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Fooling Sets and the Spanning Tree Polytope | In the study of extensions of polytopes of combinatorial optimization
problems, a notorious open question is that for the size of the smallest
extended formulation of the Minimum Spanning Tree problem on a complete graph
with $n$ nodes. The best known lower bound is $\Omega(n^2)$, the best known
upper bound is $O(n^3)$.
In this note we show that the venerable fooling set method cannot be used to
improve the lower bound: every fooling set for the Spanning Tree polytope has
size $O(n^2)$.
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From Azéma supermartingales of finite honest times to optional semimartingales of class-($Σ$) | Given a finite honest time, we derive representations for the additive and
multiplicative decomposition of it's Azéma supermartingale in terms of
optional supermartingales and its running supremum. We then extend the notion
of semimartingales of class-$(\Sigma)$ to optional semimartingales with jumps
in its finite variation part, allowing one to establish formulas similar to the
Madan-Roynette-Yor option pricing formulas for larger class of processes.
Finally, we introduce the optional multiplicative systems associated with
positive submartingales and apply them to construct random times with given
Azéma supermartingale.
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Single versus Double Blind Reviewing at WSDM 2017 | In this paper we study the implications for conference program committees of
using single-blind reviewing, in which committee members are aware of the names
and affiliations of paper authors, versus double-blind reviewing, in which this
information is not visible to committee members. WSDM 2017, the 10th ACM
International ACM Conference on Web Search and Data Mining, performed a
controlled experiment in which each paper was reviewed by four committee
members. Two of these four reviewers were chosen from a pool of committee
members who had access to author information; the other two were chosen from a
disjoint pool who did not have access to this information. This information
asymmetry persisted through the process of bidding for papers, reviewing
papers, and entering scores. Reviewers in the single-blind condition typically
bid for 22% fewer papers, and preferentially bid for papers from top
institutions. Once papers were allocated to reviewers, single-blind reviewers
were significantly more likely than their double-blind counterparts to
recommend for acceptance papers from famous authors and top institutions. The
estimated odds multipliers are 1.63 for famous authors and 1.58 and 2.10 for
top universities and companies respectively, so the result is tangible. For
female authors, the associated odds multiplier of 0.78 is not statistically
significant in our study. However, a meta-analysis places this value in line
with that of other experiments, and in the context of this larger aggregate the
gender effect is also statistically significant.
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Scale-variant Topological Information for Characterizing Complex Networks | Real-world networks are difficult to characterize because of the variation of
topological scales, the non-dyadic complex interactions, and the fluctuations.
Here, we propose a general framework to address these problems via a
methodology grounded on topology data analysis. By observing the diffusion
process in a network at a single specified timescale, we can map the network
nodes to a point cloud, which contains the topological information of the
network at a single scale. We then calculate the point clouds constructed over
variable timescales, which provide scale-variant topological information and
enable a deep understanding of the network structure and functionality.
Experiments on synthetic and real-world data demonstrate the effectiveness of
our framework in identifying network models, classifying real-world networks
and detecting transition points in time-evolving networks. Our work presents a
unified analysis that is potentially applicable to more complicated network
structures such as multilayer and multiplex networks.
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Topological orbital superfluid with chiral d-wave order in a rotating optical lattice | Topological superfluid is an exotic state of quantum matter that possesses a
nodeless superfluid gap in the bulk and Andreev edge modes at the boundary of a
finite system. Here, we study a multi-orbital superfluid driven by attractive
s-wave interaction in a rotating optical lattice. Interestingly, we find that
the rotation induces the inter- orbital hybridization and drives the system
into topological orbital superfluid in accordance with intrinsically chiral
d-wave pairing characteristics. Thanks to the conservation of spin, the
topological orbital superfluid supports four rather than two chiral Andreev
edge modes at the boundary of the lattice. Moreover, we find that the intrinsic
harmonic confining potential forms a circular spatial barrier which accumulates
atoms and supports a mass current under injection of small angular momentum as
external driving force. This feature provides an experimentally detectable
phenomenon to verify the topological orbital superfluid with chiral d-wave
order in a rotating optical lattice.
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Optimal client recommendation for market makers in illiquid financial products | The process of liquidity provision in financial markets can result in
prolonged exposure to illiquid instruments for market makers. In this case,
where a proprietary position is not desired, pro-actively targeting the right
client who is likely to be interested can be an effective means to offset this
position, rather than relying on commensurate interest arising through natural
demand. In this paper, we consider the inference of a client profile for the
purpose of corporate bond recommendation, based on typical recorded information
available to the market maker. Given a historical record of corporate bond
transactions and bond meta-data, we use a topic-modelling analogy to develop a
probabilistic technique for compiling a curated list of client recommendations
for a particular bond that needs to be traded, ranked by probability of
interest. We show that a model based on Latent Dirichlet Allocation offers
promising performance to deliver relevant recommendations for sales traders.
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Design of an Audio Interface for Patmos | This paper describes the design and implementation of an audio interface for
the Patmos processor, which runs on an Altera DE2-115 FPGA board. This board
has an audio codec included, the WM8731. The interface described in this work
allows to receive and send audio from and to the WM8731, and to synthesize,
store or manipulate audio signals writing C programs for Patmos. The audio
interface described in this paper is intended to be used with the Patmos
processor. Patmos is an open source RISC ISAs with a load-store architecture,
that is optimized for Real-Time Systems. Patmos is part of a project founded by
the European Union called T-CREST (Time-predictable Multi-Core Architecture for
Embedded Systems).[5] The structure of this project is integrated with the
Patmos project: new hardware modules have been added as IOs, which allow the
communication between the processor and the audio codec. These modules include
a clock generator for the audio chip, ADC and DAC modules for the audio
conversion from analog to digital and vice versa, and an I2C module which
allows setting configuration parameters on the audio codec. Moreover, a top
module has been created, which connects all the modules previously mentioned
between them, to Patmos and to the WM8731, using the external pins of the FPGA.
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A Classifying Variational Autoencoder with Application to Polyphonic Music Generation | The variational autoencoder (VAE) is a popular probabilistic generative
model. However, one shortcoming of VAEs is that the latent variables cannot be
discrete, which makes it difficult to generate data from different modes of a
distribution. Here, we propose an extension of the VAE framework that
incorporates a classifier to infer the discrete class of the modeled data. To
model sequential data, we can combine our Classifying VAE with a recurrent
neural network such as an LSTM. We apply this model to algorithmic music
generation, where our model learns to generate musical sequences in different
keys. Most previous work in this area avoids modeling key by transposing data
into only one or two keys, as opposed to the 10+ different keys in the original
music. We show that our Classifying VAE and Classifying VAE+LSTM models
outperform the corresponding non-classifying models in generating musical
samples that stay in key. This benefit is especially apparent when trained on
untransposed music data in the original keys.
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On Estimation of Isotonic Piecewise Constant Signals | Consider a sequence of real data points $X_1,\ldots, X_n$ with underlying
means $\theta^*_1,\dots,\theta^*_n$. This paper starts from studying the
setting that $\theta^*_i$ is both piecewise constant and monotone as a function
of the index $i$. For this, we establish the exact minimax rate of estimating
such monotone functions, and thus give a non-trivial answer to an open problem
in the shape-constrained analysis literature. The minimax rate involves an
interesting iterated logarithmic dependence on the dimension, a phenomenon that
is revealed through characterizing the interplay between the isotonic shape
constraint and model selection complexity. We then develop a penalized
least-squares procedure for estimating the vector
$\theta^*=(\theta^*_1,\dots,\theta^*_n)^T$. This estimator is shown to achieve
the derived minimax rate adaptively. For the proposed estimator, we further
allow the model to be misspecified and derive oracle inequalities with the
optimal rates, and show there exists a computationally efficient algorithm to
compute the exact solution.
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Deep Stacked Stochastic Configuration Networks for Non-Stationary Data Streams | The concept of stochastic configuration networks (SCNs) others a solid
framework for fast implementation of feedforward neural networks through
randomized learning. Unlike conventional randomized approaches, SCNs provide an
avenue to select appropriate scope of random parameters to ensure the universal
approximation property. In this paper, a deep version of stochastic
configuration networks, namely deep stacked stochastic configuration network
(DSSCN), is proposed for modeling non-stationary data streams. As an extension
of evolving stochastic connfiguration networks (eSCNs), this work contributes a
way to grow and shrink the structure of deep stochastic configuration networks
autonomously from data streams. The performance of DSSCN is evaluated by six
benchmark datasets. Simulation results, compared with prominent data stream
algorithms, show that the proposed method is capable of achieving comparable
accuracy and evolving compact and parsimonious deep stacked network
architecture.
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Extracting Syntactic Patterns from Databases | Many database columns contain string or numerical data that conforms to a
pattern, such as phone numbers, dates, addresses, product identifiers, and
employee ids. These patterns are useful in a number of data processing
applications, including understanding what a specific field represents when
field names are ambiguous, identifying outlier values, and finding similar
fields across data sets. One way to express such patterns would be to learn
regular expressions for each field in the database. Unfortunately, exist- ing
techniques on regular expression learning are slow, taking hundreds of seconds
for columns of just a few thousand values. In contrast, we develop XSystem, an
efficient method to learn patterns over database columns in significantly less
time. We show that these patterns can not only be built quickly, but are
expressive enough to capture a number of key applications, including detecting
outliers, measuring column similarity, and assigning semantic labels to columns
(based on a library of regular expressions). We evaluate these applications
with datasets that range from chemical databases (based on a collaboration with
a pharmaceutical company), our university data warehouse, and open data from
MassData.gov.
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Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification | We address the problem of multi-class classification in the case where the
number of classes is very large. We propose a double sampling strategy on top
of a multi-class to binary reduction strategy, which transforms the original
multi-class problem into a binary classification problem over pairs of
examples. The aim of the sampling strategy is to overcome the curse of
long-tailed class distributions exhibited in majority of large-scale
multi-class classification problems and to reduce the number of pairs of
examples in the expanded data. We show that this strategy does not alter the
consistency of the empirical risk minimization principle defined over the
double sample reduction. Experiments are carried out on DMOZ and Wikipedia
collections with 10,000 to 100,000 classes where we show the efficiency of the
proposed approach in terms of training and prediction time, memory consumption,
and predictive performance with respect to state-of-the-art approaches.
