existing deep models prefer to adopt image pairs or triplets to
form verification loss, which is inefficient and unstable since the number of
training pairs or triplets grows rapidly as the number of training data grows.
Moreover, their performance is limited since they ignore the fact that
different dimension of embedding may play different importance. In this paper,
we propose to employ identification loss with center loss to train a deep model
for person re-identification. The training process is efficient since it does
not require image pairs or triplets for training while the inter-class
distinction and intra-class variance are well handled. To boost the
performance, a new feature reweighting (FRW) layer is designed to explicitly
emphasize the importance of each embedding dimension, thus leading to an
improved embedding. Experiments on several benchmark datasets have shown the
superiority of our method over the state-of-the-art alternatives on both
accuracy and speed.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,416 | Unsupervised speech representation learning using WaveNet autoencoders | We consider the task of unsupervised extraction of meaningful latent
representations of speech by applying autoencoding neural networks to speech
waveforms. The goal is to learn a representation able to capture high level
semantic content from the signal, e.g. phoneme identities, while being
invariant to confounding low level details in the signal such as the underlying
pitch contour or background noise. The behavior of autoencoder models depends
on the kind of constraint that is applied to the latent representation. We
compare three variants: a simple dimensionality reduction bottleneck, a
Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE
(VQ-VAE). We analyze the quality of learned representations in terms of speaker
independence, the ability to predict phonetic content, and the ability to
accurately reconstruct individual spectrogram frames. Moreover, for discrete
encodings extracted using the VQ-VAE, we measure the ease of mapping them to
phonemes. We introduce a regularization scheme that forces the representations
to focus on the phonetic content of the utterance and report performance
comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic
unit discovery task.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,417 | Many-body localization caused by temporal disorder | The many-body localization (MBL) is commonly related to a strong spatial
disorder. We show that MBL may alternatively be generated by adding a temporal
disorder to periodically driven many-body systems. We reach this conclusion by
mapping the evolution of such systems on the dynamics of the time-independent,
disordered, Hubbard-like models. Our result opens the way to experimental
studies of MBL in systems that reveal crystalline structures in the time
domain. In particular, we discuss two relevant setups which can be implemented
in experiments on ultra-cold atomic gases.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,418 | Second-generation p-values: improved rigor, reproducibility, & transparency in statistical analyses | Verifying that a statistically significant result is scientifically
meaningful is not only good scientific practice, it is a natural way to control
the Type I error rate. Here we introduce a novel extension of the p-value - a
second-generation p-value - that formally accounts for scientific relevance and
leverages this natural Type I Error control. The approach relies on a
pre-specified interval null hypothesis that represents the collection of effect
sizes that are scientifically uninteresting or are practically null. The
second-generation p-value is the proportion of data-supported hypotheses that
are also null hypotheses. As such, second-generation p-values indicate when the
data are compatible with null hypotheses, or with alternative hypotheses, or
when the data are inconclusive. Moreover, second-generation p-values provide a
proper scientific adjustment for multiple comparisons and reduce false
discovery rates. This is an advance for environments rich in data, where
traditional p-value adjustments are needlessly punitive. Second-generation
p-values promote transparency, rigor and reproducibility of scientific results
by a priori specifying which candidate hypotheses are practically meaningful
and by providing a more reliable statistical summary of when the data are
compatible with alternative or null hypotheses.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,419 | Latency Optimal Broadcasting in Noisy Wireless Mesh Networks | In this paper, we adopt a new noisy wireless network model introduced very
recently by Censor-Hillel et al. in [ACM PODC 2017, CHHZ17]. More specifically,
for a given noise parameter $p\in [0,1],$ any sender has a probability of $p$
of transmitting noise or any receiver of a single transmission in its
neighborhood has a probability $p$ of receiving noise.
In this paper, we first propose a new asymptotically latency-optimal
approximation algorithm (under faultless model) that can complete
single-message broadcasting task in $D+O(\log^2 n)$ time units/rounds in any
WMN of size $n,$ and diameter $D$. We then show this diameter-linear
broadcasting algorithm remains robust under the noisy wireless network model
and also improves the currently best known result in CHHZ17 by a
$\Theta(\log\log n)$ factor.
In this paper, we also further extend our robust single-message broadcasting
algorithm to $k$ multi-message broadcasting scenario and show it can broadcast
$k$ messages in $O(D+k\log n+\log^2 n)$ time rounds. This new robust
multi-message broadcasting scheme is not only asymptotically optimal but also
answers affirmatively the problem left open in CHHZ17 on the existence of an
algorithm that is robust to sender and receiver faults and can broadcast $k$
messages in $O(D+k\log n + polylog(n))$ time rounds.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,420 | Construction of Directed 2K Graphs | We study the problem of constructing synthetic graphs that resemble
real-world directed graphs in terms of their degree correlations. We define the
problem of directed 2K construction (D2K) that takes as input the directed
degree sequence (DDS) and a joint degree and attribute matrix (JDAM) so as to
capture degree correlation specifically in directed graphs. We provide
necessary and sufficient conditions to decide whether a target D2K is
realizable, and we design an efficient algorithm that creates realizations with
that target D2K. We evaluate our algorithm in creating synthetic graphs that
target real-world directed graphs (such as Twitter) and we show that it brings
significant benefits compared to state-of-the-art approaches.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,421 | Pattern Generation Strategies for Improving Recognition of Handwritten Mathematical Expressions | Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging
problem because of the ambiguity and complexity of two-dimensional handwriting.
Moreover, the lack of large training data is a serious issue, especially for
academic recognition systems. In this paper, we propose pattern generation
strategies that generate shape and structural variations to improve the
performance of recognition systems based on a small training set. For data
generation, we employ the public databases: CROHME 2014 and 2016 of online
HMEs. The first strategy employs local and global distortions to generate shape
variations. The second strategy decomposes an online HME into sub-online HMEs
to get more structural variations. The hybrid strategy combines both these
strategies to maximize shape and structural variations. The generated online
HMEs are converted to images for offline HME recognition. We tested our
strategies in an end-to-end recognition system constructed from a recent deep
learning model: Convolutional Neural Network and attention-based
encoder-decoder. The results of experiments on the CROHME 2014 and 2016
databases demonstrate the superiority and effectiveness of our strategies: our
hybrid strategy achieved classification rates of 48.78% and 45.60%,
respectively, on these databases. These results are competitive compared to
others reported in recent literature. Our generated datasets are openly
available for research community and constitute a useful resource for the HME
recognition research in future.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,422 | Actions of automorphism groups of Lie groups | This is an expository article on properties of actions on Lie groups by
subgroups of their automorphism groups. After recalling various results on the
structure of the automorphism groups, we discuss actions with dense orbits,
invariant and quasi-invariant measures, the induced actions on the spaces of
probability measures on the groups, and results concerning various issues in
ergodic theory, topological dynamics, smooth dynamical systems, and probability
theory on Lie groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,423 | Interplay between relativistic energy corrections and resonant excitations in x-ray multiphoton ionization dynamics of Xe atoms | In this paper, we theoretically study x-ray multiphoton ionization dynamics
of heavy atoms taking into account relativistic and resonance effects. When an
atom is exposed to an intense x-ray pulse generated by an x-ray free-electron
laser (XFEL), it is ionized to a highly charged ion via a sequence of
single-photon ionization and accompanying relaxation processes, and its final
charge state is limited by the last ionic state that can be ionized by a
single-photon ionization. If x-ray multiphoton ionization involves deep
inner-shell electrons in heavy atoms, energy shifts by relativistic effects
play an important role in ionization dynamics, as pointed out in [Phys.\ Rev.\
Lett.\ \textbf{110}, 173005 (2013)]. On the other hand, if the x-ray beam has a
broad energy bandwidth, the high-intensity x-ray pulse can drive resonant
photo-excitations for a broad range of ionic states and ionize even beyond the
direct one-photon ionization limit, as first proposed in [Nature\ Photon.\
\textbf{6}, 858 (2012)]. To investigate both relativistic and resonance
effects, we extend the \textsc{xatom} toolkit to incorporate relativistic
energy corrections and resonant excitations in x-ray multiphoton ionization
dynamics calculations. Charge-state distributions are calculated for Xe atoms
interacting with intense XFEL pulses at a photon energy of 1.5~keV and 5.5~keV,
respectively. For both photon energies, we demonstrate that the role of
resonant excitations in ionization dynamics is altered due to significant
shifts of orbital energy levels by relativistic effects. Therefore it is
necessary to take into account both effects to accurately simulate multiphoton
multiple ionization dynamics at high x-ray intensity.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,424 | Collective spin excitations of helices and magnetic skyrmions: review and perspectives of magnonics in non-centrosymmetric magnets | Magnetic materials hosting correlated electrons play an important role for
