pwc-archive/papers-with-abstracts huggingface dataset:
Out-of-Sample Extrapolation with Neuron Editing
Date: 2019-01-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generative Adversarial Network

While neural networks can be trained to map from one specific dataset to
another, they usually do not learn a generalized transformation that can
extrapolate accurately outside the space of training. For instance, a
generative adversarial network (GAN) exclusively trained to transform images of
black-haired men to blond-haired men might not have the same effect on images
of black-haired women. This is because neural networks are good at generation
within the manifold of the data that they are trained on. However, generating
new samples outside of the manifold or extrapolating "out-of-sample" is a much
harder problem that has been less well studied. To address this, we introduce a
technique called neuron editing that learns how neurons encode an edit for a
particular transformation in a latent space. We use an autoencoder to decompose
the variation within the dataset into activations of different neurons and
generate transformed data by defining an editing transformation on those
neurons. By performing the transformation in a latent trained space, we encode
fairly complex and non-linear transformations to the data with much simpler
distribution shifts to the neuron's activations. We motivate our technique on
an image domain and then move to our two main biological applications: removal
of batch artifacts representing unwanted noise and modeling the effect of drug
treatments to predict synergy between drugs.
Introducing shrinkage in heavy-tailed state space models to predict equity excess returns
Date: 2019-07-26 00:00:00
Tasks: State Space Models

We forecast S&P 500 excess returns using a flexible Bayesian econometric
state space model with non-Gaussian features at several levels. More precisely,
we control for overparameterization via novel global-local shrinkage priors on
the state innovation variances as well as the time-invariant part of the state
space model. The shrinkage priors are complemented by heavy tailed state
innovations that cater for potential large breaks in the latent states.
Moreover, we allow for leptokurtic stochastic volatility in the observation
equation. The empirical findings indicate that several variants of the proposed
approach outperform typical competitors frequently used in the literature, both
in terms of point and density forecasts.
Sequential Experimental Design for Optimal Structural Intervention in Gene Regulatory Networks Based on the Mean Objective Cost of Uncertainty
Date: 2018-05-30 00:00:00
Tasks: Experimental Design

Scientists are attempting to use models of ever increasing complexity,
especially in medicine, where gene-based diseases such as cancer require better
modeling of cell regulation. Complex models suffer from uncertainty and
experiments are needed to reduce this uncertainty. Because experiments can be
costly and time-consuming it is desirable to determine experiments providing
the most useful information. If a sequence of experiments is to be performed,
experimental design is needed to determine the order. A classical approach is
to maximally reduce the overall uncertainty in the model, meaning maximal
entropy reduction. A recently proposed method takes into account both model
uncertainty and the translational objective, for instance, optimal structural
intervention in gene regulatory networks, where the aim is to alter the
regulatory logic to maximally reduce the long-run likelihood of being in a
cancerous state. The mean objective cost of uncertainty (MOCU) quantifies
uncertainty based on the degree to which model uncertainty affects the
objective. Experimental design involves choosing the experiment that yields the
greatest reduction in MOCU. This paper introduces finite-horizon dynamic
programming for MOCU-based sequential experimental design and compares it to
the greedy approach, which selects one experiment at a time without
consideration of the full horizon of experiments. A salient aspect of the paper
is that it demonstrates the advantage of MOCU-based design over the widely used
entropy-based design for both greedy and dynamic-programming strategies and
investigates the effect of model conditions on the comparative performances.
Optimization of the Energy-Efficient Relay-Based massive IoT Network
Date: 2018-05-31 00:00:00