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Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents | Travel time on a route varies substantially by time of day and from day to
day. It is critical to understand to what extent this variation is correlated
with various factors, such as weather, incidents, events or travel demand level
in the context of dynamic networks. This helps a better decision making for
infrastructure planning and real-time traffic operation. We propose a
data-driven approach to understand and predict highway travel time using
spatio-temporal features of those factors, all of which are acquired from
multiple data sources. The prediction model holistically selects the most
related features from a high-dimensional feature space by correlation analysis,
principle component analysis and LASSO. We test and compare the performance of
several regression models in predicting travel time 30 min in advance via two
case studies: (1) a 6-mile highway corridor of I-270N in D.C. region, and (2) a
2.3-mile corridor of I-376E in Pittsburgh region. We found that some
bottlenecks scattered in the network can imply congestion on those corridors at
least 30 minutes in advance, including those on the alternative route to the
corridors of study. In addition, real-time travel time is statistically related
to incidents on some specific locations, morning/afternoon travel demand,
visibility, precipitation, wind speed/gust and the weather type. All those
spatio-temporal information together help improve prediction accuracy,
comparing to using only speed data. In both case studies, random forest shows
the most promise, reaching a root-mean-squared error of 16.6\% and 17.0\%
respectively in afternoon peak hours for the entire year of 2014.
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The power of sum-of-squares for detecting hidden structures | We study planted problems---finding hidden structures in random noisy
inputs---through the lens of the sum-of-squares semidefinite programming
hierarchy (SoS). This family of powerful semidefinite programs has recently
yielded many new algorithms for planted problems, often achieving the best
known polynomial-time guarantees in terms of accuracy of recovered solutions
and robustness to noise. One theme in recent work is the design of spectral
algorithms which match the guarantees of SoS algorithms for planted problems.
Classical spectral algorithms are often unable to accomplish this: the twist in
these new spectral algorithms is the use of spectral structure of matrices
whose entries are low-degree polynomials of the input variables. We prove that
for a wide class of planted problems, including refuting random constraint
satisfaction problems, tensor and sparse PCA, densest-k-subgraph, community
detection in stochastic block models, planted clique, and others, eigenvalues
of degree-d matrix polynomials are as powerful as SoS semidefinite programs of
roughly degree d. For such problems it is therefore always possible to match
the guarantees of SoS without solving a large semidefinite program. Using
related ideas on SoS algorithms and low-degree matrix polynomials (and inspired
by recent work on SoS and the planted clique problem by Barak et al.), we prove
new nearly-tight SoS lower bounds for the tensor and sparse principal component
analysis problems. Our lower bounds for sparse principal component analysis are
the first to suggest that going beyond existing algorithms for this problem may
require sub-exponential time.
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An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis | Polynomial chaos expansions (PCE) have seen widespread use in the context of
uncertainty quantification. However, their application to structural
reliability problems has been hindered by the limited performance of PCE in the
tails of the model response and due to the lack of local metamodel error
estimates. We propose a new method to provide local metamodel error estimates
based on bootstrap resampling and sparse PCE. An initial experimental design is
iteratively updated based on the current estimation of the limit-state surface
in an active learning algorithm. The greedy algorithm uses the bootstrap-based
local error estimates for the polynomial chaos predictor to identify the best
candidate set of points to enrich the experimental design. We demonstrate the
effectiveness of this approach on a well-known analytical benchmark
representing a series system, on a truss structure and on a complex realistic
frame structure problem.
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Experimental Tests of Spirituality | We currently harness technologies that could shed new light on old
philosophical questions, such as whether our mind entails anything beyond our
body or whether our moral values reflect universal truth.
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Chern classes and Gromov--Witten theory of projective bundles | We prove that the Gromov--Witten theory (GWT) of a projective bundle can be
determined by the Chern classes and the GWT of the base. It completely answers
a question raised in a previous paper (arXiv:1607.00740). Its consequences
include that the GWT of the blow-up of X at a smooth subvariety Z is uniquely
determined by GWT of X, Z plus some topological data.
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Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations | We introduce the concept of numerical Gaussian processes, which we define as
Gaussian processes with covariance functions resulting from temporal
discretization of time-dependent partial differential equations. Numerical
Gaussian processes, by construction, are designed to deal with cases where: (1)
all we observe are noisy data on black-box initial conditions, and (2) we are
interested in quantifying the uncertainty associated with such noisy data in
our solutions to time-dependent partial differential equations. Our method
circumvents the need for spatial discretization of the differential operators
by proper placement of Gaussian process priors. This is an attempt to construct
structured and data-efficient learning machines, which are explicitly informed
by the underlying physics that possibly generated the observed data. The
effectiveness of the proposed approach is demonstrated through several
benchmark problems involving linear and nonlinear time-dependent operators. In
all examples, we are able to recover accurate approximations of the latent
solutions, and consistently propagate uncertainty, even in cases involving very
long time integration.
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Learning from Experience: A Dynamic Closed-Loop QoE Optimization for Video Adaptation and Delivery | The quality of experience (QoE) is known to be subjective and
context-dependent. Identifying and calculating the factors that affect QoE is
indeed a difficult task. Recently, a lot of effort has been devoted to estimate
the users QoE in order to improve video delivery. In the literature, most of
the QoE-driven optimization schemes that realize trade-offs among different
quality metrics have been addressed under the assumption of homogenous
populations. Nevertheless, people perceptions on a given video quality may not
be the same, which makes the QoE optimization harder. This paper aims at taking
a step further in order to address this limitation and meet users profiles. To
do so, we propose a closed-loop control framework based on the
users(subjective) feedbacks to learn the QoE function and optimize it at the
same time. Our simulation results show that our system converges to a steady
state, where the resulting QoE function noticeably improves the users
feedbacks.
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Deep CNN based feature extractor for text-prompted speaker recognition | Deep learning is still not a very common tool in speaker verification field.
We study deep convolutional neural network performance in the text-prompted
speaker verification task. The prompted passphrase is segmented into word
states - i.e. digits -to test each digit utterance separately. We train a
single high-level feature extractor for all states and use cosine similarity
metric for scoring. The key feature of our network is the Max-Feature-Map
activation function, which acts as an embedded feature selector. By using
multitask learning scheme to train the high-level feature extractor we were
able to surpass the classic baseline systems in terms of quality and achieved
impressive results for such a novice approach, getting 2.85% EER on the RSR2015
evaluation set. Fusion of the proposed and the baseline systems improves this
result.
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Data-Injection Attacks in Stochastic Control Systems: Detectability and Performance Tradeoffs | Consider a stochastic process being controlled across a communication
channel. The control signal that is transmitted across the control channel can
be replaced by a malicious attacker. The controller is allowed to implement any
arbitrary detection algorithm to detect if an attacker is present. This work
characterizes some fundamental limitations of when such an attack can be
detected, and quantifies the performance degradation that an attacker that
seeks to be undetected or stealthy can introduce.
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Efficient Version-Space Reduction for Visual Tracking | Discrminative trackers, employ a classification approach to separate the
target from its background. To cope with variations of the target shape and
appearance, the classifier is updated online with different samples of the
target and the background. Sample selection, labeling and updating the
classifier is prone to various sources of errors that drift the tracker. We
introduce the use of an efficient version space shrinking strategy to reduce
the labeling errors and enhance its sampling strategy by measuring the
uncertainty of the tracker about the samples. The proposed tracker, utilize an
ensemble of classifiers that represents different hypotheses about the target,
diversify them using boosting to provide a larger and more consistent coverage
of the version-space and tune the classifiers' weights in voting. The proposed
system adjusts the model update rate by promoting the co-training of the
short-memory ensemble with a long-memory oracle. The proposed tracker
outperformed state-of-the-art trackers on different sequences bearing various
tracking challenges.
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Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts | Most of the JavaScript code deployed in the wild has been minified, a process
in which identifier names are replaced with short, arbitrary and meaningless
names. Minified code occupies less space, but also makes the code extremely
difficult to manually inspect and understand. This paper presents Context2Name,
a deep learningbased technique that partially reverses the effect of
minification by predicting natural identifier names for minified names. The
core idea is to predict from the usage context of a variable a name that
captures the meaning of the variable. The approach combines a lightweight,
token-based static analysis with an auto-encoder neural network that summarizes
usage contexts and a recurrent neural network that predict natural names for a
given usage context. We evaluate Context2Name with a large corpus of real-world
JavaScript code and show that it successfully predicts 47.5% of all minified
identifiers while taking only 2.9 milliseconds on average to predict a name. A
comparison with the state-of-the-art tools JSNice and JSNaughty shows that our
approach performs comparably in terms of accuracy while improving in terms of
efficiency. Moreover, Context2Name complements the state-of-the-art by
predicting 5.3% additional identifiers that are missed by both existing tools.
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Reconstructing fluid dynamics with micro-finite element | In the theory of the Navier-Stokes equations, the viscous fluid in
incompressible flow is modelled as a homogeneous and dense assemblage of
constituent "fluid particles" with viscous stress proportional to rate of
strain. The crucial concept of fluid flow is the velocity of the particle that
is accelerated by the pressure and viscous interaction around it. In this
paper, by virtue of the alternative constituent "micro-finite element", we
introduce a set of new intrinsic quantities, called the vortex fields, to
characterise the relative orientation between elements and the feature of
micro-eddies in the element, while the description of viscous interaction in
fluid returns to the initial intuition that the interlayer friction is
proportional to the slip strength. Such a framework enables us to reconstruct
the dynamics theory of viscous fluid, in which the flowing fluid can be
modelled as a finite covering of elements and consequently indicated by a
space-time differential manifold that admits complex topological evolution.
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Towards Black-box Iterative Machine Teaching | In this paper, we make an important step towards the black-box machine
teaching by considering the cross-space machine teaching, where the teacher and
the learner use different feature representations and the teacher can not fully
observe the learner's model. In such scenario, we study how the teacher is
still able to teach the learner to achieve faster convergence rate than the
traditional passive learning. We propose an active teacher model that can
actively query the learner (i.e., make the learner take exams) for estimating
the learner's status and provably guide the learner to achieve faster
convergence. The sample complexities for both teaching and query are provided.
In the experiments, we compare the proposed active teacher with the omniscient
teacher and verify the effectiveness of the active teacher model.
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On Generalization and Regularization in Deep Learning | Why do large neural network generalize so well on complex tasks such as image
classification or speech recognition? What exactly is the role regularization
for them? These are arguably among the most important open questions in machine
learning today. In a recent and thought provoking paper [C. Zhang et al.]
several authors performed a number of numerical experiments that hint at the
need for novel theoretical concepts to account for this phenomenon. The paper
stirred quit a lot of excitement among the machine learning community but at
the same time it created some confusion as discussions on OpenReview.net
testifies. The aim of this pedagogical paper is to make this debate accessible
to a wider audience of data scientists without advanced theoretical knowledge
in statistical learning. The focus here is on explicit mathematical definitions
and on a discussion of relevant concepts, not on proofs for which we provide
references.
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Nonparametric Inference for Auto-Encoding Variational Bayes | We would like to learn latent representations that are low-dimensional and
highly interpretable. A model that has these characteristics is the Gaussian
Process Latent Variable Model. The benefits and negative of the GP-LVM are
complementary to the Variational Autoencoder, the former provides interpretable
low-dimensional latent representations while the latter is able to handle large
amounts of data and can use non-Gaussian likelihoods. Our inspiration for this
paper is to marry these two approaches and reap the benefits of both. In order
to do so we will introduce a novel approximate inference scheme inspired by the
GP-LVM and the VAE. We show experimentally that the approximation allows the
capacity of the generative bottle-neck (Z) of the VAE to be arbitrarily large
without losing a highly interpretable representation, allowing reconstruction
quality to be unlimited by Z at the same time as a low-dimensional space can be
used to perform ancestral sampling from as well as a means to reason about the
embedded data.