information technology and signal processing. The currently used ferro-, ferri-
and antiferromagnetic materials provide microscopic moments (spins) that are
mainly collinear. Recently more complex spin structures such as spin helices
and cycloids have regained a lot of interest. The interest has been initiated
by the discovery of the skyrmion lattice phase in non-centrosymmetric helical
magnets. In this review we address how spin helices and skyrmion lattices
enrich the microwave characteristics of magnetic materials. When discussing
perspectives for microwave electronics and magnonics we focus particularly on
insulating materials as they avoid eddy current losses, offer low spin-wave
damping, and might allow for electric field control of collective spin
excitations. Thereby, they further fuel the vision of magnonics operated at low
energy consumption.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,425 | On the Relation between Color Image Denoising and Classification | Large amount of image denoising literature focuses on single channel images
and often experimentally validates the proposed methods on tens of images at
most. In this paper, we investigate the interaction between denoising and
classification on large scale dataset. Inspired by classification models, we
propose a novel deep learning architecture for color (multichannel) image
denoising and report on thousands of images from ImageNet dataset as well as
commonly used imagery. We study the importance of (sufficient) training data,
how semantic class information can be traded for improved denoising results. As
a result, our method greatly improves PSNR performance by 0.34 - 0.51 dB on
average over state-of-the art methods on large scale dataset. We conclude that
it is beneficial to incorporate in classification models. On the other hand, we
also study how noise affect classification performance. In the end, we come to
a number of interesting conclusions, some being counter-intuitive.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,426 | A simplicial decomposition framework for large scale convex quadratic programming | In this paper, we analyze in depth a simplicial decomposition like
algorithmic framework for large scale convex quadratic programming. In
particular, we first propose two tailored strategies for handling the master
problem. Then, we describe a few techniques for speeding up the solution of the
pricing problem. We report extensive numerical experiments on both real
portfolio optimization and general quadratic programming problems, showing the
efficiency and robustness of the method when compared to Cplex.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,427 | A Logic of Blockchain Updates | Blockchains are distributed data structures that are used to achieve
consensus in systems for cryptocurrencies (like Bitcoin) or smart contracts
(like Ethereum). Although blockchains gained a lot of popularity recently,
there is no logic-based model for blockchains available. We introduce BCL, a
dynamic logic to reason about blockchain updates, and show that BCL is sound
and complete with respect to a simple blockchain model.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,428 | A proof of the Flaherty-Keller formula on the effective property of densely packed elastic composites | We prove in a mathematically rigorous way the asymptotic formula of Flaherty
and Keller on the effective property of densely packed periodic elastic
composites with hard inclusions. The proof is based on the primal-dual
variational principle, where the upper bound is derived by using the
Keller-type test functions and the lower bound by singular functions made of
nuclei of strain. Singular functions are solutions of the Lamé system and
capture precisely singular behavior of the stress in the narrow region between
two adjacent hard inclusions.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,429 | Regret Bounds for Reinforcement Learning via Markov Chain Concentration | We give a simple optimistic algorithm for which it is easy to derive regret
bounds of $\tilde{O}(\sqrt{t_{\rm mix} SAT})$ after $T$ steps in uniformly
ergodic Markov decision processes with $S$ states, $A$ actions, and mixing time
parameter $t_{\rm mix}$. These bounds are the first regret bounds in the
general, non-episodic setting with an optimal dependence on all given
parameters. They could only be improved by using an alternative mixing time
parameter.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,430 | Superradiance with local phase-breaking effects | We study the superradiant evolution of a set of $N$ two-level systems
spontaneously radiating under the effect of phase-breaking mechanisms. We
investigate the dynamics generated by non-radiative losses and pure dephasing,
and their interplay with spontaneous emission. Our results show that in the
parameter region relevant to many solid-state cavity quantum electrodynamics
experiments, even with a dephasing rate much faster than the radiative lifetime
of a single two-level system, a sub-optimal collective superfluorescent burst
is still observable. We also apply our theory to the dilute excitation regime,
often used to describe optical excitations in solid-state systems. In this
regime, excitations can be described in terms of bright and dark bosonic
quasiparticles. We show how the effect of dephasing and losses in this regime
translates into inter-mode scattering rates and quasiparticle lifetimes.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,431 | Kinetic model of selectivity and conductivity of the KcsA filter | We introduce a self-consistent multi-species kinetic theory based on the
structure of the narrow voltage-gated potassium channel. Transition rates
depend on a complete energy spectrum with contributions including the
dehydration amongst species, interaction with the dipolar charge of the filter
and, bulk solution properties. It displays high selectivity between species
coexisting with fast conductivity, and Coulomb blockade phenomena, and it fits
well to data.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,432 | The affine approach to homogeneous geodesics in homogeneous Finsler spaces | In a recent paper, it was claimed that any homogeneous Finsler space of odd
dimension admits a homogeneous geodesic through any point. For the proof, the
algebraic method dealing with the reductive decomposition of the Lie algebra of
the isometry group was used. However, the proof contains a serious gap. In the
present paper, homogeneous geodesics in Finsler homogeneous spaces are studied
using the affine method, which was developed in earlier papers by the author.
The mentioned statement is proved correctly and it is further proved that any
homogeneous Berwald space or homogeneous reversible Finsler space admits a
homogeneous geodesic through any point.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,433 | About Synchronized Globular Cluster Formation over Supra-galactic Scales | Observational and theoretical arguments support the idea that violent events
connected with $AGN$ activity and/or intense star forming episodes have played
a significant role in the early phases of galaxy formation at high red shifts.
Being old stellar systems, globular clusters seem adequate candidates to search
for the eventual signatures that might have been left by those energetic
phenomena. The analysis of the colour distributions of several thousands of
globular clusters in the Virgo and Fornax galaxy clusters reveals the existence
of some interesting and previously undetected features. A simple pattern
recognition technique, indicates the presence of "colour modulations",
distinctive for each galaxy cluster. The results suggest that the globular
cluster formation process has not been completely stochastic but, rather,
included a significant fraction of globulars that formed in a synchronized way
and over supra-galactic spatial scales.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,434 | Geometric counting on wavefront real spherical spaces | We provide $L^p$-versus $L^\infty$-bounds for eigenfunctions on a real
spherical space $Z$ of wavefront type. It is shown that these bounds imply a
non-trivial error term estimate for lattice counting on $Z$. The paper also
serves as an introduction to geometric counting on spaces of the mentioned
type. Section 7 on higher rank is new and extends the result from v1 to higher
rank. Final version. To appear in Acta Math. Sinica.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,435 | Fate of the spin-\frac{1}{2} Kondo effect in the presence of temperature gradients | We consider a strongly interacting quantum dot connected to two leads held at
quite different temperatures. Our aim is to study the behavior of the Kondo
effect in the presence of large thermal biases. We use three different
approaches, namely, a perturbation formalism based on the Kondo Hamiltonian, a
slave-boson mean-field theory for the Anderson model at large charging energies
and a truncated equation-of-motion approach beyond the Hartree-Fock
approximation. The two former formalisms yield a suppression of the Kondo peak
for thermal gradients above the Kondo temperature, showing a remarkably good
agreement despite their different ranges of validity. The third technique
allows us to analyze the full density of states within a wide range of
energies. Additionally, we have investigated the quantum transport properties
(electric current and thermocurrent) beyond linear response. In the
voltage-driven case, we reproduce the split differential conductance due to the
presence of different electrochemical potentials. In the temperature-driven
case, we observe a strongly nonlinear thermocurrent as a function of the
applied thermal gradient. Depending on the parameters, we can find nontrivial
zeros in the electric current for finite values of the temperature bias.
Importantly, these thermocurrent zeros yield direct access to the system's
characteristic energy scales (Kondo temperature and charging energy).
| 0 | 1 | 0 | 0 | 0 | 0 |
1,436 | Extragalactic source population studies at very high energies in the Cherenkov Telescope Array era | The Cherenkov Telescope Array (CTA) is the next generation ground-based
$\gamma$-ray observatory. It will provide an order of magnitude better
sensitivity and an extended energy coverage, 20 GeV - 300 TeV, relative to
current Imaging Atmospheric Cherenkov Telescopes (IACTs). IACTs, despite
featuring an excellent sensitivity, are characterized by a limited field of
view that makes the blind search of new sources very time inefficient.
Fortunately, the $\textit{Fermi}$-LAT collaboration recently released a new
catalog of 1,556 sources detected in the 10 GeV - 2 TeV range by the Large Area
Telescope (LAT) in the first 7 years of its operation (the 3FHL catalog). This
catalog is currently the most appropriate description of the sky that will be
energetically accessible to CTA. Here, we discuss a detailed analysis of the
extragalactic source population (mostly blazars) that will be studied in the
near future by CTA. This analysis is based on simulations built from the
expected array configurations and information reported in the 3FHL catalog.
