pwc-archive/papers-with-abstracts huggingface dataset:
Implications of macroeconomic volatility in the Euro area
Date: 2018-06-28 00:00:00

In this paper we estimate a Bayesian vector autoregressive model with factor
stochastic volatility in the error term to assess the effects of an uncertainty
shock in the Euro area. This allows us to treat macroeconomic uncertainty as a
latent quantity during estimation. Only a limited number of contributions to
the literature estimate uncertainty and its macroeconomic consequences jointly,
and most are based on single country models. We analyze the special case of a
shock restricted to the Euro area, where member states are highly related by
construction. We find significant results of a decrease in real activity for
all countries over a period of roughly a year following an uncertainty shock.
Moreover, equity prices, short-term interest rates and exports tend to decline,
while unemployment levels increase. Dynamic responses across countries differ
slightly in magnitude and duration, with Ireland, Slovakia and Greece
exhibiting different reactions for some macroeconomic fundamentals.
Modeling the number of hidden events subject to observation delay
Date: 2019-03-26 00:00:00

This paper considers the problem of predicting the number of events that have
occurred in the past, but which are not yet observed due to a delay. Such
delayed events are relevant in predicting the future cost of warranties,
pricing maintenance contracts, determining the number of unreported claims in
insurance and in modeling the outbreak of diseases. Disregarding these
unobserved events results in a systematic underestimation of the event
occurrence process. Our approach puts emphasis on modeling the time between the
occurrence and observation of the event, the so-called observation delay. We
propose a granular model for the heterogeneity in this observation delay based
on the occurrence day of the event and on calendar day effects in the
observation process, such as weekday and holiday effects. We illustrate this
approach on a European general liability insurance data set where the
occurrence of an accident is reported to the insurer with delay.
INtERAcT: Interaction Network Inference from Vector Representations of Words
Date: 2018-04-16 00:00:00
Tasks: Articles, text annotation

In recent years, the number of biomedical publications has steadfastly grown,
resulting in a rich source of untapped new knowledge. Most biomedical facts are
however not readily available, but buried in the form of unstructured text, and
hence their exploitation requires the time-consuming manual curation of
published articles. Here we present INtERAcT, a novel approach to extract
protein-protein interactions from a corpus of biomedical articles related to a
broad range of scientific domains in a completely unsupervised way. INtERAcT
exploits vector representation of words, computed on a corpus of domain
specific knowledge, and implements a new metric that estimates an interaction
score between two molecules in the space where the corresponding words are
embedded. We demonstrate the power of INtERAcT by reconstructing the molecular
pathways associated to 10 different cancer types using a corpus of
disease-specific articles for each cancer type. We evaluate INtERAcT using
STRING database as a benchmark, and show that our metric outperforms currently
adopted approaches for similarity computation at the task of identifying known
molecular interactions in all studied cancer types. Furthermore, our approach
does not require text annotation, manual curation or the definition of semantic
rules based on expert knowledge, and hence it can be easily and efficiently
applied to different scientific domains. Our findings suggest that INtERAcT may
increase our capability to summarize the understanding of a specific disease
using the published literature in an automated and completely unsupervised
fashion.
Modeling of gap gene regulatory networks in Drosophila with the account of the gene modular structure (in Russian)
Date: 2018-01-10 00:00:00

Genes are frequently regulated in complex manners, necessitating modelling
approaches which go beyond simple (linear) gene-to-gene interactions and
address the modularity of cis-regulatory regions and alternate transcription
initiation sites. In particular, sharp expression patterns (peaks or stripes)
indicate that gene regulation involves nonlinear transcription factor kinetics
(beyond first-order). We propose a methodology for approaching this problem,
using the example of the multiple cis-regulatory modules (CRMs) and two
transcripts (P1 and P2) found in the Drosophila hunchback (hb) and (CD1 and
CD2)in Kruppel (Kr) genes, the genes expressed along the anteroposterior axis
of the embryo. The positional characteristics of the mRNA expression patterns
are studied for their sensitivity to the variation of the input factors and
initial conditions.
Improved linear direct solution for asynchronous radio network localization (RNL)
Date: 2018-01-10 00:00:00
Tasks: Position