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Imbalanced Malware Images Classification: a CNN based Approach | Deep convolutional neural networks (CNNs) can be applied to malware binary
detection through images classification. The performance, however, is degraded
due to the imbalance of malware families (classes). To mitigate this issue, we
propose a simple yet effective weighted softmax loss which can be employed as
the final layer of deep CNNs. The original softmax loss is weighted, and the
weight value can be determined according to class size. A scaling parameter is
also included in computing the weight. Proper selection of this parameter has
been studied and an empirical option is given. The weighted loss aims at
alleviating the impact of data imbalance in an end-to-end learning fashion. To
validate the efficacy, we deploy the proposed weighted loss in a pre-trained
deep CNN model and fine-tune it to achieve promising results on malware images
classification. Extensive experiments also indicate that the new loss function
can fit other typical CNNs with an improved classification performance.
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Integrated optical force sensors using focusing photonic crystal arrays | Mechanical oscillators are at the heart of many sensor applications. Recently
several groups have developed oscillators that are probed optically, fabricated
from high-stress silicon nitride films. They exhibit outstanding force
sensitivities of a few aN/Hz$^{1/2}$ and can also be made highly reflective,
for efficient detection. The optical read-out usually requires complex
experimental setups, including positioning stages and bulky cavities, making
them impractical for real applications. In this paper we propose a novel way of
building fully integrated all-optical force sensors based on low-loss silicon
nitride mechanical resonators with a photonic crystal reflector. We can
circumvent previous limitations in stability and complexity by simulating a
suspended focusing photonic crystal, purely made of silicon nitride. Our design
allows for an all integrated sensor, built out of a single block that
integrates a full Fabry-Pérot cavity, without the need for assembly or
alignment. The presented simulations will allow for a radical simplification of
sensors based on high-Q silicon nitride membranes. Our results comprise, to the
best of our knowledge, the first simulations of a focusing mirror made from a
mechanically suspended flat membrane with subwavelength thickness. Cavity
lengths between a few hundred $\mu$m and mm should be directly realizable.
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Comparison of Signaling and Media Approaches to Detect VoIP SPIT Attack | IP networks became the most dominant type of information networks nowadays.
It provides a number of services and makes it easy for users to be connected.
IP networks provide an efficient way with a large number of services compared
to other ways of voice communication. This leads to the migration to make voice
calls via IP networks. Despite the wide range of IP networks services,
availability, and its capabilities, there still a large number of security
threats that affect IP networks and for sure affecting other services based on
it and voice is one of them. This paper discusses reasons of migration from
making voice calls via IP networks and leaving legacy networks, requirements to
be available in IP networks to support voice transport, and concentrating on
SPIT attack and its detection methods. Experiments took place to compare the
different approaches used to detect spam over VoIP networks.
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Coupling functions: Universal insights into dynamical interaction mechanisms | The dynamical systems found in Nature are rarely isolated. Instead they
interact and influence each other. The coupling functions that connect them
contain detailed information about the functional mechanisms underlying the
interactions and prescribe the physical rule specifying how an interaction
occurs. Here, we aim to present a coherent and comprehensive review
encompassing the rapid progress made recently in the analysis, understanding
and applications of coupling functions. The basic concepts and characteristics
of coupling functions are presented through demonstrative examples of different
domains, revealing the mechanisms and emphasizing their multivariate nature.
The theory of coupling functions is discussed through gradually increasing
complexity from strong and weak interactions to globally-coupled systems and
networks. A variety of methods that have been developed for the detection and
reconstruction of coupling functions from measured data is described. These
methods are based on different statistical techniques for dynamical inference.
Stemming from physics, such methods are being applied in diverse areas of
science and technology, including chemistry, biology, physiology, neuroscience,
social sciences, mechanics and secure communications. This breadth of
application illustrates the universality of coupling functions for studying the
interaction mechanisms of coupled dynamical systems.
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Uncertainty quantification for radio interferometric imaging: II. MAP estimation | Uncertainty quantification is a critical missing component in radio
interferometric imaging that will only become increasingly important as the
big-data era of radio interferometry emerges. Statistical sampling approaches
to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling,
can in principle recover the full posterior distribution of the image, from
which uncertainties can then be quantified. However, for massive data sizes,
like those anticipated from the Square Kilometre Array (SKA), it will be
difficult if not impossible to apply any MCMC technique due to its inherent
computational cost. We formulate Bayesian inference problems with
sparsity-promoting priors (motivated by compressive sensing), for which we
recover maximum a posteriori (MAP) point estimators of radio interferometric
images by convex optimisation. Exploiting recent developments in the theory of
probability concentration, we quantify uncertainties by post-processing the
recovered MAP estimate. Three strategies to quantify uncertainties are
developed: (i) highest posterior density credible regions; (ii) local credible
intervals (cf. error bars) for individual pixels and superpixels; and (iii)
hypothesis testing of image structure. These forms of uncertainty
quantification provide rich information for analysing radio interferometric
observations in a statistically robust manner. Our MAP-based methods are
approximately $10^5$ times faster computationally than state-of-the-art MCMC
methods and, in addition, support highly distributed and parallelised
algorithmic structures. For the first time, our MAP-based techniques provide a
means of quantifying uncertainties for radio interferometric imaging for
realistic data volumes and practical use, and scale to the emerging big-data
era of radio astronomy.
| 0 | 1 | 0 | 1 | 0 | 0 |
Semialgebraic Invariant Synthesis for the Kannan-Lipton Orbit Problem | The \emph{Orbit Problem} consists of determining, given a linear
transformation $A$ on $\mathbb{Q}^d$, together with vectors $x$ and $y$,
whether the orbit of $x$ under repeated applications of $A$ can ever reach $y$.
This problem was famously shown to be decidable by Kannan and Lipton in the
1980s.
In this paper, we are concerned with the problem of synthesising suitable
\emph{invariants} $\mathcal{P} \subseteq \mathbb{R}^d$, \emph{i.e.}, sets that
are stable under $A$ and contain $x$ and not $y$, thereby providing compact and
versatile certificates of non-reachability. We show that whether a given
instance of the Orbit Problem admits a semialgebraic invariant is decidable,
and moreover in positive instances we provide an algorithm to synthesise
suitable invariants of polynomial size.
It is worth noting that the existence of \emph{semilinear} invariants, on the
other hand, is (to the best of our knowledge) not known to be decidable.
| 1 | 0 | 1 | 0 | 0 | 0 |
Design and characterization of the Large-Aperture Experiment to Detect the Dark Age (LEDA) radiometer systems | The Large-Aperture Experiment to Detect the Dark Age (LEDA) was designed to
detect the predicted O(100)mK sky-averaged absorption of the Cosmic Microwave
Background by Hydrogen in the neutral pre- and intergalactic medium just after
the cosmological Dark Age. The spectral signature would be associated with
emergence of a diffuse Ly$\alpha$ background from starlight during 'Cosmic
Dawn'. Recently, Bowman et al. (2018) have reported detection of this predicted
absorption feature, with an unexpectedly large amplitude of 530 mK, centered at
78 MHz. Verification of this result by an independent experiment, such as LEDA,
is pressing. In this paper, we detail design and characterization of the LEDA
radiometer systems, and a first-generation pipeline that instantiates a signal
path model. Sited at the Owens Valley Radio Observatory Long Wavelength Array,
LEDA systems include the station correlator, five well-separated redundant dual
polarization radiometers and backend electronics. The radiometers deliver a
30-85MHz band (16<z<34) and operate as part of the larger interferometric
array, for purposes ultimately of in situ calibration. Here, we report on the
LEDA system design, calibration approach, and progress in characterization as
of January 2016. The LEDA systems are currently being modified to improve
performance near 78 MHz in order to verify the purported absorption feature.
| 0 | 1 | 0 | 0 | 0 | 0 |
Automatic Bayesian Density Analysis | Making sense of a dataset in an automatic and unsupervised fashion is a
challenging problem in statistics and AI. Classical approaches for density
estimation are usually not flexible enough to deal with the uncertainty
inherent to real-world data: they are often restricted to fixed latent
interaction models and homogeneous likelihoods; they are sensitive to missing,
corrupt and anomalous data; moreover, their expressiveness generally comes at
the price of intractable inference. As a result, supervision from statisticians
is usually needed to find the right model for the data. However, as domain
experts do not necessarily have to be experts in statistics, we propose
Automatic Bayesian Density Analysis (ABDA) to make density estimation
accessible at large. ABDA automates the selection of adequate likelihood models
from arbitrarily rich dictionaries while modeling their interactions via a deep
latent structure adaptively learned from data as a sum-product network. ABDA
casts uncertainty estimation at these local and global levels into a joint
Bayesian inference problem, providing robust and yet tractable inference.
Extensive empirical evidence shows that ABDA is a suitable tool for automatic
exploratory analysis of heterogeneous tabular data, allowing for missing value
estimation, statistical data type and likelihood discovery, anomaly detection
and dependency structure mining, on top of providing accurate density
estimation.
| 0 | 0 | 0 | 1 | 0 | 0 |
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service | With the widespread use of machine learning (ML) techniques, ML as a service
has become increasingly popular. In this setting, an ML model resides on a
server and users can query the model with their data via an API. However, if
the user's input is sensitive, sending it to the server is not an option.
Equally, the service provider does not want to share the model by sending it to
the client for protecting its intellectual property and pay-per-query business
model. In this paper, we propose MLCapsule, a guarded offline deployment of
machine learning as a service. MLCapsule executes the machine learning model
locally on the user's client and therefore the data never leaves the client.
Meanwhile, MLCapsule offers the service provider the same level of control and
security of its model as the commonly used server-side execution. In addition,
MLCapsule is applicable to offline applications that require local execution.