These results show the improvements that CTA will provide on the extragalactic
TeV source population studies, which will be carried out by Key Science
Projects as well as dedicated proposals.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,437 | Modeling the SBC Tanzania Production-Distribution Logistics Network | The increase in customer expectation in terms of cost and services rendered,
coupled with competitive business environment and uncertainty in cost of raw
materials have posed challenges on effective supply chain engineering making it
essential to do cost-benefit analysis before making final decisions on
production distribution logistics. This paper provides a conceptual model that
provide guidance in supply chain decision making for business expansion. It
presents a mathematical model for production-distribution of an integrated
supply chain derived from current operations of SBC Tanzania Ltd which is a
major supply chain that manages products' distribution in whole of Tanzania. In
addition to finding the optimal cost, we also carried out a sensitivity
analysis on the model so as to find ways in which the company can expand at
optimal cost, while meeting customers' demands. Genetic algorithms is used to
run the simulation for their efficient in solving combinatorial problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,438 | Dark matter in dwarf galaxies | Although the cusp-core controversy for dwarf galaxies is seen as a problem, I
argue that the cored central profiles can be explained by flattened cusps
because they suffer from conflicting measurements and poor statistics and
because there is a large number of conventional processes that could have
flattened them since their creation, none of which requires new physics. Other
problems, such as "too big to fail", are not discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,439 | A Survey of Parallel A* | A* is a best-first search algorithm for finding optimal-cost paths in graphs.
A* benefits significantly from parallelism because in many applications, A* is
limited by memory usage, so distributed memory implementations of A* that use
all of the aggregate memory on the cluster enable problems that can not be
solved by serial, single-machine implementations to be solved. We survey
approaches to parallel A*, focusing on decentralized approaches to A* which
partition the state space among processors. We also survey approaches to
parallel, limited-memory variants of A* such as parallel IDA*.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,440 | Large second harmonic generation enhancement in SiN waveguides by all-optically induced quasi phase matching | Integrated waveguides exhibiting efficient second-order nonlinearities are
crucial to obtain compact and low power optical signal processing devices.
Silicon nitride (SiN) has shown second harmonic generation (SHG) capabilities
in resonant structures and single-pass devices leveraging intermodal phase
matching, which is defined by waveguide design. Lithium niobate allows
compensating for the phase mismatch using periodically poled waveguides,
however the latter are not reconfigurable and remain difficult to integrate
with SiN or silicon (Si) circuits. Here we show the all-optical enhancement of
SHG in SiN waveguides by more than 30 dB. We demonstrate that a Watt-level
laser causes a periodic modification of the waveguide second-order
susceptibility. The resulting second order nonlinear grating has a periodicity
allowing for quasi phase matching (QPM) between the pump and SH mode. Moreover,
changing the pump wavelength or polarization updates the period, relaxing phase
matching constraints imposed by the waveguide geometry. We show that the
grating is long term inscribed in the waveguides, and we estimate a second
order nonlinearity of the order of 0.3 pm/V, while a maximum conversion
efficiency (CE) of 1.8x10-6 W-1 cm-2 is reached.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,441 | Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning | In this paper, we revisit the large-scale constrained linear regression
problem and propose faster methods based on some recent developments in
sketching and optimization. Our algorithms combine (accelerated) mini-batch SGD
with a new method called two-step preconditioning to achieve an approximate
solution with a time complexity lower than that of the state-of-the-art
techniques for the low precision case. Our idea can also be extended to the
high precision case, which gives an alternative implementation to the Iterative
Hessian Sketch (IHS) method with significantly improved time complexity.
Experiments on benchmark and synthetic datasets suggest that our methods indeed
outperform existing ones considerably in both the low and high precision cases.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,442 | Geometrical dependence of domain wall propagation and nucleation fields in magnetic domain wall sensor devices | We study the key domain wall properties in segmented nanowires loop-based
structures used in domain wall based sensors. The two reasons for device
failure, namely the distribution of domain wall propagation field (depinning)
and the nucleation field are determined with Magneto-Optical Kerr Effect (MOKE)
and Giant Magnetoresistance (GMR) measurements for thousands of elements to
obtain significant statistics. Single layers of Ni$_{81}$Fe$_{19}$, a complete
GMR stack with Co$_{90}$Fe$_{10}$/Ni$_{81}$Fe$_{19}$ as a free layer and a
single layer of Co$_{90}$Fe$_{10}$ are deposited and industrially patterned to
determine the influence of the shape anisotropy, the magnetocrystalline
anisotropy and the fabrication processes. We show that the propagation field is
little influenced by the geometry but significantly by material parameters. The
domain wall nucleation fields can be described by a typical Stoner-Wohlfarth
model related to the measured geometrical parameters of the wires and fitted by
considering the process parameters. The GMR effect is subsequently measured in
a substantial number of devices (3000), in order to accurately gauge the
variation between devices. This reveals a corrected upper limit to the
nucleation fields of the sensors that can be exploited for fast
characterization of working elements.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,443 | Faster Rates for Policy Learning | This article improves the existing proven rates of regret decay in optimal
policy estimation. We give a margin-free result showing that the regret decay
for estimating a within-class optimal policy is second-order for empirical risk
minimizers over Donsker classes, with regret decaying at a faster rate than the
standard error of an efficient estimator of the value of an optimal policy. We
also give a result from the classification literature that shows that faster
regret decay is possible via plug-in estimation provided a margin condition
holds. Four examples are considered. In these examples, the regret is expressed
in terms of either the mean value or the median value; the number of possible
actions is either two or finitely many; and the sampling scheme is either
independent and identically distributed or sequential, where the latter
represents a contextual bandit sampling scheme.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,444 | Anisotropic Exchange in ${\bf LiCu_2O_2}$ | We investigate the magnetic properties of the multiferroic quantum-spin
system LiCu$_2$O$_2$ by electron spin resonance (ESR) measurements at $X$- and
$Q$-band frequencies in a wide temperature range $(T_{\rm N1} \leq T \leq
300$\,K). The observed anisotropies of the $g$ tensor and the ESR linewidth in
untwinned single crystals result from the crystal-electric field and from local
exchange geometries acting on the magnetic Cu$^{2+}$ ions in the zigzag-ladder
like structure of LiCu$_2$O$_2$. Supported by a microscopic analysis of the
exchange paths involved, we show that both the symmetric anisotropic exchange
interaction and the antisymmetric Dzyaloshinskii-Moriya interaction provide the
dominant spin-spin relaxation channels in this material.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,445 | Which friends are more popular than you? Contact strength and the friendship paradox in social networks | The friendship paradox states that in a social network, egos tend to have
lower degree than their alters, or, "your friends have more friends than you
do". Most research has focused on the friendship paradox and its implications
for information transmission, but treating the network as static and
unweighted. Yet, people can dedicate only a finite fraction of their attention
budget to each social interaction: a high-degree individual may have less time
to dedicate to individual social links, forcing them to modulate the quantities
of contact made to their different social ties. Here we study the friendship
paradox in the context of differing contact volumes between egos and alters,
finding a connection between contact volume and the strength of the friendship
paradox. The most frequently contacted alters exhibit a less pronounced
friendship paradox compared with the ego, whereas less-frequently contacted
alters are more likely to be high degree and give rise to the paradox. We argue
therefore for a more nuanced version of the friendship paradox: "your closest
friends have slightly more friends than you do", and in certain networks even:
"your best friend has no more friends than you do". We demonstrate that this
relationship is robust, holding in both a social media and a mobile phone
dataset. These results have implications for information transfer and influence
in social networks, which we explore using a simple dynamical model.
| 1 | 1 | 0 | 0 | 0 | 0 |
1,446 | Stochastic Optimization with Bandit Sampling | Many stochastic optimization algorithms work by estimating the gradient of
the cost function on the fly by sampling datapoints uniformly at random from a
training set. However, the estimator might have a large variance, which
inadvertently slows down the convergence rate of the algorithms. One way to
reduce this variance is to sample the datapoints from a carefully selected
non-uniform distribution. In this work, we propose a novel non-uniform sampling
approach that uses the multi-armed bandit framework. Theoretically, we show
that our algorithm asymptotically approximates the optimal variance within a
factor of 3. Empirically, we show that using this datapoint-selection technique
results in a significant reduction in the convergence time and variance of
several stochastic optimization algorithms such as SGD, SVRG and SAGA. This
approach for sampling datapoints is general, and can be used in conjunction
with any algorithm that uses an unbiased gradient estimation -- we expect it to
have broad applicability beyond the specific examples explored in this work.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,447 | Learning Robust Options | Robust reinforcement learning aims to produce policies that have strong
guarantees even in the face of environments/transition models whose parameters
have strong uncertainty. Existing work uses value-based methods and the usual
primitive action setting. In this paper, we propose robust methods for learning
temporally abstract actions, in the framework of options. We present a Robust
Options Policy Iteration (ROPI) algorithm with convergence guarantees, which
learns options that are robust to model uncertainty. We utilize ROPI to learn
robust options with the Robust Options Deep Q Network (RO-DQN) that solves
multiple tasks and mitigates model misspecification due to model uncertainty.