In the field of localization the linear least square solution is frequently
used. This solution is compared to nonlinear solvers more effected by noise,
but able to provide a position estimation without the knowledge of any starting
condition. The linear least square solution is able to minimize Gaussian noise
by solving an overdetermined equation with the MoorePenrose pseudoinverse.
Unfortunately this solution fails if it comes to non Gaussian noise. This
publication presents a direct solution which is able to use prefiltered data
for the LPM (RNL) equation. The used input for the linear position estimation
will not be the raw data but over the time filtered data, for this reason this
solution will be called direct solution. It will be shown that the presented
symmetrical direct solution is superior to non symmetrical direct solution and
especially to the not prefiltered linear least square solution.
DeepSeek: Content Based Image Search & Retrieval
Authors: Piplani Tanya, Bamman David
Date: 2018-01-11 00:00:00
Tasks: Deep Learning, Image Captioning, Image Retrieval, Machine Translation, Retrieval, Translation

Most of the internet today is composed of digital media that includes videos
and images. With pixels becoming the currency in which most transactions happen
on the internet, it is becoming increasingly important to have a way of
browsing through this ocean of information with relative ease. YouTube has 400
hours of video uploaded every minute and many million images are browsed on
Instagram, Facebook, etc. Inspired by recent advances in the field of deep
learning and success that it has gained on various problems like image
captioning and, machine translation , word2vec , skip thoughts, etc, we present
DeepSeek a natural language processing based deep learning model that allows
users to enter a description of the kind of images that they want to search,
and in response the system retrieves all the images that semantically and
contextually relate to the query. Two approaches are described in the following
sections.
Assessment of SFSDP Cooperative Localization Algorithm for WLAN Environment
Date: 2018-01-10 00:00:00

Cooperative localization for indoor WiFi networks have received little
attention thus far. Many cooperative location algorithms exist for Wireless
Sensor Network Applications but their suitability for WiFi based networks has
not been studied. In this paper the performance of the Sparse Finite Semi
Definite Program (SFSDP) has been examined using real measurements data and
under different indoor conditions. Effects of other network parameters such as
varying number of anchors and blind nodes are also included.
Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints
Date: 2018-01-10 00:00:00
Tasks: compressed sensing

Objective: The purpose of this manuscript is to accelerate cardiac diffusion
tensor imaging (CDTI) by integrating low-rankness and compressed sensing.
Methods: Diffusion-weighted images exhibit both transform sparsity and
low-rankness. These properties can jointly be exploited to accelerate CDTI,
especially when a phase map is applied to correct for the phase inconsistency
across diffusion directions, thereby enhancing low-rankness. The proposed
method is evaluated both ex vivo and in vivo, and is compared to methods using
either a low-rank or sparsity constraint alone. Results: Compared to using a
low-rank or sparsity constraint alone, the proposed method preserves more
accurate helix angle features, the transmural continuum across the myocardium
wall, and mean diffusivity at higher acceleration, while yielding significantly
lower bias and higher intraclass correlation coefficient. Conclusion:
Low-rankness and compressed sensing together facilitate acceleration for both
ex vivo and in vivo CDTI, improving reconstruction accuracy compared to
employing either constraint alone. Significance: Compared to previous methods
for accelerating CDTI, the proposed method has the potential to reach higher
acceleration while preserving myofiber architecture features which may allow
more spatial coverage, higher spatial resolution and shorter temporal footprint
in the future.
Robust martingale selection problem and its connections to the no-arbitrage theory
Date: 2018-11-23 00:00:00

We analyze the martingale selection problem of Rokhlin (2006) in a pointwise
(robust) setting. We derive conditions for solvability of this problem and show
how it is related to the classical no-arbitrage deliberations. We obtain
versions of the Fundamental Theorem of Asset Pricing in examples spanning
frictionless markets, models with proportional transaction costs and also
models for illiquid markets. In all these examples, we also incorporate trading
constraints.
Mosaicked multispectral image compression based on inter- and intra-band correlation
Date: 2018-01-10 00:00:00
Tasks: Image Compression