Beyond protecting against direct model access, we demonstrate that MLCapsule
allows for implementing defenses against advanced attacks on machine learning
models such as model stealing/reverse engineering and membership inference.
| 0 | 0 | 0 | 1 | 0 | 0 |
Brownian forgery of statistical dependences | The balance held by Brownian motion between temporal regularity and
randomness is embodied in a remarkable way by Levy's forgery of continuous
functions. Here we describe how this property can be extended to forge
arbitrary dependences between two statistical systems, and then establish a new
Brownian independence test based on fluctuating random paths. We also argue
that this result allows revisiting the theory of Brownian covariance from a
physical perspective and opens the possibility of engineering nonlinear
correlation measures from more general functional integrals.
| 0 | 0 | 1 | 1 | 0 | 0 |
Internal sizes in $μ$-abstract elementary classes | Working in the context of $\mu$-abstract elementary classes ($\mu$-AECs) -
or, equivalently, accessible categories with all morphisms monomorphisms - we
examine the two natural notions of size that occur, namely cardinality of
underlying sets and internal size. The latter, purely category-theoretic,
notion generalizes e.g. density character in complete metric spaces and
cardinality of orthogonal bases in Hilbert spaces. We consider the relationship
between these notions under mild set-theoretic hypotheses, including weakenings
of the singular cardinal hypothesis. We also establish preliminary results on
the existence and categoricity spectra of $\mu$-AECs, including specific
examples showing dramatic failures of the eventual categoricity conjecture
(with categoricity defined using cardinality) in $\mu$-AECs.
| 0 | 0 | 1 | 0 | 0 | 0 |
Seasonal forecasts of the summer 2016 Yangtze River basin rainfall | The Yangtze River has been subject to heavy flooding throughout history, and
in recent times severe floods such as those in 1998 have resulted in heavy loss
of life and livelihoods. Dams along the river help to manage flood waters, and
are important sources of electricity for the region. Being able to forecast
high-impact events at long lead times therefore has enormous potential benefit.
Recent improvements in seasonal forecasting mean that dynamical climate models
can start to be used directly for operational services. The teleconnection from
El Niño to Yangtze River basin rainfall meant that the strong El Niño in
winter 2015/2016 provided a valuable opportunity to test the application of a
dynamical forecast system.
This paper therefore presents a case study of a real time seasonal forecast
for the Yangtze River basin, building on previous work demonstrating the
retrospective skill of such a forecast. A simple forecasting methodology is
presented, in which the forecast probabilities are derived from the historical
relationship between hindcast and observations. Its performance for 2016 is
discussed. The heavy rainfall in the May-June-July period was correctly
forecast well in advance. August saw anomalously low rainfall, and the
forecasts for the June-July-August period correctly showed closer to average
levels. The forecasts contributed to the confidence of decision-makers across
the Yangtze River basin. Trials of climate services such as this help to
promote appropriate use of seasonal forecasts, and highlight areas for future
improvements.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modeling Interference Via Symmetric Treatment Decomposition | Classical causal inference assumes a treatment meant for a given unit does
not have an effect on other units. When this "no interference" assumption is
violated, new types of spillover causal effects arise, and causal inference
becomes much more difficult. In addition, interference introduces a unique
complication where outcomes may transmit treatment influences to each other,
which is a relationship that has some features of a causal one, but is
symmetric. In settings where detailed temporal information on outcomes is not
available, addressing this complication using statistical inference methods
based on Directed Acyclic Graphs (DAGs) (Ogburn & VanderWeele, 2014) leads to
conceptual difficulties.
In this paper, we develop a new approach to decomposing the spillover effect
into direct (also known as the contagion effect) and indirect (also known as
the infectiousness effect) components that extends the DAG based treatment
decomposition approach to mediation found in (Robins & Richardson, 2010) to
causal chain graph models (Lauritzen & Richardson, 2002). We show that when
these components of the spillover effect are identified in these models, they
have an identifying functional, which we call the symmetric mediation formula,
that generalizes the mediation formula in DAGs (Pearl, 2011). We further show
that, unlike assumptions in classical mediation analysis, an assumption
permitting identification in our setting leads to restrictions on the observed
data law, making the assumption empirically falsifiable. Finally, we discuss
statistical inference for the components of the spillover effect in the special
case of two interacting outcomes, and discuss a maximum likelihood estimator,
and a doubly robust estimator.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Bayesian algorithm for distributed network localization using distance and direction data | A reliable, accurate, and affordable positioning service is highly required
in wireless networks. In this paper, the novel Message Passing Hybrid
Localization (MPHL) algorithm is proposed to solve the problem of cooperative
distributed localization using distance and direction estimates. This hybrid
approach combines two sensing modalities to reduce the uncertainty in
localizing the network nodes. A statistical model is formulated for the
problem, and approximate minimum mean square error (MMSE) estimates of the node
locations are computed. The proposed MPHL is a distributed algorithm based on
belief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. It
improves the identifiability of the localization problem and reduces its
sensitivity to the anchor node geometry, compared to distance-only or
direction-only localization techniques. For example, the unknown location of a
node can be found if it has only a single neighbor; and a whole network can be
localized using only a single anchor node. Numerical results are presented
showing that the average localization error is significantly reduced in almost
every simulation scenario, about 50% in most cases, compared to the competing
algorithms.
| 1 | 0 | 0 | 1 | 0 | 0 |
Bounds for multivariate residues and for the polynomials in the elimination theorem | We present several upper bounds for the height of global residues of rational
forms on an affine variety. As a consequence, we deduce upper bounds for the
height of the coefficients in the Bergman-Weil trace formula.
We also present upper bounds for the degree and the height of the polynomials
in the elimination theorem on an affine variety. This is an arithmetic analogue
of Jelonek's effective elimination theorem, that plays a crucial role in the
proof of our bounds for the height of global residues.
| 0 | 0 | 1 | 0 | 0 | 0 |
An adsorbed gas estimation model for shale gas reservoirs via statistical learning | Shale gas plays an important role in reducing pollution and adjusting the
structure of world energy. Gas content estimation is particularly significant
in shale gas resource evaluation. There exist various estimation methods, such
as first principle methods and empirical models. However, resource evaluation
presents many challenges, especially the insufficient accuracy of existing
models and the high cost resulting from time-consuming adsorption experiments.
In this research, a low-cost and high-accuracy model based on geological
parameters is constructed through statistical learning methods to estimate
adsorbed shale gas content
| 0 | 1 | 0 | 1 | 0 | 0 |
A Semantic Loss Function for Deep Learning with Symbolic Knowledge | This paper develops a novel methodology for using symbolic knowledge in deep
learning. From first principles, we derive a semantic loss function that
bridges between neural output vectors and logical constraints. This loss
function captures how close the neural network is to satisfying the constraints
on its output. An experimental evaluation shows that it effectively guides the
learner to achieve (near-)state-of-the-art results on semi-supervised
multi-class classification. Moreover, it significantly increases the ability of
the neural network to predict structured objects, such as rankings and paths.
These discrete concepts are tremendously difficult to learn, and benefit from a
tight integration of deep learning and symbolic reasoning methods.
| 1 | 0 | 0 | 1 | 0 | 0 |
Chaotic properties of a turbulent isotropic fluid | By tracking the divergence of two initially close trajectories in phase space
in an Eulerian approach to forced turbulence, the relation between the maximal
Lyapunov exponent $\lambda$, and the Reynolds number $Re$ is measured using
direct numerical simulations, performed on up to $2048^3$ collocation points.
The Lyapunov exponent is found to solely depend on the Reynolds number with
$\lambda \propto Re^{0.53}$ and that after a transient period the divergence of
trajectories grows at the same rate at all scales. Finally a linear divergence
is seen that is dependent on the energy forcing rate. Links are made with other
chaotic systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
White light emission from silicon nanoparticles | As one of the most important semiconductors, silicon (Si) has been used to
fabricate electronic devices, waveguides, detectors, and solar cells etc.
However, its indirect bandgap hinders the use of Si for making good emitters1.
For integrated photonic circuits, Si-based emitters with sizes in the range of
100-300 nm are highly desirable. Here, we show that efficient white light
emission can be realized in spherical and cylindrical Si nanoparticles with
feature sizes of ~200 nm. The up-converted luminescence appears at the magnetic
and electric multipole resonances when the nanoparticles are resonantly excited
at their magnetic and electric dipole resonances by using femtosecond (fs)
laser pulses with ultralow low energy of ~40 pJ. The lifetime of the white
light is as short as ~52 ps, almost three orders of magnitude smaller than the
state-of-the-art results reported so far for Si (~10 ns). Our finding paves the
way for realizing efficient Si-based emitters compatible with current
semiconductor fabrication technology, which can be integrated to photonic
circuits.
| 0 | 1 | 0 | 0 | 0 | 0 |
The collapse of ecosystem engineer populations | Humans are the ultimate ecosystem engineers who have profoundly transformed
the world's landscapes in order to enhance their survival. Somewhat
paradoxically, however, sometimes the unforeseen effect of this ecosystem
engineering is the very collapse of the population it intended to protect. Here
we use a spatial version of a standard population dynamics model of ecosystem
engineers to study the colonization of unexplored virgin territories by a small
settlement of engineers. We find that during the expansion phase the population
density reaches values much higher than those the environment can support in
the equilibrium situation. When the colonization front reaches the boundary of
the available space, the population density plunges sharply and attains its
equilibrium value. The collapse takes place without warning and happens just
after the population reaches its peak number. We conclude that overpopulation
and the consequent collapse of an expanding population of ecosystem engineers
is a natural consequence of the nonlinear feedback between the population and
environment variables.
| 0 | 1 | 0 | 0 | 0 | 0 |
Highly Viscous Microjet Generator | This paper describes a simple yet novel system for generating a highly
viscous microjet. The jet is produced inside a wettable thin tube partially
submerged in a liquid. The gas-liquid interface inside the tube, which is
initially concave, is kept much deeper than that outside the tube. An impulsive
force applied at the bottom of a liquid container leads to significant
acceleration of the liquid inside the tube followed by flow-focusing due to the
concave interface. The jet generation process can be divided into two parts
that occur in different time scales, i.e. the Impact time (impact duration $\le
O(10^{-4})$ s) and Focusing time (focusing duration $\gg O(10^{-4})$ s). In
Impact time, the liquid accelerates suddenly due to the impact. In Focusing
time, the microjet emerges due to flow-focusing. In order to explain the sudden
acceleration inside the tube in Impact time, we develop a physical model based
on a pressure impulse approach. Numerical simulations confirm the proposed
model, indicating that the basic mechanism of the acceleration of the liquid
due to the impulsive force is elucidated. Remarkably, the viscous effect is
negligible in Impact time. In contrast, in Focusing time, the viscosity plays
an important role in the microjet generation. We experimentally and numerically
investigate the velocity of microjets with various viscosities. We find that
higher viscosities lead to reduction of the jet velocity, which can be
described by using Reynolds number (the ratio between the inertia force and the
viscous force). This novel device may be a starting point for next-generation
technologies, such as high-viscosity inkjet printers including bioprinters and
needle-free injection devices for minimally invasive medical treatments.
| 0 | 1 | 0 | 0 | 0 | 0 |
Section problems for configuration spaces of surfaces | In this paper we give a close-to-sharp answer to the basic questions: When is
there a continuous way to add a point to a configuration of $n$ ordered points
on a surface $S$ of finite type so that all the points are still distinct? When
this is possible, what are all the ways to do it? More precisely, let
PConf$_n(S)$ be the space of ordered $n$-tuples of distinct points in $S$. Let
$f_n(S): \text{PConf}_{n+1}(S) \to \text{PConf}_n(S)$ be the map given by
$f_n(x_0,x_1,\ldots,x_n):=(x_1,\ldots,x_n)$. We will classify all continuous
sections of $f_n$ by proving:
1. If $S=\mathbb{R}^2$ and $n>3$, any section of $f_{n}(S)$ is either "adding
a point at infinity" or "adding a point near $x_k$". (We define these two terms
in Section 2.1, whether we can define "adding a point near $x_k$" or "adding a
point at infinity" depends in a delicate way on properties of $S$.)