We present experimental results which suggest that policy iteration with linear
features may have an inherent form of robustness when using coarse feature
representations. In addition, we present experimental results which demonstrate
that robustness helps policy iteration implemented on top of deep neural
networks to generalize over a much broader range of dynamics than non-robust
policy iteration.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,448 | Levitation of non-magnetizable droplet inside ferrofluid | The central theme of this work is that a stable levitation of a denser
non-magnetizable liquid droplet, against gravity, inside a relatively lighter
ferrofluid -- a system barely considered in ferrohydrodynamics -- is possible,
and exhibits unique interfacial features; the stability of the levitation
trajectory, however, is subject to an appropriate magnetic field modulation. We
explore the shapes and the temporal dynamics of a plane non-magnetizable
droplet levitating inside ferrofluid against gravity due to a spatially
complex, but systematically generated, magnetic field in two dimensions. The
effect of the viscosity ratio, the stability of the levitation path and the
possibility of existence of multiple-stable equilibrium states is investigated.
We find, for certain conditions on the viscosity ratio, that there can be
developments of cusps and singularities at the droplet surface; this phenomenon
we also observe experimentally and compared with the simulations. Our
simulations closely replicate the singular projection on the surface of the
levitating droplet. Finally, we present an dynamical model for the vertical
trajectory of the droplet. This model reveals a condition for the onset of
levitation and the relation for the equilibrium levitation height. The
linearization of the model around the steady state captures that the nature of
the equilibrium point goes under a transition from being a spiral to a node
depending upon the control parameters, which essentially means that the
temporal route to the equilibrium can be either monotonic or undulating. The
analytical model for the droplet trajectory is in close agreement with the
detailed simulations. (See draft for full abstract).
| 0 | 1 | 0 | 0 | 0 | 0 |
1,449 | Simultaneous Detection of H and D NMR Signals in a micro-Tesla Field | We present NMR spectra of remote-magnetized deuterated water, detected in an
unshielded environment by means of a differential atomic magnetometer. The
measurements are performed in a $\mu$T field, while pulsed techniques are
applied -following the sample displacement- in a 100~$\mu$T field, to tip both
D and H nuclei by controllable amounts. The broadband nature of the detection
system enables simultaneous detection of the two signals and accurate
evaluation of their decay times. The outcomes of the experiment demonstrate the
potential of ultra-low-field NMR spectroscopy in important applications where
the correlation between proton and deuteron spin-spin relaxation rates as a
function of external parameters contains significant information.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,450 | Learning Deep Networks from Noisy Labels with Dropout Regularization | Large datasets often have unreliable labels-such as those obtained from
Amazon's Mechanical Turk or social media platforms-and classifiers trained on
mislabeled datasets often exhibit poor performance. We present a simple,
effective technique for accounting for label noise when training deep neural
networks. We augment a standard deep network with a softmax layer that models
the label noise statistics. Then, we train the deep network and noise model
jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled)
dataset. The augmented model is overdetermined, so in order to encourage the
learning of a non-trivial noise model, we apply dropout regularization to the
weights of the noise model during training. Numerical experiments on noisy
versions of the CIFAR-10 and MNIST datasets show that the proposed dropout
technique outperforms state-of-the-art methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,451 | On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs | Modern networks are of huge sizes as well as high dynamics, which challenges
the efficiency of community detection algorithms. In this paper, we study the
problem of overlapping community detection on distributed and dynamic graphs.
Given a distributed, undirected and unweighted graph, the goal is to detect
overlapping communities incrementally as the graph is dynamically changing. We
propose an efficient algorithm, called \textit{randomized Speaker-Listener
Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener
Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of
label propagation. Besides detecting high-quality communities, rSLPA can
incrementally update the detected communities after a batch of edge insertion
and deletion operations. To the best of our knowledge, rSLPA is the first
algorithm that can incrementally capture the same communities as those obtained
by applying the detection algorithm from the scratch on the updated graph.
Extensive experiments are conducted on both synthetic and real-world datasets,
and the results show that our algorithm can achieve high accuracy and
efficiency at the same time.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,452 | Structured Black Box Variational Inference for Latent Time Series Models | Continuous latent time series models are prevalent in Bayesian modeling;
examples include the Kalman filter, dynamic collaborative filtering, or dynamic
topic models. These models often benefit from structured, non mean field
variational approximations that capture correlations between time steps. Black
box variational inference with reparameterization gradients (BBVI) allows us to
explore a rich new class of Bayesian non-conjugate latent time series models;
however, a naive application of BBVI to such structured variational models
would scale quadratically in the number of time steps. We describe a BBVI
algorithm analogous to the forward-backward algorithm which instead scales
linearly in time. It allows us to efficiently sample from the variational
distribution and estimate the gradients of the ELBO. Finally, we show results
on the recently proposed dynamic word embedding model, which was trained using
our method.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,453 | $L^p$ Norms of Eigenfunctions on Regular Graphs and on the Sphere | We prove upper bounds on the $L^p$ norms of eigenfunctions of the discrete
Laplacian on regular graphs. We then apply these ideas to study the $L^p$ norms
of joint eigenfunctions of the Laplacian and an averaging operator over a
finite collection of algebraic rotations of the $2$-sphere. Under mild
conditions, such joint eigenfunctions are shown to satisfy for large $p$ the
same bounds as those known for Laplace eigenfunctions on a surface of
non-positive curvature.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,454 | Spatially distributed multipartite entanglement enables Einstein-Podolsky-Rosen steering of atomic clouds | A key resource for distributed quantum-enhanced protocols is entanglement
between spatially separated modes. Yet, the robust generation and detection of
nonlocal entanglement between spatially separated regions of an ultracold
atomic system remains a challenge. Here, we use spin mixing in a tightly
confined Bose-Einstein condensate to generate an entangled state of
indistinguishable particles in a single spatial mode. We show experimentally
that this local entanglement can be spatially distributed by self-similar
expansion of the atomic cloud. Spatially resolved spin read-out is used to
reveal a particularly strong form of quantum correlations known as
Einstein-Podolsky-Rosen steering between distinct parts of the expanded cloud.
Based on the strength of Einstein-Podolsky-Rosen steering we construct a
witness, which testifies up to genuine five-partite entanglement.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,455 | Multipath IP Routing on End Devices: Motivation, Design, and Performance | Most end devices are now equipped with multiple network interfaces.
Applications can exploit all available interfaces and benefit from multipath
transmission. Recently Multipath TCP (MPTCP) was proposed to implement
multipath transmission at the transport layer and has attracted lots of
attention from academia and industry. However, MPTCP only supports TCP-based
applications and its multipath routing flexibility is limited. In this paper,
we investigate the possibility of orchestrating multipath transmission from the
network layer of end devices, and develop a Multipath IP (MPIP) design
consisting of signaling, session and path management, multipath routing, and
NAT traversal. We implement MPIP in Linux and Android kernels. Through
controlled lab experiments and Internet experiments, we demonstrate that MPIP
can effectively achieve multipath gains at the network layer. It not only
supports the legacy TCP and UDP protocols, but also works seamlessly with
MPTCP. By facilitating user-defined customized routing, MPIP can route traffic
from competing applications in a coordinated fashion to maximize the aggregate
user Quality-of-Experience.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,456 | Defense semantics of argumentation: encoding reasons for accepting arguments | In this paper we show how the defense relation among abstract arguments can
be used to encode the reasons for accepting arguments. After introducing a
novel notion of defenses and defense graphs, we propose a defense semantics
together with a new notion of defense equivalence of argument graphs, and
compare defense equivalence with standard equivalence and strong equivalence,
respectively. Then, based on defense semantics, we define two kinds of reasons
for accepting arguments, i.e., direct reasons and root reasons, and a notion of
root equivalence of argument graphs. Finally, we show how the notion of root
equivalence can be used in argumentation summarization.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,457 | Fast Global Convergence via Landscape of Empirical Loss | While optimizing convex objective (loss) functions has been a powerhouse for
machine learning for at least two decades, non-convex loss functions have
attracted fast growing interests recently, due to many desirable properties
such as superior robustness and classification accuracy, compared with their
convex counterparts. The main obstacle for non-convex estimators is that it is
in general intractable to find the optimal solution. In this paper, we study
the computational issues for some non-convex M-estimators. In particular, we
show that the stochastic variance reduction methods converge to the global
optimal with linear rate, by exploiting the statistical property of the
population loss. En route, we improve the convergence analysis for the batch
gradient method in \cite{mei2016landscape}.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,458 | Photodetector figures of merit in terms of POVMs | A photodetector may be characterized by various figures of merit such as
response time, bandwidth, dark count rate, efficiency, wavelength resolution,
and photon-number resolution. On the other hand, quantum theory says that any
measurement device is fully described by its POVM, which stands for
Positive-Operator-Valued Measure, and which generalizes the textbook notion of
the eigenstates of the appropriate hermitian operator (the "observable") as
measurement outcomes. Here we show how to define a multitude of photodetector
figures of merit in terms of a given POVM. We distinguish classical and quantum
figures of merit and issue a conjecture regarding trade-off relations between
them. We discuss the relationship between POVM elements and photodetector
clicks, and how models of photodetectors may be tested by measuring either POVM
elements or figures of merit. Finally, the POVM is advertised as a
platform-independent way of comparing different types of photodetectors, since
any such POVM refers to the Hilbert space of the incoming light, and not to any
Hilbert space internal to the detector.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,459 | Kinetics of Protein-DNA Interactions: First-Passage Analysis | All living systems can function only far away from equilibrium, and for this
reason chemical kinetic methods are critically important for uncovering the
mechanisms of biological processes. Here we present a new theoretical method of
investigating dynamics of protein-DNA interactions, which govern all major
biological processes. It is based on a first-passage analysis of biochemical
and biophysical transitions, and it provides a fully analytic description of
the processes. Our approach is explained for the case of a single protein
searching for a specific binding site on DNA. In addition, the application of
the method to investigations of the effect of DNA sequence heterogeneity, and
the role multiple targets and traps in the protein search dynamics are
discussed.