To meet the requirements of high energy efficiency (EE) and large system
capacity for the fifth-generation (5G) Internet of Things (IoT), the use of
massive multiple-input multipleoutput (MIMO) technology has been launched in
the massive IoT (mIoT) network, where a large number of devices are connected
and scheduled simultaneously. This paper considers the energyefficient design
of a multi-pair decode-and-forward relay-based IoT network, in which multiple
sources simultaneously transmit their information to the corresponding
destinations via a relay equipped with a large array. In order to obtain an
accurate yet tractable expression of the EE, firstly, a closed-form expression
of the EE is derived under an idealized simplifying assumption, in which the
location of each device is known by the network. Then, an exact integral-based
expression of the EE is derived under the assumption that the devices are
randomly scattered following a uniform distribution and transmit power of the
relay is equally shared among the destination devices. Furthermore, a simple
yet efficient lower bound of the EE is obtained. Based on this, finally, a
low-complexity energy-efficient resource allocation strategy of the mIoT
network is proposed under the specific qualityof- service (QoS) constraint. The
proposed strategy determines the near-optimal number of relay antennas, the
near-optimal transmit power at the relay and near-optimal density of active
mIoT device pairs in a given coverage area. Numerical results demonstrate the
accuracy of the performance analysis and the efficiency of the proposed
algorithms.
Millimeter-Wave NOMA Transmission in Cellular M2M Communications for Internet of Things
Date: 2018-05-31 00:00:00

Massive connectivity and low latency are two important challenges for the
Internet of Things (IoT) to achieve the Quality of Service (QoS) provisions
required by the numerous devices it is designed to service. Motivated by these
challenges, in the paper we introduce a new millimeter-wave non-orthogonal
multiple access (mmWave-NOMA) transmission scheme designed for cellular
machine-to-machine (M2M) communication systems for IoT applications. It
consists of one base station (BS) and numerous multiple machine type
communication (MTC) devices operating in a cellular communication environment.
We consider its down-link performance and assume that multiple MTC devices
share the same communication resources offered by the proposed mmWave-NOMA
transmission scheme, which can support massive connectivity. For this system, a
novel MTC pairing scheme is introduced the design of which is based upon the
distance between the BS and the MTC devices aiming at reducing the system
overall overhead for massive connectivity and latency. In particular, we
consider three different MTC device pairing schemes, namely i) the random near
and the random far MTC devices (RNRF); ii) the nearest near and the nearest far
MTC devices (NNNF); and iii) the nearest near and the farthest far MTC device
(NNFF). For all three pairing schemes, their performance is analyzed by
deriving closed-form expressions of the outage probability and the sum rate.
Furthermore, performance comparison studies of the three MTC device pairing
schemes have been carried out.
Robust MIMO Radar Target Localization based on Lagrange Programming Neural Network
Date: 2018-05-31 00:00:00

This paper focuses on target localization in a widely distributed
multiple-input-multiple-output (MIMO) radar system. In this system, range
measurements, which include the sum of distances between transmitter and target
and the distances from the target to receivers, are used. We can obtain an
accurate estimated position of the target by minimizing the measurement errors.
In order to make our model come closer to reality, we introduce two kinds of
noises, namely, Gaussian noise and outliers. When we evaluate a target
localization algorithm, its localization accuracy and computational complexity
are two main criteria. To improve the positioning accuracy, the original
problem is formulated as solving a non-smooth constrained optimization problem
in which the objective function is either l1-norm or l0-norm term. To achieve a
real-time solution, the Lagrange programming neural network (LPNN) is utilized
to solve this problem. However, it is well known that LPNN requires
twice-differentiable objective function and constraints. Obviously, the l1-norm
or l0-norm term in the objective function does not satisfy this requirement. To
address this non-smooth optimization problem, this paper proposes two
modifications based on the LPNN framework. In the first method, a
differentiable proximate l1-norm function is introduced. While in the second
method, locally competitive algorithm is utilized. Simulation and experimental
results demonstrate that the performance of the proposed algorithms outperforms
several existing schemes.
Collaborative Multi-modal deep learning for the personalized product retrieval in Facebook Marketplace
Authors: Zheng Lu, Tan Zhao, Han Kun, Mao Ren
Date: 2018-05-31 00:00:00
Tasks: Retrieval