Multispectral imaging has been utilized in many fields, but the cost of
capturing and storing image data is still high. Single-sensor cameras with
multispectral filter arrays can reduce the cost of capturing images at the
expense of slightly lower image quality. When multispectral filter arrays are
used, conventional multispectral image compression methods can be applied after
interpolation, but the compressed image data after interpolation has some
redundancy because the interpolated data are computed from the captured raw
data. In this paper, we propose an efficient image compression method for
single-sensor multispectral cameras. The proposed method encodes the captured
multispectral data before interpolation. We also propose a new spectral
transform method for the compression of mosaicked multispectral images. This
transform is designed by considering the filter arrangement and the spectral
sensitivities of a multispectral filter array. The experimental results show
that the proposed method achieves a higher peak signal-to-noise ratio at higher
bit rates than a conventional compression method that encodes a multispectral
image after interpolation, e.g., 3-dB gain over conventional compression when
coding at rates of over 0.1 bit/pixel/bands.
Applying Vector Space Model (VSM) Techniques in Information Retrieval for Arabic Language
Authors: Abu-Salih Bilal
Date: 2018-01-15 00:00:00
Tasks: Information Retrieval, Management, Retrieval, valid

Information Retrieval (IR) allows the storage, management, processing and
retrieval of information, documents, websites, etc. Building an IR system for
any language is imperative. This is evident through the massive conducted
efforts to build IR systems using any of its models that are valid for certain
languages. This report presents an implementation for a core IR technique which
is Vector Space Model (VSM). We have chosen VSM model for our project since it
is a term weighting scheme, and the retrieved documents could be sorted
according to their relevancy degree. One other significant feature for such
technique is the ability to get a relevance feedback from the users of the
system; users can judge whether the retrieved document is relative to their
need or not. The developed system has been validated through building an Arabic
IR website using server side scripting. The experiments verifies the
effectiveness of our system to apply all techniques of vector space model and
valid over Arabic language.
A Linear Solution Method of Generalized Robust Chance Constrained Real-time Dispatch
Date: 2018-01-11 00:00:00

In this letter, a novel solution method of generalized robust chance
constrained real-time dispatch (GRCC-RTD) considering wind power uncertainty is
proposed. GRCC models are advantageous in dealing with distributional
uncertainty, however, they are difficult to solve because of the complex
ambiguity set. By constructing traceable counterparts of the robust chance
constraints and using the reformulation linearization technique, the model is
equivalently transformed into a deterministic linear programming problem, which
can be solved efficiently by off-the-shelf solvers. Numerical results verify
the effectiveness and efficiency of the approach.
Is there a housing bubble in China
Date: 2018-01-11 00:00:00
Tasks: Diagnostic

There is a growing concern in recent years over the potential formation of
bubbles in the Chinese real estate market. This paper aims to conduct a series
of bubble diagnostic analysis over nine representative Chinese cities from two
aspects. First, we investigate whether the prices had been significantly
deviating from economic fundamentals by applying a standard Engle-Granger
cointegration test. Second, we apply the Log-Periodic-Power-Law-Singularity
(LPPLS) model to detect whether there is any evidence of unsustainable,
self-reinforcing speculative behaviours amongst the price series. We propose
that, given the heterogeneity that exists amongst cities with different types
of bubble signatures, it is vital to conduct bubble diagnostic tests and
implement relevant policies toward specific bubble characteristics, rather than
enforcing one-that-fits-for-all type policy that does not take into account
such heterogeneity.
The time interpretation of expected utility theory
Date: 2018-01-11 00:00:00
Tasks: Decision Making