| 0 | 0 | 0 | 0 | 1 | 0 |
1,460 | Jamming transitions induced by an attraction in pedestrian flow | We numerically study jamming transitions in pedestrian flow interacting with
an attraction, mostly based on the social force model for pedestrians who can
join the attraction. We formulate the joining probability as a function of
social influence from others, reflecting that individual choice behavior is
likely influenced by others. By controlling pedestrian influx and the social
influence parameter, we identify various pedestrian flow patterns. For the
bidirectional flow scenario, we observe a transition from the free flow phase
to the freezing phase, in which oppositely walking pedestrians reach a complete
stop and block each other. On the other hand, a different transition behavior
appears in the unidirectional flow scenario, i.e., from the free flow phase to
the localized jam phase and then to the extended jam phase. It is also observed
that the extended jam phase can end up in freezing phenomena with a certain
probability when pedestrian flux is high with strong social influence. This
study highlights that attractive interactions between pedestrians and an
attraction can trigger jamming transitions by increasing the number of
conflicts among pedestrians near the attraction. In order to avoid excessive
pedestrian jams, we suggest suppressing the number of conflicts under a certain
level by moderating pedestrian influx especially when the social influence is
strong.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,461 | A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer | Survival analysis has been developed and applied in the number of areas
including manufacturing, finance, economics and healthcare. In healthcare
domain, usually clinical data are high-dimensional, sparse and complex and
sometimes there exists few amount of time-to-event (labeled) instances.
Therefore building an accurate survival model from electronic health records is
challenging. With this motivation, we address this issue and provide a new
survival analysis framework using deep learning and active learning with a
novel sampling strategy. First, our approach provides better representation
with lower dimensions from clinical features using labeled (time-to-event) and
unlabeled (censored) instances and then actively trains the survival model by
labeling the censored data using an oracle. As a clinical assistive tool, we
introduce a simple effective treatment recommendation approach based on our
survival model. In the experimental study, we apply our approach on
SEER-Medicare data related to prostate cancer among African-Americans and white
patients. The results indicate that our approach outperforms significantly than
baseline models.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,462 | Detecting Topological Changes in Dynamic Community Networks | The study of time-varying (dynamic) networks (graphs) is of fundamental
importance for computer network analytics. Several methods have been proposed
to detect the effect of significant structural changes in a time series of
graphs. The main contribution of this work is a detailed analysis of a dynamic
community graph model. This model is formed by adding new vertices, and
randomly attaching them to the existing nodes. It is a dynamic extension of the
well-known stochastic blockmodel. The goal of the work is to detect the time at
which the graph dynamics switches from a normal evolution -- where balanced
communities grow at the same rate -- to an abnormal behavior -- where
communities start merging. In order to circumvent the problem of decomposing
each graph into communities, we use a metric to quantify changes in the graph
topology as a function of time. The detection of anomalies becomes one of
testing the hypothesis that the graph is undergoing a significant structural
change. In addition the the theoretical analysis of the test statistic, we
perform Monte Carlo simulations of our dynamic graph model to demonstrate that
our test can detect changes in graph topology.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,463 | Online Boosting Algorithms for Multi-label Ranking | We consider the multi-label ranking approach to multi-label learning.
Boosting is a natural method for multi-label ranking as it aggregates weak
predictions through majority votes, which can be directly used as scores to
produce a ranking of the labels. We design online boosting algorithms with
provable loss bounds for multi-label ranking. We show that our first algorithm
is optimal in terms of the number of learners required to attain a desired
accuracy, but it requires knowledge of the edge of the weak learners. We also
design an adaptive algorithm that does not require this knowledge and is hence
more practical. Experimental results on real data sets demonstrate that our
algorithms are at least as good as existing batch boosting algorithms.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,464 | Semisuper Efimov effect of two-dimensional bosons at a three-body resonance | Wave-particle duality in quantum mechanics allows for a halo bound state
whose spatial extension far exceeds a range of the interaction potential. What
is even more striking is that such quantum halos can be arbitrarily large on
special occasions. The two examples known so far are the Efimov effect and the
super Efimov effect, which predict that spatial extensions of higher excited
states grow exponentially and double exponentially, respectively. Here, we
establish yet another new class of arbitrarily large quantum halos formed by
spinless bosons with short-range interactions in two dimensions. When the
two-body interaction is absent but the three-body interaction is resonant, four
bosons exhibit an infinite tower of bound states whose spatial extensions scale
as $R_n\sim e^{(\pi n)^2/27}$ for a large $n$. The emergent scaling law is
universal and is termed a semisuper Efimov effect, which together with the
Efimov and super Efimov effects constitutes a trio of few-body universality
classes allowing for arbitrarily large quantum halos.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,465 | Free quantitative fourth moment theorems on Wigner space | We prove a quantitative Fourth Moment Theorem for Wigner integrals of any
order with symmetric kernels, generalizing an earlier result from Kemp et al.
(2012). The proof relies on free stochastic analysis and uses a new biproduct
formula for bi-integrals. A consequence of our main result is a
Nualart-Ortiz-Latorre type characterization of convergence in law to the
semicircular distribution for Wigner integrals. As an application, we provide
Berry-Esseen type bounds in the context of the free Breuer-Major theorem for
the free fractional Brownian motion.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,466 | Optimizing the Latent Space of Generative Networks | Generative Adversarial Networks (GANs) have been shown to be able to sample
impressively realistic images. GAN training consists of a saddle point
optimization problem that can be thought of as an adversarial game between a
generator which produces the images, and a discriminator, which judges if the
images are real. Both the generator and the discriminator are commonly
parametrized as deep convolutional neural networks. The goal of this paper is
to disentangle the contribution of the optimization procedure and the network
parametrization to the success of GANs. To this end we introduce and study
Generative Latent Optimization (GLO), a framework to train a generator without
the need to learn a discriminator, thus avoiding challenging adversarial
optimization problems. We show experimentally that GLO enjoys many of the
desirable properties of GANs: learning from large data, synthesizing
visually-appealing samples, interpolating meaningfully between samples, and
performing linear arithmetic with noise vectors.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,467 | Conservativity of realizations on motives of abelian type over finite fields | We show that the l-adic realization functor is conservative when restricted
to the Chow motives of abelian type over a finite field. A weak version of this
conservativity result extends to mixed motives of abelian type.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,468 | Towards understanding startup product development as effectual entrepreneurial behaviors | Software startups face with multiple technical and business challenges, which
could make the startup journey longer, or even become a failure. Little is
known about entrepreneurial decision making as a direct force to startup
development outcome. In this study, we attempted to apply a behaviour theory of
entrepreneurial firms to understand the root-cause of some software startup s
challenges. Six common challenges related to prototyping and product
development in twenty software startups were identified. We found the behaviour
theory as a useful theoretical lens to explain the technical challenges.