Facebook Marketplace is quickly gaining momentum among consumers as a favored
customer-to-customer (C2C) product trading platform. The recommendation system
behind it helps to significantly improve the user experience. Building the
recommendation system for Facebook Marketplace is challenging for two reasons:
1) Scalability: the number of products in Facebook Marketplace is huge. Tens of
thousands of products need to be scored and recommended within a couple hundred
milliseconds for millions of users every day; 2) Cold start: the life span of
the C2C products is very short and the user activities on the products are
sparse. Thus it is difficult to accumulate enough product level signals for
recommendation and we are facing a significant cold start issue. In this paper,
we propose to address both the scalability and the cold-start issue by building
a collaborative multi-modal deep learning based retrieval system where the
compact embeddings for the users and the products are trained with the
multi-modal content information. This system shows significant improvement over
the benchmark in online and off-line experiments: In the online experiment, it
increases the number of messages initiated by the buyer to the seller by
+26.95%; in the off-line experiment, it improves the prediction accuracy by
+9.58%.
Radio Vortex Wireless Communications With Non-Coaxial UCA Transceiver
Date: 2018-05-31 00:00:00

In the past decade, more and more researchers have concentrated on
orbital-angular-momentum (OAM) based radio vortex wireless communications,
which is expected to provide orthogonality among different OAM-modes. The
uniform circular array (UCA) is considered as one promising antenna structure
for OAM based radio vortex wireless communications. However, most studies
regarding UCA focus on the scenario where the transmit and receive UCAs are
aligned with each other. In this paper, we investigate the radio vortex
wireless communications with non-coaxial UCA, i.e., the UCA transceivers are
parallel but non-coaxial. We study the channel model and develop the
mode-decomposition scheme to decompose the OAM-modes. Then, we discuss the
impact of included angles on the channel model under non-coaxial scenario.
Numerical results are presented to evaluate our developed scheme and show that
the spectrum efficiency of the non-coaxial UCA transceiver in some cases is
larger than that of the aligned UCA transceiver based radio vortex wireless
communications.
The long-term impact of ranking algorithms in growing networks
Authors: Zhang Shilun, Medo Matúš, Lü Linyuan, Mariani Manuel Sebastian
Date: 2018-11-20 00:00:00
Tasks: Diversity

When we search online for content, we are constantly exposed to rankings. For
example, web search results are presented as a ranking, and online bookstores
often show us lists of best-selling books. While popularity-based ranking
algorithms (like Google's PageRank) have been extensively studied in previous
works, we still lack a clear understanding of their potential systemic
consequences. In this work, we fill this gap by introducing a new model of
network growth that allows us to compare the properties of the networks
generated under the influence of different ranking algorithms. We show that by
correcting for the omnipresent age bias of popularity-based ranking algorithms,
the resulting networks exhibit a significantly larger agreement between the
nodes' inherent quality and their long-term popularity, and a less concentrated
popularity distribution. To further promote popularity diversity, we introduce
and validate a perturbation of the original rankings where a small number of
randomly-selected nodes are promoted to the top of the ranking. Our findings
move the first steps toward a model-based understanding of the long-term impact
of popularity-based ranking algorithms, and could be used as an informative
tool for the design of improved information filtering tools.
Fast Hybrid Schemes for Fractional Riccati Equations (Rough is not so Tough)
Date: 2020-02-18 00:00:00

We solve a family of fractional Riccati differential equations with constant
(possibly complex) coefficients. These equations arise, e.g., in fractional
Heston stochastic volatility models, that have received great attention in the
recent financial literature thanks to their ability to reproduce a rough
volatility behavior. We first consider the case of a zero initial value
corresponding to the characteristic function of the log-price. Then we
investigate the case of a general starting value associated to a transform also
involving the volatility process. The solution to the fractional Riccati
equation takes the form of power series, whose convergence domain is typically
finite. This naturally suggests a hybrid numerical algorithm to explicitly
obtain the solution also beyond the convergence domain of the power series
representation. Our numerical tests show that the hybrid algorithm turns out to
be extremely fast and stable. When applied to option pricing, our method
largely outperforms the only available alternative in the literature, based on
the Adams method.
Robust Real-time Ellipse Fitting Based on Lagrange Programming Neural Network and Locally Competitive Algorithm
Date: 2018-05-31 00:00:00
Tasks: Edge Detection