Decision theory is the model of individual human behavior employed by
neoclassical economics. Built on this model of individual behavior are models
of aggregate behavior that feed into models of macroeconomics and inform
economic policy. Neoclassical economics has been fiercely criticized for
failing to make meaningful predictions of individual and aggregate behavior,
and as a consequence has been accused of misguiding economic policy. We
identify as the Achilles heel of the formalism its least constrained component,
namely the concept of utility. This concept was introduced as an additional
degree of freedom in the 18th century when it was noticed that previous models
of decision-making failed in many realistic situations. At the time, only
pre-18th century mathematics was available, and a fundamental solution of the
problems was impossible. We re-visit the basic problem and resolve it using
modern techniques, developed in the late 19th and throughout the 20th century.
From this perspective utility functions do not appear as (irrational)
psychological re- weightings of monetary amounts but as non-linear
transformations that define ergodic observables on non-ergodic growth
processes. As a consequence we are able to interpret different utility
functions as encoding different non-ergodic dynamics and remove the element of
human irrationality from the explanation of basic economic behavior. Special
cases were treated in [1]. Here we develop the theory for general utility
functions.
A Critical-like Collective State Leads to Long-range Cell Communication in Dictyostelium discoideum Aggregation
Date: 2018-01-11 00:00:00

The transition from single-cell to multicellular behavior is important in
early development but rarely studied. The starvation-induced aggregation of the
social amoeba Dictyostelium discoideum into a multicellular slug is known to
result from single-cell chemotaxis towards emitted pulses of cyclic adenosine
monophosphate (cAMP). However, how exactly do transient short-range chemical
gradients lead to coherent collective movement at a macroscopic scale? Here, we
use a multiscale model verified by quantitative microscopy to describe
wide-ranging behaviors from chemotaxis and excitability of individual cells to
aggregation of thousands of cells. To better understand the mechanism of
long-range cell-cell communication and hence aggregation, we analyze cell-cell
correlations, showing evidence for self-organization at the onset of
aggregation (as opposed to following a leader cell). Surprisingly, cell
collectives, despite their finite size, show features of criticality known from
phase transitions in physical systems. Application of external cAMP
perturbations in our simulations near the sensitive critical point allows
steering cells into early aggregation and towards certain locations but not
once an aggregation center has been chosen.
Viable Insider Markets
Date: 2018-01-11 00:00:00

We consider the problem of optimal inside portfolio $\pi(t)$ in a financial
market with a corresponding wealth process $X(t)=X^{\pi}(t)$ modelled by
\begin{align}\label{eq0.1} \begin{cases}
dX(t)&=\pi(t)X(t)[\alpha(t)dt+\beta(t)dB(t)]; \quad t\in[0, T] X(0)&=x_0>0,
\end{cases} \end{align} where $B(\cdot)$ is a Brownian motion. We assume that
the insider at time $t$ has access to market information $\varepsilon_t>0$
units ahead of time, in addition to the history of the market up to time $t$.
The problem is to find an insider portfolio $\pi^{*}$ which maximizes the
expected logarithmic utility $J(\pi)$ of the terminal wealth, i.e. such that
$$\sup_{\pi}J(\pi)= J(\pi^{*}), \text {where } J(\pi)=
\mathbb{E}[\log(X^{\pi}(T))].$$ The insider market is called \emph{viable} if
this value is finite. We study under what inside information flow $\mathbb{H}$
the insider market is viable or not. For example, assume that for all $t<T$ the
insider knows the value of $B(t+\epsilon_t)$, where $t + \epsilon_t \geq T$
converges monotonically to $T$ from above as $t$ goes to $T$ from below. Then
(assuming that the insider has a perfect memory) at time $t$ she has the inside
information $\mathcal{H}_t$, consisting of the history $\mathcal{F}_t$ of
$B(s); 0 \leq s \leq t$ plus all the values of Brownian motion in the interval
$[t+\epsilon_t, \epsilon_0]$, i.e. we have the enlarged filtration
\begin{equation}\label{eq0.2} \mathbb{H}=\{\mathcal{H}_t\}_{t\in[0.T]},\quad
\mathcal{H}_t=\mathcal{F}_t\vee\sigma(B(t+\epsilon_t+r),0\leq r \leq
\epsilon_0-t-\epsilon_t), \forall t\in [0,T]. \end{equation} Using forward
integrals, Hida-Malliavin calculus and Donsker delta functionals we show that
if $$\int_0^T\frac{1}{\varepsilon_t}dt=\infty,$$ then the insider market is not
viable.
A path-integral formulation of the run and tumble motion and chemotaxis in Escherichia coli
Date: 2018-02-23 00:00:00