Software startups search for local optimal solutions, emphasise on short-run
feedback rather than long-run strategies, which results in vague prototype
planning, paradox of demonstration and evolving throw-away prototypes. The
finding implies that effectual entrepreneurial processes might require a more
suitable product development approach than the current state-of-practice.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,469 | Generalized Dirac structure beyond the linear regime in graphene | We show that a generalized Dirac structure survives beyond the linear regime
of the low-energy dispersion relations of graphene. A generalized uncertainty
principle of the kind compatible with specific quantum gravity scenarios with a
fundamental minimal length (here graphene lattice spacing) and Lorentz
violation (here the particle/hole asymmetry, the trigonal warping, etc.) is
naturally obtained. We then show that the corresponding emergent field theory
is a table-top realization of such scenarios, by explicitly computing the third
order Hamiltonian, and giving the general recipe for any order. Remarkably, our
results imply that going beyond the low-energy approximation does not spoil the
well-known correspondence with analogue massless quantum electrodynamics
phenomena (as usually believed), but rather it is a way to obtain experimental
signatures of quantum-gravity-like corrections to such phenomena.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,470 | Generative Mixture of Networks | A generative model based on training deep architectures is proposed. The
model consists of K networks that are trained together to learn the underlying
distribution of a given data set. The process starts with dividing the input
data into K clusters and feeding each of them into a separate network. After
few iterations of training networks separately, we use an EM-like algorithm to
train the networks together and update the clusters of the data. We call this
model Mixture of Networks. The provided model is a platform that can be used
for any deep structure and be trained by any conventional objective function
for distribution modeling. As the components of the model are neural networks,
it has high capability in characterizing complicated data distributions as well
as clustering data. We apply the algorithm on MNIST hand-written digits and
Yale face datasets. We also demonstrate the clustering ability of the model
using some real-world and toy examples.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,471 | Shape-dependence of the barrier for skyrmions on a two-lane racetrack | Single magnetic skyrmions are localized whirls in the magnetization with an
integer winding number. They have been observed on nano-meter scales up to room
temperature in multilayer structures. Due to their small size, topological
winding number, and their ability to be manipulated by extremely tiny forces,
they are often called interesting candidates for future memory devices. The
two-lane racetrack has to exhibit two lanes that are separated by an energy
barrier. The information is then encoded in the position of a skyrmion which is
located in one of these close-by lanes. The artificial barrier between the
lanes can be created by an additional nanostrip on top of the track. Here we
study the dependence of the potential barrier on the shape of the additional
nanostrip, calculating the potentials for a rectangular, triangular, and
parabolic cross section, as well as interpolations between the first two. We
find that a narrow barrier is always repulsive and that the height of the
potential strongly depends on the shape of the nanostrip, whereas the shape of
the potential is more universal. We finally show that the shape-dependence is
redundant for possible applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,472 | Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing | We complement the theory developed in Preinerstorfer and Pötscher (2016)
with further finite sample results on size and power of heteroskedasticity and
autocorrelation robust tests. These allows us, in particular, to show that the
sufficient conditions for the existence of size-controlling critical values
recently obtained in Pötscher and Preinerstorfer (2016) are often also
necessary. We furthermore apply the results obtained to tests for hypotheses on
deterministic trends in stationary time series regressions, and find that many
tests currently used are strongly size-distorted.
| 0 | 0 | 1 | 1 | 0 | 0 |
1,473 | A powerful approach to the study of moderate effect modification in observational studies | Effect modification means the magnitude or stability of a treatment effect
varies as a function of an observed covariate. Generally, larger and more
stable treatment effects are insensitive to larger biases from unmeasured
covariates, so a causal conclusion may be considerably firmer if this pattern
is noted if it occurs. We propose a new strategy, called the submax-method,
that combines exploratory and confirmatory efforts to determine whether there
is stronger evidence of causality - that is, greater insensitivity to
unmeasured confounding - in some subgroups of individuals. It uses the joint
distribution of test statistics that split the data in various ways based on
certain observed covariates. For $L$ binary covariates, the method splits the
population $L$ times into two subpopulations, perhaps first men and women,
perhaps then smokers and nonsmokers, computing a test statistic from each
subpopulation, and appends the test statistic for the whole population, making
$2L+1$ test statistics in total. Although $L$ binary covariates define $2^{L}$
interaction groups, only $2L+1$ tests are performed, and at least $L+1$ of
these tests use at least half of the data. The submax-method achieves the
highest design sensitivity and the highest Bahadur efficiency of its component
tests. Moreover, the form of the test is sufficiently tractable that its large
sample power may be studied analytically. The simulation suggests that the
submax method exhibits superior performance, in comparison with an approach
using CART, when there is effect modification of moderate size. Using data from
the NHANES I Epidemiologic Follow-Up Survey, an observational study of the
effects of physical activity on survival is used to illustrate the method. The
method is implemented in the $\texttt{R}$ package $\texttt{submax}$ which
contains the NHANES example.
| 0 | 0 | 0 | 1 | 0 | 0 |
1,474 | Ad-blocking: A Study on Performance, Privacy and Counter-measures | Many internet ventures rely on advertising for their revenue. However, users
feel discontent by the presence of ads on the websites they visit, as the
data-size of ads is often comparable to that of the actual content. This has an
impact not only on the loading time of webpages, but also on the internet bill
of the user in some cases. In absence of a mutually-agreed procedure for opting
out of advertisements, many users resort to ad-blocking browser-extensions. In
this work, we study the performance of popular ad-blockers on a large set of
news websites. Moreover, we investigate the benefits of ad-blockers on user
privacy as well as the mechanisms used by websites to counter them. Finally, we
explore the traffic overhead due to the ad-blockers themselves.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,475 | On the quantum differentiation of smooth real-valued functions | Calculating the value of $C^{k\in\{1,\infty\}}$ class of smoothness
real-valued function's derivative in point of $\mathbb{R}^+$ in radius of
convergence of its Taylor polynomial (or series), applying an analog of
Newton's binomial theorem and $q$-difference operator. $(P,q)$-power difference
introduced in section 5. Additionally, by means of Newton's interpolation
formula, the discrete analog of Taylor series, interpolation using
$q$-difference and $p,q$-power difference is shown.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,476 | On recognizing shapes of polytopes from their shadows | Let $P$ and $Q$ be two convex polytopes both contained in the interior of an
Euclidean ball $r\textbf{B}^{d}$. We prove that $P=Q$ provided that their sight
cones from any point on the sphere $rS^{d-1}$ are congruent. We also prove an
analogous result for spherical projections.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,477 | Variational methods for steady-state Darcy/Fick flow in swollen and poroelastic solids | Existence of steady states in elastic media at small strains with diffusion
of a solvent or fluid due to Fick's or Darcy's laws is proved by combining
usage of variational methods inspired from static situations with Schauder's
fixed-point arguments. In the plain variant, the problem consists in the force
equilibrium coupled with the continuity equation, and the underlying operator
is non-potential and non-pseudomonotone so that conventional methods are not
applicable. In advanced variants, electrically-charged multi-component flows
through an electrically charged elastic solid are treated, employing critical
points of the saddle-point type. Eventually, anisothermal variants involving
heat-transfer equation are treated, too.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,478 | Case Studies on Plasma Wakefield Accelerator Design | The field of plasma-based particle accelerators has seen tremendous progress
over the past decade and experienced significant growth in the number of
activities. During this process, the involved scientific community has expanded
from traditional university-based research and is now encompassing many large
research laboratories worldwide, such as BNL, CERN, DESY, KEK, LBNL and SLAC.
As a consequence, there is a strong demand for a consolidated effort in
education at the intersection of accelerator, laser and plasma physics. The
CERN Accelerator School on Plasma Wake Acceleration has been organized as a
result of this development. In this paper, we describe the interactive
component of this one-week school, which consisted of three case studies to be
solved in 11 working groups by the participants of the CERN Accelerator School.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,479 | GANs for Biological Image Synthesis | In this paper, we propose a novel application of Generative Adversarial
Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy.
Compared to natural images, cells tend to have a simpler and more geometric
global structure that facilitates image generation. However, the correlation
between the spatial pattern of different fluorescent proteins reflects
important biological functions, and synthesized images have to capture these
relationships to be relevant for biological applications. We adapt GANs to the
task at hand and propose new models with casual dependencies between image
channels that can generate multi-channel images, which would be impossible to
obtain experimentally. We evaluate our approach using two independent
techniques and compare it against sensible baselines. Finally, we demonstrate
that by interpolating across the latent space we can mimic the known changes in
protein localization that occur through time during the cell cycle, allowing us
to predict temporal evolution from static images.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,480 | An objective classification of Saturn cloud features from Cassini ISS images | A clustering algorithm is applied to Cassini Imaging Science Subsystem
continuum and methane band images of Saturns northern hemisphere to objectively
classify regional albedo features and aid in their dynamical interpretation.