Given a set of 2-dimensional (2-D) scattering points, which are usually
obtained from the edge detection process, the aim of ellipse fitting is to
construct an elliptic equation that best fits the collected observations.
However, some of the scattering points may contain outliers due to imperfect
edge detection. To address this issue, we devise a robust real-time ellipse
fitting approach based on two kinds of analog neural network, Lagrange
programming neural network (LPNN) and locally competitive algorithm (LCA).
First, to alleviate the influence of these outliers, the fitting task is
formulated as a nonsmooth constrained optimization problem in which the
objective function is either an l1-norm or l0-norm term. It is because compared
with the l2-norm in some traditional ellipse fitting models, the lp-norm with
p<2 is less sensitive to outliers. Then, to calculate a real-time solution of
this optimization problem, LPNN is applied. As the LPNN model cannot handle the
non-differentiable term in its objective, the concept of LCA is introduced and
combined with the LPNN framework. Simulation and experimental results show that
the proposed ellipse fitting approach is superior to several state-of-the-art
algorithms.
Holographic Microscopy with Python and HoloPy
Date: 2018-05-31 00:00:00

A holographic microscope captures interference patterns, or holograms, that
encode three-dimensional (3D) information about the object being viewed.
Computation is essential to extracting that 3D information. By wrapping
low-level scattering codes and taking advantage of Python's data analysis
ecosystem, HoloPy makes it easy for experimentalists to use modern,
sophisticated inference methods to analyze holograms. The resulting data can be
used to understand how small particles or microorganisms move and interact. The
project illustrates how computational tools can enable experimental methods and
new experiments.
Scanning Fluorescence Correlation Spectroscopy (SFCS) with a Scan Path Perpendicular to the Membrane Plane
Date: 2018-05-31 00:00:00

Scanning fluorescence correlation spectroscopy (SFCS) with a scan path
perpendicular to the membrane plane was introduced to measure diffusion and
interactions of fluorescent components in free standing biomembranes. Using a
confocal laser scanning microscope (CLSM) the open detection volume is moved
laterally with kHz frequency through the membrane and the photon events are
continuously recorded and stored in a file. While the accessory hardware
requirements for a conventional CLSM are minimal, data evaluation can pose a
bottleneck. The photon events must be assigned to each scan, in which the
maximum signal intensities have to be detected, binned, and aligned between the
scans, in order to derive the membrane related intensity fluctuations of one
spot. Finally, this time-dependent signal must be correlated and evaluated by
well known FCS model functions. Here we provide two platform independent, open
source software tools (PyScanFCS and PyCorrFit) that allow to perform all of
these steps and to establish perpendicular SFCS in its one- or two-focus as
well as its single- or dual-colour modality.
Exact steady-state distributions of multispecies birth-death-immigration processes: effects of mutations and carrying capacity on diversity
Date: 2018-08-11 00:00:00
Tasks: Diversity

Stochastic models that incorporate birth, death and immigration (also called
birth-death and innovation models) are ubiquitous and applicable to many
research topics such as quantifying species sizes in ecological populations,
describing gene family sizes, modeling lymphocyte evolution in the body, and
modeling the evolution of firm sizes. Many of these applications involve the
immigration of new species into the system. We develop the full
high-dimensional stochastic process associated with multispecies
birth-death-immigration processes and present a number of exact and asymptotic
results for the steady-state solutions to these types of processes. We
considered models that include carrying capacity and random mutations and find
analytic and approximate results for the statistics of the total number of
individuals, the total number of species, the species size distribution, and
various diversity indices. Our results include a rigorous analysis of the
behavior of these systems in the fast immigration limit which shows that of the
different diversity indices, the species richness is best able to distinguish
different types of birth-death-immigration models. Surprisingly, we also find
that detailed balance is preserved in all of our neutral
birth-death-immigration models, except for those that incorporate carrying
capacity through the birth rate.
Fast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular Communications
Date: 2019-09-25 00:00:00