Bacteria such as Escherichia coli move about in a series of runs and tumbles:
while a run state (straight motion) entails all the flagellar motors spinning
in counterclockwise mode, a tumble is caused by a shift in the state of one or
more motors to clockwise spinning mode. In the presence of an attractant
gradient in the environment, runs in the favourable direction are extended, and
this results in a net drift of the organism in the direction of the gradient.
The underlying signal transduction mechanism produces directed motion through a
bi-lobed response function which relates the clockwise bias of the flagellar
motor to temporal changes in the attractant concentration. The two lobes
(positive and negative) of the response function are separated by a time
interval of $\sim 1$s, such that the bacterium effectively compares the
concentration at two different positions in space and responds accordingly. We
present here a novel path-integral method which allows us to address this
problem in the most general way possible, including multi-step CW-CCW
transitions, directional persistence and power-law waiting time distributions.
The method allows us to calculate quantities such as the effective diffusion
coefficient and drift velocity, in a power series expansion in the attractant
gradient. Explicit results in the lowest order in the expansion are presented
for specific models, which, wherever applicable, agree with the known results.
New results for gamma-distributed run interval distributions are also
presented.
Direction of Arrival with One Microphone, a few LEGOs, and Non-Negative Matrix Factorization
Date: 2018-08-28 00:00:00
Tasks: Sound Source Localization

Conventional approaches to sound source localization require at least two
microphones. It is known, however, that people with unilateral hearing loss can
also localize sounds. Monaural localization is possible thanks to the
scattering by the head, though it hinges on learning the spectra of the various
sources. We take inspiration from this human ability to propose algorithms for
accurate sound source localization using a single microphone embedded in an
arbitrary scattering structure. The structure modifies the frequency response
of the microphone in a direction-dependent way giving each direction a
signature. While knowing those signatures is sufficient to localize sources of
white noise, localizing speech is much more challenging: it is an ill-posed
inverse problem which we regularize by prior knowledge in the form of learned
non-negative dictionaries. We demonstrate a monaural speech localization
algorithm based on non-negative matrix factorization that does not depend on
sophisticated, designed scatterers. In fact, we show experimental results with
ad hoc scatterers made of LEGO bricks. Even with these rudimentary structures
we can accurately localize arbitrary speakers; that is, we do not need to learn
the dictionary for the particular speaker to be localized. Finally, we discuss
multi-source localization and the related limitations of our approach.
The probabilities of trees and cladograms under Ford's $\alpha$-model
Date: 2018-01-11 00:00:00

We give correct explicit formulas for the probabilities of rooted binary
trees and cladograms under Ford's $\alpha$-model.
Enhancing Translation Language Models with Word Embedding for Information Retrieval
Authors: Frej Jibril  LIG, Chevallet Jean-Pierre  LGI - IMAG, Schwab Didier  UGA
Date: 2018-01-11 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Retrieval, Translation

In this paper, we explore the usage of Word Embedding semantic resources for
Information Retrieval (IR) task. This embedding, produced by a shallow neural
network, have been shown to catch semantic similarities between words (Mikolov
et al., 2013). Hence, our goal is to enhance IR Language Models by addressing
the term mismatch problem. To do so, we applied the model presented in the
paper Integrating and Evaluating Neural Word Embedding in Information Retrieval
by Zuccon et al. (2015) that proposes to estimate the translation probability
of a Translation Language Model using the cosine similarity between Word
Embedding. The results we obtained so far did not show a statistically
significant improvement compared to classical Language Model.