2. If $S=S^2$ a $2$-sphere and $n>4$, any section of $f_{n}(S)$ is "adding a
point near $x_k$", if $S=S^2$ and $n=2$, the bundle $f_n(S)$ does not have a
section. (We define this term in Section 3.2)
3. If $S=S_g$ a surface of genus $g>1$ and for $n>1$, the bundle $f_{n}(S)$
does not have a section.
| 0 | 0 | 1 | 0 | 0 | 0 |
Odd-triplet superconductivity in single-level quantum dots | We study the interplay of spin and charge coherence in a single-level quantum
dot. A tunnel coupling to a superconducting lead induces superconducting
correlations in the dot. With full spin symmetry retained, only even-singlet
superconducting correlations are generated. An applied magnetic field or
attached ferromagnetic leads partially or fully reduce the spin symmetry, and
odd-triplet superconducting correlations are generated as well. For
single-level quantum dots, no other superconducting correlations are possible.
We analyze, with the help of a diagrammatic real-time technique, the interplay
of spin symmetry and superconductivity and its signatures in electronic
transport, in particular current and shot noise.
| 0 | 1 | 0 | 0 | 0 | 0 |
From Neuronal Models to Neuronal Dynamics and Image Processing | This paper is an introduction to the membrane potential equation for neurons.
Its properties are described, as well as sample applications. Networks of these
equations can be used for modeling neuronal systems, which also process images
and video sequences, respectively. Specifically, (i) a dynamic retina is
proposed (based on a reaction-diffusion system), which predicts afterimages and
simple visual illusions, (ii) a system for texture segregation (texture
elements are understood as even-symmetric contrast features), and (iii) a
network for detecting object approaches (inspired by the locust visual system).
| 0 | 0 | 0 | 0 | 1 | 0 |
A compilation of LEGO Technic parts to support learning experiments on linkages | We present a compilation of LEGO Technic parts to provide easy-to-build
constructions of basic planar linkages. Some technical issues and their
possible solutions are discussed. To solve questions on fine details---like
deciding whether the motion is an exactly straight line or not---we refer to
the dynamic mathematics software tool GeoGebra.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the Casas-Alvero conjecture | The conjecture is formulated in an affine structure and linked with
dimension=1 of the defined CA sets. Then some known results are proved in this
context. The short intended proof relies on a direct yet unclear statement
about homogeneous dependence of algebraic equations. This might not be a
complete proof or even one on the right track, but it may provoke more thoughts
in this respect as expected.
| 0 | 0 | 1 | 0 | 0 | 0 |
Effect of compressibility and aspect ratio on performance of long elastic seals | Recent experiments show no statistical impact of seal length on the
performance of long elastomeric seals in relatively smooth test fixtures.
Motivated by these results, we analytically and computationally investigate the
combined effects of seal length and compressibility on the maximum differential
pressure a seal can support. We present a Saint-Venant type analytic shear lag
solution for slightly compressible seals with large aspect ratios, which
compares well with nonlinear finite element simulations in regions far from the
ends of the seal. However, at the high- and low-pressure ends, where fracture
is observed experimentally, the analytic solution is in poor agreement with
detailed finite element calculations. Nevertheless, we show that the analytic
solution provides far-field stress measures that correlate, over a range of
aspect ratios and bulk moduli, the calculated energy release rates for the
growth of small cracks at the two ends of the seal. Thus a single finite
element simulation coupled with the analytic solution can be used to determine
tendencies for fracture at the two ends of the seal over a wide range of
geometry and compressibility. Finally, using a hypothetical critical energy
release rate, predictions for whether a crack on the high-pressure end will
begin to grow before or after a crack on the low-pressure end begins to grow
are made using the analytic solution and compared with finite element
simulations for finite deformation, hyperelastic seals.
| 0 | 1 | 0 | 0 | 0 | 0 |
Integral models of reductive groups and integral Mumford-Tate groups | Let $G$ be a reductive algebraic group over a $p$-adic field or number field
$K$, and let $V$ be a $K$-linear faithful representation of $G$. A lattice
$\Lambda$ in the vector space $V$ defines a model $\hat{G}_{\Lambda}$ of $G$
over $\mathscr{O}_K$. One may wonder to what extent $\Lambda$ is determined by
the group scheme $\hat{G}_{\Lambda}$. In this paper we prove that up to a
natural equivalence relation on the set of lattices there are only finitely
many $\Lambda$ corresponding to one model $\hat{G}_{\Lambda}$. Furthermore, we
relate this fact to moduli spaces of abelian varieties as follows: let
$\mathscr{A}_{g,n}$ be the moduli space of principally polarised abelian
varieties of dimension $g$ with level $n$ structure. We prove that there are at
most finitely many special subvarieties of $\mathscr{A}_{g,n}$ with a given
integral generic Mumford-Tate group.
| 0 | 0 | 1 | 0 | 0 | 0 |
A proof of Boca's Theorem | We give a general method of extending unital completely positive maps to
amalgamated free products of C*-algebras. As an application we give a dilation
theoretic proof of Boca's Theorem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations | Convolution with Green's function of a differential operator appears in a lot
of applications e.g. Lippmann-Schwinger integral equation. Algorithms for
computing such are usually non-trivial and require non-uniform mesh. However,
recently Vico, Greengard and Ferrando developed method for computing
convolution with smooth functions with compact support with spectral accuracy,
requiring nothing more than Fast Fourier Transform (FFT). Their approach is
very suitable for the low-rank tensor implementation which we develop using
Quantized Tensor Train (QTT) decomposition.
| 1 | 0 | 0 | 0 | 0 | 0 |
Mott insulators of hardcore bosons in 1D: many-body orders, entanglement, edge modes | Many-body phenomena were always an integral part of physics comprising of
collective behaviors through self-organization, in systems consisting of many
components and degrees of freedom. We investigate the collective behaviors of
strongly interacting particles confined in one dimension. We show that
many-body orders with topological characteristics can be found at the Mott
insulator limit for hardcore bosons, at different fillings, without considering
the spin degree of freedom or long-range microscopic interactions. These orders
have unique properties like weak or strong quantum correlations (entanglement),
quantified by the entanglement entropy, edge excitations/modes and gapped
energy spectrum with highly degenerate ground state, bearing resemblance to
topologically ordered phases of matter.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects | When performing Bayesian data analysis using a general linear mixed model,
the resulting posterior density is almost always analytically intractable.
However, if proper conditionally conjugate priors are used, there is a simple
two-block Gibbs sampler that is geometrically ergodic in nearly all practical
settings, including situations where $p > n$ (Abrahamsen and Hobert, 2017).
Unfortunately, the (conditionally conjugate) multivariate normal prior on
$\beta$ does not perform well in the high-dimensional setting where $p \gg n$.
In this paper, we consider an alternative model in which the multivariate
normal prior is replaced by the normal-gamma shrinkage prior developed by
Griffin and Brown (2010). This change leads to a much more complex posterior
density, and we develop a simple MCMC algorithm for exploring it. This
algorithm, which has both deterministic and random scan components, is easier
to analyze than the more obvious three-step Gibbs sampler. Indeed, we prove
that the new algorithm is geometrically ergodic in most practical settings.
| 0 | 0 | 1 | 1 | 0 | 0 |
Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction | With the increasing of electric vehicle (EV) adoption in recent years, the
impact of EV charging activities to the power grid becomes more and more
significant. In this article, an optimal scheduling algorithm which combines
smart EV charging and V2G gird service is developed to integrate EVs into power
grid as distributed energy resources, with improved system cost performance.
Specifically, an optimization problem is formulated and solved at each EV
charging station according to control signal from aggregated control center and
user charging behavior prediction by mean estimation and linear regression. The
control center collects distributed optimization results and updates the
control signal, periodically. The iteration continues until it converges to
optimal scheduling. Experimental result shows this algorithm helps fill the
valley and shave the peak in electric load profiles within a microgrid, while
the energy demand of individual driver can be satisfied.
| 1 | 0 | 1 | 0 | 0 | 0 |
Horcrux: A Password Manager for Paranoids | Vulnerabilities in password managers are unremitting because current designs
provide large attack surfaces, both at the client and server. We describe and
evaluate Horcrux, a password manager that is designed holistically to minimize
and decentralize trust, while retaining the usability of a traditional password
manager. The prototype Horcrux client, implemented as a Firefox add-on, is
split into two components, with code that has access to the user's master's
password and any key material isolated into a small auditable component,
separate from the complexity of managing the user interface. Instead of
exposing actual credentials to the DOM, a dummy username and password are
autofilled by the untrusted component. The trusted component intercepts and
modifies POST requests before they are encrypted and sent over the network. To
avoid trusting a centralized store, stored credentials are secret-shared over
multiple servers. To provide domain and username privacy, while maintaining
resilience to off-line attacks on a compromised password store, we incorporate
cuckoo hashing in a way that ensures an attacker cannot determine if a guessed
master password is correct. Our approach only works for websites that do not
manipulate entered credentials in the browser client, so we conducted a
large-scale experiment that found the technique appears to be compatible with
over 98% of tested login forms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cross-validation | This text is a survey on cross-validation. We define all classical
cross-validation procedures, and we study their properties for two different
goals: estimating the risk of a given estimator, and selecting the best
estimator among a given family. For the risk estimation problem, we compute the
bias (which can also be corrected) and the variance of cross-validation
methods. For estimator selection, we first provide a first-order analysis
(based on expectations). Then, we explain how to take into account second-order
terms (from variance computations, and by taking into account the usefulness of
overpenalization). This allows, in the end, to provide some guidelines for
choosing the best cross-validation method for a given learning problem.
| 0 | 0 | 1 | 1 | 0 | 0 |
Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees | Objective: Using traditional approaches, a Brain-Computer Interface (BCI)
requires the collection of calibration data for new subjects prior to online
use. Calibration time can be reduced or eliminated e.g.~by transfer of a
pre-trained classifier or unsupervised adaptive classification methods which
learn from scratch and adapt over time. While such heuristics work well in
practice, none of them can provide theoretical guarantees. Our objective is to
modify an event-related potential (ERP) paradigm to work in unison with the
machine learning decoder to achieve a reliable calibration-less decoding with a
guarantee to recover the true class means.
Method: We introduce learning from label proportions (LLP) to the BCI
community as a new unsupervised, and easy-to-implement classification approach
for ERP-based BCIs. The LLP estimates the mean target and non-target responses
based on known proportions of these two classes in different groups of the
data. We modified a visual ERP speller to meet the requirements of the LLP. For
evaluation, we ran simulations on artificially created data sets and conducted
an online BCI study with N=13 subjects performing a copy-spelling task.