The procedure is based on a technique applied previously to visible-infrared
images of Earth. It provides a new perspective on giant planet cloud morphology
and its relationship to the dynamics and a meteorological context for the
analysis of other types of simultaneous Saturn observations. The method
identifies six clusters that exhibit distinct morphology, vertical structure,
and preferred latitudes of occurrence. These correspond to areas dominated by
deep convective cells; low contrast areas, some including thinner and thicker
clouds possibly associated with baroclinic instability; regions with possible
isolated thin cirrus clouds; darker areas due to thinner low level clouds or
clearer skies due to downwelling, or due to absorbing particles; and fields of
relatively shallow cumulus clouds. The spatial associations among these cloud
types suggest that dynamically, there are three distinct types of latitude
bands on Saturn: deep convectively disturbed latitudes in cyclonic shear
regions poleward of the eastward jets; convectively suppressed regions near and
surrounding the westward jets; and baroclinically unstable latitudes near
eastward jet cores and in the anti-cyclonic regions equatorward of them. These
are roughly analogous to some of the features of Earths tropics, subtropics,
and midlatitudes, respectively. Temporal variations of feature contrast and
cluster occurrence suggest that the upper tropospheric haze in the northern
hemisphere may have thickened by 2014.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,481 | The Peridynamic Stress Tensors and the Non-local to Local Passage | We re-examine the notion of stress in peridynamics. Based on the idea of
traction we define two new peridynamic stress tensors $\mathbf{P}^{\mathbf{y}}$
and $\mathbf{P}$ which stand, respectively, for analogues of the Cauchy and 1st
Piola-Kirchhoff stress tensors from classical elasticity. We show that the
tensor $\mathbf{P}$ differs from the earlier defined peridynamic stress tensor
$\nu$; though their divergence is equal. We address the question of symmetry of
the tensor $\mathbf{P}^{\mathbf{y}}$ which proves to be symmetric in case of
bond-based peridynamics; as opposed to the inverse Piola transform of $\nu$
(corresponding to the analogue of Cauchy stress tensor) which fails to be
symmetric in general. We also derive a general formula of the force-flux in
peridynamics and compute the limit of $\mathbf{P}$ for vanishing non-locality,
denoted by $\mathbf{P}_0$. We show that this tensor $\mathbf{P}_0$ surprisingly
coincides with the collapsed tensor $\nu_0$, a limit of the original tensor
$\nu$. At the end, using this flux-formula, we suggest an explanation why the
collapsed tensor $\mathbf{P}_0$ (and hence $\nu_0$) can be indeed identified
with the 1st Piola-Kirchhoff stress tensor.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,482 | Identification of Unmodeled Objects from Symbolic Descriptions | Successful human-robot cooperation hinges on each agent's ability to process
and exchange information about the shared environment and the task at hand.
Human communication is primarily based on symbolic abstractions of object
properties, rather than precise quantitative measures. A comprehensive robotic
framework thus requires an integrated communication module which is able to
establish a link and convert between perceptual and abstract information.
The ability to interpret composite symbolic descriptions enables an
autonomous agent to a) operate in unstructured and cluttered environments, in
tasks which involve unmodeled or never seen before objects; and b) exploit the
aggregation of multiple symbolic properties as an instance of ensemble
learning, to improve identification performance even when the individual
predicates encode generic information or are imprecisely grounded.
We propose a discriminative probabilistic model which interprets symbolic
descriptions to identify the referent object contextually w.r.t.\ the structure
of the environment and other objects. The model is trained using a collected
dataset of identifications, and its performance is evaluated by quantitative
measures and a live demo developed on the PR2 robot platform, which integrates
elements of perception, object extraction, object identification and grasping.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,483 | Balanced News Using Constrained Bandit-based Personalization | We present a prototype for a news search engine that presents balanced
viewpoints across liberal and conservative articles with the goal of
de-polarizing content and allowing users to escape their filter bubble. The
balancing is done according to flexible user-defined constraints, and leverages
recent advances in constrained bandit optimization. We showcase our balanced
news feed by displaying it side-by-side with the news feed produced by a
traditional (polarized) feed.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,484 | Intuitionistic Layered Graph Logic: Semantics and Proof Theory | Models of complex systems are widely used in the physical and social
sciences, and the concept of layering, typically building upon graph-theoretic
structure, is a common feature. We describe an intuitionistic substructural
logic called ILGL that gives an account of layering. The logic is a bunched
system, combining the usual intuitionistic connectives, together with a
non-commutative, non-associative conjunction (used to capture layering) and its
associated implications. We give soundness and completeness theorems for a
labelled tableaux system with respect to a Kripke semantics on graphs. We then
give an equivalent relational semantics, itself proven equivalent to an
algebraic semantics via a representation theorem. We utilise this result in two
ways. First, we prove decidability of the logic by showing the finite
embeddability property holds for the algebraic semantics. Second, we prove a
Stone-type duality theorem for the logic. By introducing the notions of ILGL
hyperdoctrine and indexed layered frame we are able to extend this result to a
predicate version of the logic and prove soundness and completeness theorems
for an extension of the layered graph semantics . We indicate the utility of
predicate ILGL with a resource-labelled bigraph model.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,485 | Learning Efficient Image Representation for Person Re-Identification | Color names based image representation is successfully used in person
re-identification, due to the advantages of being compact, intuitively
understandable as well as being robust to photometric variance. However, there
exists the diversity between underlying distribution of color names' RGB values
and that of image pixels' RGB values, which may lead to inaccuracy when
directly comparing them in Euclidean space. In this paper, we propose a new
method named soft Gaussian mapping (SGM) to address this problem. We model the
discrepancies between color names and pixels using a Gaussian and utilize the
inverse of covariance matrix to bridge the gap between them. Based on SGM, an
image could be converted to several soft Gaussian maps. In each soft Gaussian
map, we further seek to establish stable and robust descriptors within a local
region through a max pooling operation. Then, a robust image representation
based on color names is obtained by concatenating the statistical descriptors
in each stripe. When labeled data are available, one discriminative subspace
projection matrix is learned to build efficient representations of an image via
cross-view coupling learning. Experiments on the public datasets - VIPeR,
PRID450S and CUHK03, demonstrate the effectiveness of our method.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,486 | Exciting Nucleons in Compton Scattering and Hydrogen-Like Atoms | This PhD thesis is devoted to the low-energy structure of the nucleon (proton
and neutron) as seen through electromagnetic probes, e.g., electron and Compton
scattering. The research presented here is based primarily on dispersion theory
and chiral effective-field theory. The main motivation is the recent proton
radius puzzle, which is the discrepancy between the classic proton charge
radius determinations (based on electron-proton scattering and normal hydrogen
spectroscopy) and the highly precise extraction based on first muonic-hydrogen
experiments by the CREMA Collaboration. The precision of muonic-hydrogen
experiments is presently limited by the knowledge of proton structure effects
beyond the charge radius. A major part of this thesis is devoted to calculating
these effects using everything we know about the nucleon electromagnetic
structure from both theory and experiment.
The thesis consists of eight chapters. The first and last are, respectively,
the introduction and conclusion. The remainder of this thesis can roughly be
divided into the following three topics: finite-size effects in hydrogen-like
atoms, real and virtual Compton scattering, and two-photon-exchange effects.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,487 | Multiple universalities in order-disorder magnetic phase transitions | Phase transitions in isotropic quantum antiferromagnets are associated with
the condensation of bosonic triplet excitations. In three dimensional quantum
antiferromagnets, such as TlCuCl$_3$, condensation can be either pressure or
magnetic field induced. The corresponding magnetic order obeys universal
scaling with thermal critical exponent $\phi$. Employing a relativistic quantum
field theory, the present work predicts the emergence of multiple (three)
universalities under combined pressure and field tuning. Changes of
universality are signalled by changes of the critical exponent $\phi$.
Explicitly, we predict the existence of two new exponents $\phi=1$ and $1/2$ as
well as recovering the known exponent $\phi=3/2$. We also predict logarithmic
corrections to the power law scaling.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,488 | Exact Inference of Causal Relations in Dynamical Systems | From philosophers of ancient times to modern economists, biologists and other
researchers are engaged in revealing causal relations. The most challenging
problem is inferring the type of the causal relationship: whether it is uni- or
bi-directional or only apparent - implied by a hidden common cause only. Modern
technology provides us tools to record data from complex systems such as the
ecosystem of our planet or the human brain, but understanding their functioning
needs detection and distinction of causal relationships of the system
components without interventions. Here we present a new method, which
distinguishes and assigns probabilities to the presence of all the possible
causal relations between two or more time series from dynamical systems. The
new method is validated on synthetic datasets and applied to EEG
(electroencephalographic) data recorded in epileptic patients. Given the
universality of our method, it may find application in many fields of science.