Millimeter wave (mmWave) has been claimed to be the only viable solution for
high-bandwidth vehicular communications. However, frequent channel estimation
and beamforming required to provide a satisfactory quality of service limits
mmWave for vehicular communications. In this paper, we propose a novel channel
estimation and beam tracking framework for mmWave communications in a vehicular
network setting. For channel estimation, we propose an algorithm termed robust
adaptive multi-feedback (RAF) that achieves comparable estimation performance
as existing channel estimation algorithms, with a significantly smaller number
of feedback bits. We derive upper and lower bounds on the probability of
estimation error (PEE) of the RAF algorithm, given a number of channel
estimations, whose accuracy is verified through Monte Carlo simulations. For
beam tracking, we propose a new practical model for mmWave vehicular
communications. In contrast to the prior works, the model is based on position,
velocity, and channel coefficient, which allows a significant improvement of
the tracking performance. Focused on the new beam tracking model, we re-derive
the equations for Jacobian matrices, reducing the complexity for vehicular
communications. An extensive number of simulations is conducted to show the
superiority of our proposed channel estimation method and beam tracking
algorithm in comparison with the existing algorithms and models. Our
simulations suggest that the RAF algorithm can achieve the desired PEE, while
on average, reducing the feedback overhead by 75.5% and the total channel
estimation time by 14%. The beam tracking algorithm is also shown to
significantly improve beam tracking performance, allowing more room for data
transmission.
Lineage EM Algorithm for Inferring Latent States from Cellular Lineage Trees
Date: 2019-11-29 00:00:00

Phenotypic variability in a population of cells can work as the bet-hedging
of the cells under an unpredictably changing environment, the typical example
of which is the bacterial persistence. To understand the strategy to control
such phenomena, it is indispensable to identify the phenotype of each cell and
its inheritance. Although recent advancements in microfluidic technology offer
us useful lineage data, they are insufficient to directly identify the
phenotypes of the cells. An alternative approach is to infer the phenotype from
the lineage data by latent-variable estimation. To this end, however, we must
resolve the bias problem in the inference from lineage called survivorship
bias. In this work, we clarify how the survivor bias distorts statistical
estimations. We then propose a latent-variable estimation algorithm without the
survivorship bias from lineage trees based on an expectation-maximization (EM)
algorithm, which we call Lineage EM algorithm (LEM). LEM provides a statistical
method to identify the traits of the cells applicable to various kinds of
lineage data.
A Numerical Study of the Relationship Between Erectile Pressure and Shear Wave Speed of Corpus Cavernosa in Ultrasound Vibro-elastography
Date: 2018-06-21 00:00:00

The objective of this study was to investigate the relationship between
erectile pressure (EP) and shear wave speed of the corpus cavernosa obtained
via a specific ultrasound vibro-elastography (UVE) technique. This study builds
upon our prior investigation, in which UVE was used to evaluate the
viscoelastic properties of the corpus cavernosa in the flaccid and erect
states. A two-dimensional poroviscoelastic finite element model (FEM) was
developed to simulate wave propagation in the penile tissue according to our
experimental setup. Various levels of EP were applied to the corpus cavernosa,
and the relationship between shear wave speed in the corpus cavernosa and EP
was investigated. Results demonstrated non-linear, positive correlations
between shear wave speeds in the corpus cavernosa and increasing EP at
different vibration frequencies (100-200 Hz). These findings represent the
first report of the impact of EP on shear wave speed and validates the use of
UVE in the evaluation of men with erectile dysfunction. Further evaluations are
warranted to determine the clinical utility of this instrument in the diagnosis
and treatment of men with erectile dysfunction.