Results: Theoretical considerations show that LLP is guaranteed to minimize
the loss function similarly to a corresponding supervised classifier. It
performed well in simulations and in the online application, where 84.5% of
characters were spelled correctly on average without prior calibration.
Significance: The continuously adapting LLP classifier is the first
unsupervised decoder for ERP BCIs guaranteed to find the true class means. This
makes it an ideal solution to avoid a tedious calibration and to tackle
non-stationarities in the data. Additionally, LLP works on complementary
principles compared to existing unsupervised methods, allowing for their
further enhancement when combined with LLP.
| 1 | 0 | 0 | 1 | 0 | 0 |
Generalized Index Coding Problem and Discrete Polymatroids | The index coding problem has been generalized recently to accommodate
receivers which demand functions of messages and which possess functions of
messages. The connections between index coding and matroid theory have been
well studied in the recent past. Index coding solutions were first connected to
multi linear representation of matroids. For vector linear index codes discrete
polymatroids which can be viewed as a generalization of the matroids was used.
It was shown that a vector linear solution to an index coding problem exists if
and only if there exists a representable discrete polymatroid satisfying
certain conditions. In this work we explore the connections between generalized
index coding and discrete polymatroids. The conditions that needs to be
satisfied by a representable discrete polymatroid for a generalized index
coding problem to have a vector linear solution is established. From a discrete
polymatroid we construct an index coding problem with coded side information
and shows that if the index coding problem has a certain optimal length
solution then the discrete polymatroid satisfies certain properties. From a
matroid we construct a similar generalized index coding problem and shows that
the index coding problem has a binary scalar linear solution of optimal length
if and only if the matroid is binary representable.
| 1 | 0 | 1 | 0 | 0 | 0 |
Reconstruction via the intrinsic geometric structures of interior transmission eigenfunctions | We are concerned with the inverse scattering problem of extracting the
geometric structures of an unknown/inaccessible inhomogeneous medium by using
the corresponding acoustic far-field measurement. Using the intrinsic geometric
properties of the so-called interior transmission eigenfunctions, we develop a
novel inverse scattering scheme. The proposed method can efficiently capture
the cusp singularities of the support of the inhomogeneous medium. If further a
priori information is available on the support of the medium, say, it is a
convex polyhedron, then one can actually recover its shape. Both theoretical
analysis and numerical experiments are provided. Our reconstruction method is
new to the literature and opens up a new direction in the study of inverse
scattering problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Thermoelectric phase diagram of the SrTiO3-SrNbO3 solid solution system | Thermoelectric energy conversion - the exploitation of the Seebeck effect to
convert waste heat into electricity - has attracted an increasing amount of
research attention for energy harvesting technology. Niobium-doped strontium
titanate (SrTi1-xNbxO3) is one of the most promising thermoelectric material
candidates, particularly as it poses a much lesser environmental risk in
comparison to materials based on heavy metal elements. Two-dimensional electron
confinement, e.g. through the formation of superlattices or two-dimensional
electron gases, is recognized as an effective strategy to improve the
thermoelectric performance of SrTi1-xNbxO3. Although electron confinement is
closely related to the electronic structure, the fundamental electronic phase
behavior of the SrTi1-xNbxO3 solid solution system has yet to be
comprehensively investigated. Here, we present a thermoelectric phase diagram
for the SrTi1-xNbxO3 (0.05 =< x =< 1) solid solution system, which we derived
from the characterization of epitaxial films. We observed two thermoelectric
phase boundaries in the system, which originate from the step-like decrease in
carrier effective mass at x ~ 0.3, and from a local minimum in carrier
relaxation time at x ~ 0.5. The origins of these phase boundaries are
considered to be related to isovalent/heterovalent B-site substitution:
parabolic Ti 3d orbitals dominate electron conduction for compositions with x <
0.3, whereas the Nb 4d orbital dominates when x > 0.3. At x ~ 0.5, a tetragonal
distortion of the lattice, in which the B-site is composed of Ti4+ and Nb4+
ions, leads to the formation of tail-like impurity bands, which maximizes the
electron scattering. These results provide a foundation for further research
into improving the thermoelectric performance of SrTi1-xNbxO3.
| 0 | 1 | 0 | 0 | 0 | 0 |
AMPA, NMDA and GABAA receptor mediated network burst dynamics in cortical cultures in vitro | In this work we study the excitatory AMPA, and NMDA, and inhibitory GABAA
receptor mediated dynamical changes in neuronal networks of neonatal rat cortex
in vitro. Extracellular network-wide activity was recorded with 59 planar
electrodes simultaneously under different pharmacological conditions. We
analyzed the changes of overall network activity and network-wide burst
frequency between baseline and AMPA receptor (AMPA-R) or NMDA receptor (NMDA-R)
driven activity, as well as between the latter states and disinhibited
activity. Additionally, spatiotemporal structures of pharmacologically modified
bursts and recruitment of electrodes during the network bursts were studied.
Our results show that AMPA-R and NMDA-R receptors have clearly distinct roles
in network dynamics. AMPA-Rs are in greater charge to initiate network wide
bursts. Therefore NMDA-Rs maintain the already initiated activity. GABAA
receptors (GABAA-Rs) inhibit AMPA-R driven network activity more strongly than
NMDA-R driven activity during the bursts.
| 0 | 0 | 0 | 0 | 1 | 0 |
Coarse fundamental groups and box spaces | We use a coarse version of the fundamental group first introduced by Barcelo,
Kramer, Laubenbacher and Weaver to show that box spaces of finitely presented
groups detect the normal subgroups used to construct the box space, up to
isomorphism. As a consequence we have that two finitely presented groups admit
coarsely equivalent box spaces if and only if they are commensurable via normal
subgroups. We also provide an example of two filtrations $(N_i)$ and $(M_i)$ of
a free group $F$ such that $M_i>N_i$ for all $i$ with $[M_i:N_i]$ uniformly
bounded, but with $\Box_{(N_i)}F$ not coarsely equivalent to $\Box_{(M_i)}F$.
Finally, we give some applications of the main theorem for rank gradient and
the first $\ell^2$ Betti number, and show that the main theorem can be used to
construct infinitely many coarse equivalence classes of box spaces with various
properties.
| 0 | 0 | 1 | 0 | 0 | 0 |
Algebraic entropy of (integrable) lattice equations and their reductions | We study the growth of degrees in many autonomous and non-autonomous lattice
equations defined by quad rules with corner boundary values, some of which are
known to be integrable by other characterisations. Subject to an enabling
conjecture, we prove polynomial growth for a large class of equations which
includes the Adler-Bobenko-Suris equations and Viallet's $Q_V$ and its
non-autonomous generalization. Our technique is to determine the ambient degree
growth of the projective version of the lattice equations and to conjecture the
growth of their common factors at each lattice vertex, allowing the true degree
growth to be found. The resulting degrees satisfy a linear partial difference
equation which is universal, i.e. the same for all the integrable lattice
equations considered. When we take periodic reductions of these equations,
which includes staircase initial conditions, we obtain from this linear partial
difference equation an ordinary difference equation for degrees that implies
quadratic or linear degree growth. We also study growth of degree of several
non-integrable lattice equations. Exponential growth of degrees of these
equations, and their mapping reductions, is also proved subject to a
conjecture.
| 0 | 1 | 0 | 0 | 0 | 0 |
Measurement of mirror birefringence with laser heterodyne polarimetry | A laser heterodyne polarimeter (LHP) designed for the measurement of the
birefringence of dielectric super-mirrors is described and initial results are
reported. The LHP does not require an optical resonator and so promises
unprecedented accuracy in the measurement of the birefringence of individual
mirrors. The working principle of the LHP can be applied to the measurement of
vacuum birefringence and potentially ALPS (Any Light Particle Search).
| 0 | 1 | 0 | 0 | 0 | 0 |
Individual position diversity in dependence socioeconomic networks increases economic output | The availability of big data recorded from massively multiplayer online
role-playing games (MMORPGs) allows us to gain a deeper understanding of the
potential connection between individuals' network positions and their economic
outputs. We use a statistical filtering method to construct dependence networks
from weighted friendship networks of individuals. We investigate the 30
distinct motif positions in the 13 directed triadic motifs which represent
microscopic dependences among individuals. Based on the structural similarity
of motif positions, we further classify individuals into different groups. The
node position diversity of individuals is found to be positively correlated
with their economic outputs. We also find that the economic outputs of leaf
nodes are significantly lower than that of the other nodes in the same motif.
Our findings shed light on understanding the influence of network structure on
economic activities and outputs in socioeconomic system.
| 1 | 1 | 0 | 0 | 0 | 0 |
A formula for the nonsymmetric Opdam's hypergeometric function of type $A_2$ | The aim of this paper is to give an explicit formula for the nonsymmetric
Heckman-Opdam's hypergeometric function of type $A_2$. This is obtained by
differentiating the corresponding symmetric hypergeometric function.
| 0 | 0 | 1 | 0 | 0 | 0 |
A new algorithm for irreducible decomposition of representations of finite groups | An algorithm for irreducible decomposition of representations of finite
groups over fields of characteristic zero is described. The algorithm uses the
fact that the decomposition induces a partition of the invariant inner product
into a complete set of mutually orthogonal projectors. By expressing the
projectors through the basis elements of the centralizer ring of the
representation, the problem is reduced to solving systems of quadratic
equations. The current implementation of the algorithm is able to split
representations of dimensions up to hundreds of thousands. Examples of
calculations are given.
| 1 | 0 | 0 | 0 | 0 | 0 |
Stability and Grothendieck | This note is a commentary on the model-theoretic interpretation of
Grothendieck's double limit characterization of weak relative compactness.
| 0 | 0 | 1 | 0 | 0 | 0 |
Visual Analogies between Atari Games for Studying Transfer Learning in RL | In this work, we ask the following question: Can visual analogies, learned in
an unsupervised way, be used in order to transfer knowledge between pairs of
games and even play one game using an agent trained for another game? We
attempt to answer this research question by creating visual analogies between a
pair of games: a source game and a target game. For example, given a video
frame in the target game, we map it to an analogous state in the source game
and then attempt to play using a trained policy learned for the source game. We
demonstrate convincing visual mapping between four pairs of games (eight
mappings), which are used to evaluate three transfer learning approaches.
| 0 | 0 | 0 | 1 | 0 | 0 |
Learning in the Repeated Secretary Problem | In the classical secretary problem, one attempts to find the maximum of an
unknown and unlearnable distribution through sequential search. In many
real-world searches, however, distributions are not entirely unknown and can be
learned through experience. To investigate learning in such a repeated
secretary problem we conduct a large-scale behavioral experiment in which
people search repeatedly from fixed distributions. In contrast to prior
investigations that find no evidence for learning in the classical scenario, in
the repeated setting we observe substantial learning resulting in near-optimal
stopping behavior. We conduct a Bayesian comparison of multiple behavioral
models which shows that participants' behavior is best described by a class of
threshold-based models that contains the theoretically optimal strategy.