| 0 | 0 | 0 | 0 | 1 | 0 |
1,489 | Privacy-Preserving Deep Inference for Rich User Data on The Cloud | Deep neural networks are increasingly being used in a variety of machine
learning applications applied to rich user data on the cloud. However, this
approach introduces a number of privacy and efficiency challenges, as the cloud
operator can perform secondary inferences on the available data. Recently,
advances in edge processing have paved the way for more efficient, and private,
data processing at the source for simple tasks and lighter models, though they
remain a challenge for larger, and more complicated models. In this paper, we
present a hybrid approach for breaking down large, complex deep models for
cooperative, privacy-preserving analytics. We do this by breaking down the
popular deep architectures and fine-tune them in a particular way. We then
evaluate the privacy benefits of this approach based on the information exposed
to the cloud service. We also asses the local inference cost of different
layers on a modern handset for mobile applications. Our evaluations show that
by using certain kind of fine-tuning and embedding techniques and at a small
processing costs, we can greatly reduce the level of information available to
unintended tasks applied to the data feature on the cloud, and hence achieving
the desired tradeoff between privacy and performance.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,490 | Gradient Method With Inexact Oracle for Composite Non-Convex Optimization | In this paper, we develop new first-order method for composite non-convex
minimization problems with simple constraints and inexact oracle. The objective
function is given as a sum of "`hard"', possibly non-convex part, and
"`simple"' convex part. Informally speaking, oracle inexactness means that, for
the "`hard"' part, at any point we can approximately calculate the value of the
function and construct a quadratic function, which approximately bounds this
function from above. We give several examples of such inexactness: smooth
non-convex functions with inexact Hölder-continuous gradient, functions given
by auxiliary uniformly concave maximization problem, which can be solved only
approximately. For the introduced class of problems, we propose a gradient-type
method, which allows to use different proximal setup to adapt to geometry of
the feasible set, adaptively chooses controlled oracle error, allows for
inexact proximal mapping. We provide convergence rate for our method in terms
of the norm of generalized gradient mapping and show that, in the case of
inexact Hölder-continuous gradient, our method is universal with respect to
Hölder parameters of the problem. Finally, in a particular case, we show that
small value of the norm of generalized gradient mapping at a point means that a
necessary condition of local minimum approximately holds at that point.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,491 | Kernel Implicit Variational Inference | Recent progress in variational inference has paid much attention to the
flexibility of variational posteriors. One promising direction is to use
implicit distributions, i.e., distributions without tractable densities as the
variational posterior. However, existing methods on implicit posteriors still
face challenges of noisy estimation and computational infeasibility when
applied to models with high-dimensional latent variables. In this paper, we
present a new approach named Kernel Implicit Variational Inference that
addresses these challenges. As far as we know, for the first time implicit
variational inference is successfully applied to Bayesian neural networks,
which shows promising results on both regression and classification tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
1,492 | The Ringel dual of the Auslander-Dlab-Ringel algebra | The ADR algebra $R_A$ of a finite-dimensional algebra $A$ is a
quasihereditary algebra. In this paper we study the Ringel dual
$\mathcal{R}(R_A)$ of $R_A$. We prove that $\mathcal{R}(R_A)$ can be identified
with $(R_{A^{op}})^{op}$, under certain 'minimal' regularity conditions for
$A$. We also give necessary and sufficient conditions for the ADR algebra to be
Ringel selfdual.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,493 | The socle filtrations of principal series representations of $SL(3,\mathbb{R})$ and $Sp(2,\mathbb{R})$ | We study the structure of the $(\mathfrak{g},K)$-modules of the principal
series representations of $SL(3,\mathbb{R})$ and $Sp(2,\mathbb{R})$ induced
from minimal parabolic subgroups, in the case when the infinitesimal character
is nonsingular. The composition factors of these modules are known by
Kazhdan-Lusztig-Vogan conjecture. In this paper, we give complete descriptions
of the socle filtrations of these modules.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,494 | Improving the phase response of an atom interferometer by means of temporal pulse shaping | We study theoretically and experimentally the influence of temporally shaping
the light pulses in an atom interferometer, with a focus on the phase response
of the interferometer. We show that smooth light pulse shapes allow rejecting
high frequency phase fluctuations (above the Rabi frequency) and thus relax the
requirements on the phase noise or frequency noise of the interrogation lasers
driving the interferometer. The light pulse shape is also shown to modify the
scale factor of the interferometer, which has to be taken into account in the
evaluation of its accuracy budget. We discuss the trade-offs to operate when
choosing a particular pulse shape, by taking into account phase noise
rejection, velocity selectivity, and applicability to large momentum transfer
atom interferometry.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,495 | Helium-like and Lithium-like ions: Ground state energy | It is shown that the non-relativistic ground state energy of helium-like and
lithium-like ions with static nuclei can be interpolated in full physics range
of nuclear charges $Z$ with accuracy of not less than 6 decimal digits (d.d.)
or 7-8 significant digits (s.d.) using a meromorphic function in appropriate
variable with a few free parameters. It is demonstrated that finite nuclear
mass effects do not change 4-5 s.d. for $Z \in [1,50]$ for 2-,3-electron
systems and the leading relativistic and QED corrections leave unchanged 3-4
s.d. for $Z \in [1,12]$ in the ground state energy for 2-electron system, thus,
the interpolation reproduces definitely those figures. A meaning of proposed
interpolation is in a construction of unified, {\it two-point} Pade approximant
(for both small and large $Z$ expansions) with fitting some parameters at
intermediate $Z$.
| 0 | 1 | 0 | 0 | 0 | 0 |
1,496 | Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method | Traditional data cleaning identifies dirty data by classifying original data
sequences, which is a class$-$imbalanced problem since the proportion of
incorrect data is much less than the proportion of correct ones for most
diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using
machine learning algorithms to classify diagnostic data based on
class$-$imbalanced training set, most classifiers are biased towards the major
class and show very poor classification rates on the minor class. By
transforming the direct classification problem about original data sequences
into a classification problem about the physical similarity between data
sequences, the class$-$balanced effect of Time$-$Domain Global Similarity
(TDGS) method on training set structure is investigated in this paper.
Meanwhile, the impact of improved training set structure on data cleaning
performance of TDGS method is demonstrated with an application example in EAST
POlarimetry$-$INTerferometry (POINT) system.
| 1 | 0 | 0 | 0 | 0 | 0 |
1,497 | Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines | Three complementary methods have been implemented in the code Denovo that
accelerate neutral particle transport calculations with methods that use
leadership-class computers fully and effectively: a multigroup block (MG)
Krylov solver, a Rayleigh Quotient Iteration (RQI) eigenvalue solver, and a
multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more
quickly than Gauss Seidel and enables energy decomposition such that Denovo can
scale to hundreds of thousands of cores. RQI should converge in fewer
iterations than power iteration (PI) for large and challenging problems. RQI
creates shifted systems that would not be tractable without the MG Krylov
solver. It also creates ill-conditioned matrices. The MGE preconditioner
reduces iteration count significantly when used with RQI and takes advantage of
the new energy decomposition such that it can scale efficiently. Each
individual method has been described before, but this is the first time they
have been demonstrated to work together effectively.
The combination of solvers enables the RQI eigenvalue solver to work better
than the other available solvers for large reactors problems on leadership
class machines. Using these methods together, RQI converged in fewer iterations
and in less time than PI for a full pressurized water reactor core. These
solvers also performed better than an Arnoldi eigenvalue solver for a reactor
benchmark problem when energy decomposition is needed. The MG Krylov, MGE
preconditioner, and RQI solver combination also scales well in energy. This
solver set is a strong choice for very large and challenging problems.
| 1 | 1 | 0 | 0 | 0 | 0 |
1,498 | Thermoregulation in mice, rats and humans: An insight into the evolution of human hairlessness | The thermoregulation system in animals removes body heat in hot temperatures
and retains body heat in cold temperatures. The better the animal removes heat,
the worse the animal retains heat and visa versa. It is the balance between
these two conflicting goals that determines the mammal's size, heart rate and
amount of hair. The rat's loss of tail hair and human's loss of its body hair
are responses to these conflicting thermoregulation needs as these animals
evolved to larger size over time.
| 0 | 0 | 0 | 0 | 1 | 0 |
1,499 | Koszul A-infinity algebras and free loop space homology | We introduce a notion of Koszul A-infinity algebra that generalizes Priddy's
notion of a Koszul algebra and we use it to construct small A-infinity algebra
models for Hochschild cochains. As an application, this yields new techniques
for computing free loop space homology algebras of manifolds that are either
formal or coformal (over a field or over the integers). We illustrate these
techniques in two examples.
| 0 | 0 | 1 | 0 | 0 | 0 |
1,500 | Learning RBM with a DC programming Approach | By exploiting the property that the RBM log-likelihood function is the
difference of convex functions, we formulate a stochastic variant of the
difference of convex functions (DC) programming to minimize the negative
log-likelihood. Interestingly, the traditional contrastive divergence algorithm
is a special case of the above formulation and the hyperparameters of the two
algorithms can be chosen such that the amount of computation per mini-batch is
identical. We show that for a given computational budget the proposed algorithm
almost always reaches a higher log-likelihood more rapidly, compared to the
standard contrastive divergence algorithm. Further, we modify this algorithm to
use the centered gradients and show that it is more efficient and effective
compared to the standard centered gradient algorithm on benchmark datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
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