Fitting such a threshold-based model to data reveals players' estimated
thresholds to be surprisingly close to the optimal thresholds after only a
small number of games.
| 1 | 0 | 0 | 0 | 0 | 0 |
Flexible Mixture Modeling on Constrained Spaces | This paper addresses challenges in flexibly modeling multimodal data that lie
on constrained spaces. Applications include climate or crime measurements in a
geographical area, or flow-cytometry experiments, where unsuitable recordings
are discarded. A simple approach to modeling such data is through the use of
mixture models, with each component following an appropriate truncated
distribution. Problems arise when the truncation involves complicated
constraints, leading to difficulties in specifying the component distributions,
and in evaluating their normalization constants. Bayesian inference over the
parameters of these models results in posterior distributions that are
doubly-intractable. We address this problem via an algorithm based on rejection
sampling and data augmentation. We view samples from a truncated distribution
as outcomes of a rejection sampling scheme, where proposals are made from a
simple mixture model, and are rejected if they violate the constraints. Our
scheme proceeds by imputing the rejected samples given mixture parameters, and
then resampling parameters given all samples. We study two modeling approaches:
mixtures of truncated components and truncated mixtures of components. In both
situations, we describe exact Markov chain Monte Carlo sampling algorithms, as
well as approximations that bound the number of rejected samples, achieving
computational efficiency and lower variance at the cost of asymptotic bias.
Overall, our methodology only requires practitioners to provide an indicator
function for the set of interest. We present results on simulated data and
apply our algorithm to two problems, one involving flow-cytometry data, and the
other, crime recorded in the city of Chicago.
| 0 | 0 | 0 | 1 | 0 | 0 |
Universal equilibrium scaling functions at short times after a quench | By analyzing spin-spin correlation functions at relatively short distances,
we show that equilibrium near-critical properties can be extracted at short
times after quenches into the vicinity of a quantum critical point. The time
scales after which equilibrium properties can be extracted are sufficiently
short so that the proposed scheme should be viable for quantum simulators of
spin models based on ultracold atoms or trapped ions. Our results, analytic as
well as numeric, are for one-dimensional spin models, either integrable or
nonintegrable, but we expect our conclusions to be valid in higher dimensions
as well.
| 0 | 1 | 0 | 0 | 0 | 0 |
First Results from CUORE: A Search for Lepton Number Violation via $0νββ$ Decay of $^{130}$Te | The CUORE experiment, a ton-scale cryogenic bolometer array, recently began
operation at the Laboratori Nazionali del Gran Sasso in Italy. The array
represents a significant advancement in this technology, and in this work we
apply it for the first time to a high-sensitivity search for a
lepton-number--violating process: $^{130}$Te neutrinoless double-beta decay.
Examining a total TeO$_2$ exposure of 86.3 kg$\cdot$yr, characterized by an
effective energy resolution of (7.7 $\pm$ 0.5) keV FWHM and a background in the
region of interest of (0.014 $\pm$ 0.002) counts/(keV$\cdot$kg$\cdot$yr), we
find no evidence for neutrinoless double-beta decay. The median statistical
sensitivity of this search is $7.0\times10^{24}$ yr. Including systematic
uncertainties, we place a lower limit on the decay half-life of
$T^{0\nu}_{1/2}$($^{130}$Te) > $1.3\times 10^{25}$ yr (90% C.L.). Combining
this result with those of two earlier experiments, Cuoricino and CUORE-0, we
find $T^{0\nu}_{1/2}$($^{130}$Te) > $1.5\times 10^{25}$ yr (90% C.L.), which is
the most stringent limit to date on this decay. Interpreting this result as a
limit on the effective Majorana neutrino mass, we find $m_{\beta\beta}<(110 -
520)$ meV, where the range reflects the nuclear matrix element estimates
employed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bernoulli-Carlitz and Cauchy-Carlitz numbers with Stirling-Carlitz numbers | Recently, the Cauchy-Carlitz number was defined as the counterpart of the
Bernoulli-Carlitz number. Both numbers can be expressed explicitly in terms of
so-called Stirling-Carlitz numbers. In this paper, we study the second analogue
of Stirling-Carlitz numbers and give some general formulae, including Bernoulli
and Cauchy numbers in formal power series with complex coefficients, and
Bernoulli-Carlitz and Cauchy-Carlitz numbers in function fields. We also give
some applications of Hasse-Teichmüller derivative to hypergeometric Bernoulli
and Cauchy numbers in terms of associated Stirling numbers.
| 0 | 0 | 1 | 0 | 0 | 0 |
Irreducible network backbones: unbiased graph filtering via maximum entropy | Networks provide an informative, yet non-redundant description of complex
systems only if links represent truly dyadic relationships that cannot be
directly traced back to node-specific properties such as size, importance, or
coordinates in some embedding space. In any real-world network, some links may
be reducible, and others irreducible, to such local properties. This dichotomy
persists despite the steady increase in data availability and resolution, which
actually determines an even stronger need for filtering techniques aimed at
discerning essential links from non-essential ones. Here we introduce a
rigorous method that, for any desired level of statistical significance,
outputs the network backbone that is irreducible to the local properties of
nodes, i.e. their degrees and strengths. Unlike previous approaches, our method
employs an exact maximum-entropy formulation guaranteeing that the filtered
network encodes only the links that cannot be inferred from local information.
Extensive empirical analysis confirms that this approach uncovers essential
backbones that are otherwise hidden amidst many redundant relationships and
inaccessible to other methods. For instance, we retrieve the hub-and-spoke
skeleton of the US airport network and many specialised patterns of
international trade. Being irreducible to local transportation and economic
constraints of supply and demand, these backbones single out genuinely
higher-order wiring principles.
| 1 | 1 | 0 | 0 | 0 | 0 |
Synkhronos: a Multi-GPU Theano Extension for Data Parallelism | We present Synkhronos, an extension to Theano for multi-GPU computations
leveraging data parallelism. Our framework provides automated execution and
synchronization across devices, allowing users to continue to write serial
programs without risk of race conditions. The NVIDIA Collective Communication
Library is used for high-bandwidth inter-GPU communication. Further
enhancements to the Theano function interface include input slicing (with
aggregation) and input indexing, which perform common data-parallel computation
patterns efficiently. One example use case is synchronous SGD, which has
recently been shown to scale well for a growing set of deep learning problems.
When training ResNet-50, we achieve a near-linear speedup of 7.5x on an NVIDIA
DGX-1 using 8 GPUs, relative to Theano-only code running a single GPU in
isolation. Yet Synkhronos remains general to any data-parallel computation
programmable in Theano. By implementing parallelism at the level of individual
Theano functions, our framework uniquely addresses a niche between manual
multi-device programming and prescribed multi-GPU training routines.
| 1 | 0 | 0 | 0 | 0 | 0 |
The PomXYZ Proteins Self-Organize on the Bacterial Nucleoid to Stimulate Cell Division | Cell division site positioning is precisely regulated to generate correctly
sized and shaped daughters. We uncover a novel strategy to position the FtsZ
cytokinetic ring at midcell in the social bacterium Myxococcus xanthus. PomX,
PomY and the nucleoid-binding ParA/MinD ATPase PomZ self-assemble forming a
large nucleoid-associated complex that localizes at the division site before
FtsZ to directly guide and stimulate division. PomXYZ localization is generated
through self-organized biased random motion on the nucleoid towards midcell and
constrained motion at midcell. Experiments and theory show that PomXYZ motion
is produced by diffusive PomZ fluxes on the nucleoid into the complex. Flux
differences scale with the intracellular asymmetry of the complex and are
converted into a local PomZ concentration gradient across the complex with
translocation towards the higher PomZ concentration. At midcell, fluxes
equalize resulting in constrained motion. Flux-based mechanisms may represent a
general paradigm for positioning of macromolecular structures in bacteria.
| 0 | 0 | 0 | 0 | 1 | 0 |
Quantitative Results on Diophantine Equations in Many Variables | We consider a system of polynomials $f_1,\ldots, f_R\in
\mathbb{Z}[x_1,\ldots, x_n]$ of the same degree with non-singular local zeros
and in many variables. Generalising the work of Birch (1962) we find
quantitative asymptotics (in terms of the maximum of the absolute value of the
coefficients of these polynomials) for the number of integer zeros of this
system within a growing box. Using a quantitative version of the
Nullstellensatz, we obtain a quantitative strong approximation result, i.e. an
upper bound on the smallest integer zero provided the system of polynomials is
non-singular.
| 0 | 0 | 1 | 0 | 0 | 0 |
Generalized Springer correspondence for symmetric spaces associated to orthogonal groups | Let $G = GL_N$ over an algebraically closed field of odd characteristic, and
$\theta$ an involutive automorphism on $G$ such that $H = (G^{\theta})^0$ is
isomorphic to $SO_N$. Then $G^{\iota\theta} = \{ g \in G \mid \theta(g) =
g^{-1} \}$ is regarded as a symmetric space $G/G^{\theta}$. Let
$G^{\iota\theta}_{uni}$ be the set of unipotent elements in $G^{\iota\theta}$.
$H$ acts on $G^{\iota\theta}_{uni}$ by the conjugation. As an analogue of the
generalized Springer correspondence in the case of reductive groups, we
establish in this paper the generalized Springer correspondence between
$H$-orbits in $G^{\iota\theta}_{uni}$ and irreducible representations of
various symmetric groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit | Our work focuses on the problem of predicting the transfer of pediatric
patients from the general ward of a hospital to the pediatric intensive care
unit. Using data collected over 5.5 years from the electronic health records of
two medical facilities, we develop classifiers based on adaptive boosting and
gradient tree boosting. We further combine these learned classifiers into an
ensemble model and compare its performance to a modified pediatric early
warning score (PEWS) baseline that relies on expert defined guidelines. To
gauge model generalizability, we perform an inter-facility evaluation where we
train our algorithm on data from one facility and perform evaluation on a
hidden test dataset from a separate facility. We show that improvements are
witnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80
vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).
| 1 | 0 | 0 | 1 | 0 | 0 |
Effects of Network Structure on the Performance of a Modeled Traffic Network under Drivers' Bounded Rationality | We propose a minority route choice game to investigate the effect of the
network structure on traffic network performance under the assumption of
drivers' bounded rationality. We investigate ring-and-hub topologies to capture
the nature of traffic networks in cities, and employ a minority game-based
inductive learning process to model the characteristic behavior under the route
choice scenario. Through numerical experiments, we find that topological
changes in traffic networks induce a phase transition from an uncongested phase
to a congested phase. Understanding this phase transition is helpful in
planning new traffic networks.
| 1 | 1 | 0 | 0 | 0 | 0 |
Sneak into Devil's Colony- A study of Fake Profiles in Online Social Networks and the Cyber Law | Massive content about user's social, personal and professional life stored on
Online Social Networks (OSNs) has attracted not only the attention of
researchers and social analysts but also the cyber criminals. These cyber
criminals penetrate illegally into an OSN by establishing fake profiles or by
designing bots and exploit the vulnerabilities of an OSN to carry out illegal
activities. With the growth of technology cyber crimes have been increasing
manifold. Daily reports of the security and privacy threats in the OSNs demand
not only the intelligent automated detection systems that can identify and
alleviate fake profiles in real time but also the reinforcement of the security
and privacy laws to curtail the cyber crime. In this paper, we have studied
various categories of fake profiles like compromised profiles, cloned profiles
and online bots (spam-bots, social-bots, like-bots and influential-bots) on
different OSN sites along with existing cyber laws to mitigate their threats.
In order to design fake profile detection systems, we have highlighted
different category of fake profile features which are capable to distinguish
different kinds of fake entities from real ones. Another major challenges faced
by researchers while building the fake profile detection systems is the
unavailability of data specific to fake users. The paper addresses this
challenge by providing extremely obliging data collection techniques along with
some existing data sources. Furthermore, an attempt is made to present several
machine learning techniques employed to design different fake profile detection
systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Preserving Data-Privacy with Added Noises: Optimal Estimation and Privacy Analysis | Networked system often relies on distributed algorithms to achieve a global
computation goal with iterative local information exchanges between neighbor
nodes. To preserve data privacy, a node may add a random noise to its original
data for information exchange at each iteration. Nevertheless, a neighbor node
can estimate other's original data based on the information it received. The
estimation accuracy and data privacy can be measured in terms of $(\epsilon,
\delta)$-data-privacy, defined as the probability of $\epsilon$-accurate
estimation (the difference of an estimation and the original data is within
$\epsilon$) is no larger than $\delta$ (the disclosure probability). How to
optimize the estimation and analyze data privacy is a critical and open issue.
In this paper, a theoretical framework is developed to investigate how to
optimize the estimation of neighbor's original data using the local information
received, named optimal distributed estimation. Then, we study the disclosure
probability under the optimal estimation for data privacy analysis. We further
apply the developed framework to analyze the data privacy of the
privacy-preserving average consensus algorithm and identify the optimal noises
for the algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Geometric tuning of self-propulsion for Janus catalytic particles | Catalytic swimmers have attracted much attention as alternatives to
biological systems for examining collective microscopic dynamics and the
response to physico-chemical signals. Yet, understanding and predicting even
the most fundamental characteristics of their individual propulsion still
raises important challenges. While chemical asymmetry is widely recognized as
the cornerstone of catalytic propulsion, different experimental studies have
reported that particles with identical chemical properties may propel in
opposite directions. Here, we show that, beyond its chemical properties, the
detailed shape of a catalytic swimmer plays an essential role in determining
its direction of motion, demonstrating the compatibility of the classical
theoretical framework with experimental observations.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the Bogolubov-de Gennes Equations | We consider the Bogolubov-de Gennes equations giving an equivalent
formulation of the BCS theory of superconductivity. We are interested in the
case when the magnetic field is present. We (a) discuss their general features,
(b) isolate key physical classes of solutions (normal, vortex and vortex
lattice states) and (c) prove existence of the normal, vortex and vortex
lattice states and stability/instability of the normal states for large/small
temperature or/and magnetic fields.
| 0 | 0 | 1 | 0 | 0 | 0 |
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking | Penalized likelihood methods are widely used for high-dimensional regression.
Although many methods have been proposed and the associated theory is now
well-developed, the relative efficacy of different methods in finite-sample
settings, as encountered in practice, remains incompletely understood. There is
therefore a need for empirical investigations in this area that can offer
practical insight and guidance to users of these methods. In this paper we
present a large-scale comparison of penalized regression methods. We
distinguish between three related goals: prediction, variable selection and
variable ranking. Our results span more than 1,800 data-generating scenarios,
allowing us to systematically consider the influence of various factors (sample
size, dimensionality, sparsity, signal strength and multicollinearity). We
consider several widely-used methods (Lasso, Elastic Net, Ridge Regression,
SCAD, the Dantzig Selector as well as Stability Selection). We find
considerable variation in performance between methods, with results dependent
on details of the data-generating scenario and the specific goal. Our results
support a `no panacea' view, with no unambiguous winner across all scenarios,
even in this restricted setting where all data align well with the assumptions
underlying the methods. Lasso is well-behaved, performing competitively in many
scenarios, while SCAD is highly variable. Substantial benefits from a
Ridge-penalty are only seen in the most challenging scenarios with strong
multi-collinearity. The results are supported by semi-synthetic analyzes using
gene expression data from cancer samples. Our empirical results complement
existing theory and provide a resource to compare methods across a range of
scenarios and metrics.
| 0 | 0 | 0 | 1 | 0 | 0 |
Using Human Brain Activity to Guide Machine Learning | Machine learning is a field of computer science that builds algorithms that
learn. In many cases, machine learning algorithms are used to recreate a human
ability like adding a caption to a photo, driving a car, or playing a game.
While the human brain has long served as a source of inspiration for machine
learning, little effort has been made to directly use data collected from
working brains as a guide for machine learning algorithms. Here we demonstrate
a new paradigm of "neurally-weighted" machine learning, which takes fMRI
measurements of human brain activity from subjects viewing images, and infuses
these data into the training process of an object recognition learning
algorithm to make it more consistent with the human brain. After training,
these neurally-weighted classifiers are able to classify images without
requiring any additional neural data. We show that our neural-weighting
approach can lead to large performance gains when used with traditional machine
vision features, as well as to significant improvements with already
high-performing convolutional neural network features. The effectiveness of
this approach points to a path forward for a new class of hybrid machine
learning algorithms which take both inspiration and direct constraints from
neuronal data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning | We use a deep learning model trained only on a patient's blood oxygenation
data (measurable with an inexpensive fingertip sensor) to predict impending
hypoxemia (low blood oxygen) more accurately than trained anesthesiologists
with access to all the data recorded in a modern operating room. We also
provide a simple way to visualize the reason why a patient's risk is low or
high by assigning weight to the patient's past blood oxygen values. This work
has the potential to provide cutting-edge clinical decision support in
low-resource settings, where rates of surgical complication and death are
substantially greater than in high-resource areas.
| 1 | 0 | 0 | 1 | 0 | 0 |
Merging fragments of classical logic | We investigate the possibility of extending the non-functionally complete
logic of a collection of Boolean connectives by the addition of further Boolean
connectives that make the resulting set of connectives functionally complete.
More precisely, we will be interested in checking whether an axiomatization for
Classical Propositional Logic may be produced by merging Hilbert-style calculi
for two disjoint incomplete fragments of it. We will prove that the answer to
that problem is a negative one, unless one of the components includes only
top-like connectives.
| 1 | 0 | 1 | 0 | 0 | 0 |
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series | We propose a new variational Bayes estimator for high-dimensional copulas
with discrete, or a combination of discrete and continuous, margins. The method
is based on a variational approximation to a tractable augmented posterior, and
is faster than previous likelihood-based approaches. We use it to estimate
drawable vine copulas for univariate and multivariate Markov ordinal and mixed
time series. These have dimension $rT$, where $T$ is the number of observations
and $r$ is the number of series, and are difficult to estimate using previous
methods. The vine pair-copulas are carefully selected to allow for
heteroskedasticity, which is a feature of most ordinal time series data. When
combined with flexible margins, the resulting time series models also allow for
other common features of ordinal data, such as zero inflation, multiple modes
and under- or over-dispersion. Using six example series, we illustrate both the
flexibility of the time series copula models, and the efficacy of the
variational Bayes estimator for copulas of up to 792 dimensions and 60
parameters. This far exceeds the size and complexity of copula models for
discrete data that can be estimated using previous methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
COSMO: Contextualized Scene Modeling with Boltzmann Machines | Scene modeling is very crucial for robots that need to perceive, reason about
and manipulate the objects in their environments. In this paper, we adapt and
extend Boltzmann Machines (BMs) for contextualized scene modeling. Although
there are many models on the subject, ours is the first to bring together
objects, relations, and affordances in a highly-capable generative model. For
this end, we introduce a hybrid version of BMs where relations and affordances
are introduced with shared, tri-way connections into the model. Moreover, we
contribute a dataset for relation estimation and modeling studies. We evaluate
our method in comparison with several baselines on object estimation,
out-of-context object detection, relation estimation, and affordance estimation
tasks. Moreover, to illustrate the generative capability of the model, we show
several example scenes that the model is able to generate.
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Learning Large-Scale Topological Maps Using Sum-Product Networks | In order to perform complex actions in human environments, an autonomous
robot needs the ability to understand the environment, that is, to gather and
maintain spatial knowledge. Topological map is commonly used for representing
large scale, global maps such as floor plans. Although much work has been done
in topological map extraction, we have found little previous work on the
problem of learning the topological map using a probabilistic model. Learning a
topological map means learning the structure of the large-scale space and
dependency between places, for example, how the evidence of a group of places
influence the attributes of other places. This is an important step towards
planning complex actions in the environment. In this thesis, we consider the
problem of using probabilistic deep learning model to learn the topological
map, which is essentially a sparse undirected graph where nodes represent
places annotated with their semantic attributes (e.g. place category). We
propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs),
due to their unique properties. We present two methods for learning topological
maps using SPNs: the place grid method and the template-based method. We
contribute an algorithm that builds SPNs for graphs using template models. Our
experiments evaluate the ability of our models to enable robots to infer
semantic attributes and detect maps with novel semantic attribute arrangements.
Our results demonstrate their understanding of the topological map structure
and spatial relations between places.
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Entanglement scaling and spatial correlations of the transverse field Ising model with perturbations | We study numerically the entanglement entropy and spatial correlations of the
one dimensional transverse field Ising model with three different
perturbations. First, we focus on the out of equilibrium, steady state with an
energy current passing through the system. By employing a variety of
matrix-product state based methods, we confirm the phase diagram and compute
the entanglement entropy. Second, we consider a small perturbation that takes
the system away from integrability and calculate the correlations, the central
charge and the entanglement entropy. Third, we consider periodically weakened
bonds, exploring the phase diagram and entanglement properties first in the
situation when the weak and strong bonds alternate (period two-bonds) and then
the general situation of a period of n bonds. In the latter case we find a
critical weak bond that scales with the transverse field as $J'_c/J$ =
$(h/J)^n$, where $J$ is the strength of the strong bond, $J'$ of the weak bond
and $h$ the transverse field. We explicitly show that the energy current is not
a conserved quantity in this case.
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Anyonic excitations of hardcore anyons | Strongly interacting many-body systems consisting of fermions or bosons can
host exotic quasiparticles with anyonic statistics. Here, we demonstrate that
many-body systems of anyons can also form anyonic quasi-particles. The charge
and statistics of the emergent anyons can be different from those of the
original anyons.
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