pwc-archive/papers-with-abstracts huggingface dataset:
Justifying the Adoption and Relevance of Inflation Targeting Framework: A Time-Varying Evidence from Ghana
Date: 2018-05-29 00:00:00

This paper scrutinizes the rationale for the adoption of inflation targeting
(IT) by Bank of Ghana in 2002. In this case, we determine the stability or
otherwise of the relationship between money supply and inflation in Ghana over
the period 1970M1-2016M3 using battery of econometric methods. The empirical
results show an unstable link between inflation and monetary growth in Ghana,
while the final state coefficient of inflation elasticity to money growth is
positive but statistically insignificant. We find that inflation elasticity to
monetary growth has continued to decline since the 1970s, showing a waning
impact of money growth on inflation in Ghana. Notably, there is also evidence
of negative inflation elasticity to monetary growth between 2001 and 2004,
lending support to the adoption of IT framework in Ghana in 2002. We emphasized
that the unprecedented 31-months of single-digit inflation (June 2010-December
2012), despite the observed inflationary shocks in 2010 and 2012, reinforces
the immense contribution of the IT framework in anchoring inflation
expectations, with better inflation outcomes and inflation variability in
Ghana. The paper therefore recommends the continuous pursuance and
strengthening of the IT framework in Ghana, as it embodies a more eclectic
approach to policy formulation and implementation.
Effects of forest fire severity on terrestrial carbon emission and ecosystem production in the Himalayan region, India
Authors: Srikanta Sannigrahi, Sandeep Bhatt, Shahid Rahmat, Virendra Rana, Suman Chakraborti
Date: 2018-05-27 00:00:00

Remote sensing techniques have been used effectively for measuring the overall loss of terrestrial ecosystem production and biodiversity due to the forest fire. The current research focuses on assessing the impact of forest fire severity on terrestrial ecosystem productivity using different burn indices in Uttarakhand, India. Satellite-based land surface temperature (LST) was calculated for pre-fire (2014) and fire (2016) year using MODerate Resolution Imaging Spectroradiometer (MODIS) to identify the burn area hotspots across all eco-regions in Uttarakhand. In this study, spatial and temporal changes of different vegetation and burn area indices i.e Normalized Burn Ratio (NBR), Burnt Area Index (BAI), Normalized Multiband Drought Index (NMDI), Soil Adjusted Vegetation Index (SAVI), Global Environmental Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI)were estimated for both pre-fire and fire years. Additionally, two Light Use Efficiency (LUE) models i.e Carnegie- Ames-Stanford-Approach (CASA) and Vegetation Photosynthesis Model (VPM) were selected to quantify the terrestrial Net Primary Productivity (NPP) in pre-fire and fire years across all biomes of the study area.The present approach appears to be promising and has a potential in quantifying the loss of ecosystem productivity due to forest fires. A detailed field observation data is required for further training, and testing of remotely sensed fire maps for future research.
Towards Lipreading Sentences with Active Appearance Models
Date: 2018-05-29 00:00:00
Tasks: Audio-Visual Speech Recognition, Lipreading, speech-recognition, Speech Recognition, Visual Speech Recognition

Automatic lipreading has major potential impact for speech recognition,
supplementing and complementing the acoustic modality. Most attempts at
lipreading have been performed on small vocabulary tasks, due to a shortfall of
appropriate audio-visual datasets. In this work we use the publicly available
TCD-TIMIT database, designed for large vocabulary continuous audio-visual
speech recognition. We compare the viseme recognition performance of the most
widely used features for lipreading, Discrete Cosine Transform (DCT) and Active
Appearance Models (AAM), in a traditional Hidden Markov Model (HMM) framework.
We also exploit recent advances in AAM fitting. We found the DCT to outperform
AAM by more than 6% for a viseme recognition task with 56 speakers. The overall
accuracy of the DCT is quite low (32-34%). We conclude that a fundamental
rethink of the modelling of visual features may be needed for this task.
The VIREO KIS at VBS 2018
Authors: Nguyen Phuong Anh, Lu Yi-Jie, Zhang Hao, Ngo Chong-Wah
Date: 2018-05-30 00:00:00

This short paper presents the video browsing tool of VIREO team which has
been used in the Video Browser Showdown 2018. All added functions in the final
version are introduced and experiences gained from the benchmark are also
shared.
The Effects of Climate Change on Predator-Prey Dynamics
Date: 2018-05-30 00:00:00

In this investigation we study the effects of climate change on predator-prey
population dynamics. The interaction of predator and prey is described by the
Variable Territory (VT) model with Allee effects in an Ordinary Differential
Equation (ODE) framework. Climate influence is modeled in this ODE framework as
an exogenous driver which affects the prey growth rate. Mathematically, this
exogenous driver is a function of time, the climate function, that describes
how favorable is the climate, on a scale from $-1$ to $+1$. Climate change has
a number of effects; in this work, we focus on changes in climate variability,
in particular, a decrease in the switching rate between good years and bad.
With regard to the predator-prey populations, we focus on the conditions that
lead to extinction. We developed a high performance software tool to allow the
systematic exploration of a wide range of climate scenarios at a high level of
detail. Our results show that the predator-prey system is sensitive to the
rapid drop in growth rate that accompanies a switch from good growth years to
bad growth years. The amplitude of this negative variation as well as its
duration are key factors, but more importantly, it is the moment when this
negative change occurs during the predator-prey cycle that is critical for the
survival of the species.
Mortality/longevity Risk-Minimization with or without securitization
Date: 2018-05-30 00:00:00

This paper addresses the risk-minimization problem, with and without
mortality securitization, \`a la F\"ollmer-Sondermann for a large class of
equity-linked mortality contracts when no model for the death time is
specified. This framework includes the situation where the correlation between
the market model and the time of death is arbitrary general, and hence leads to
the case of a market model where there are two levels of information. The
public information which is generated by the financial assets, and a larger
flow of information that contains additional knowledge about a death time of an
insured. By enlarging the filtration, the death uncertainty and its entailed
risk are fully considered without any mathematical restriction.
  Our key tool lies in our optional martingale representation that states that
any martingale in the large filtration stopped at the death time can be
decomposed into precise orthogonal local martingales. This allows us to derive
the dynamics of the value processes of the mortality/longevity securities used
for the securitization, and to decompose any mortality/longevity liability into
the sum of orthogonal risks by means of a risk basis. The first main
contribution of this paper resides in quantifying, as explicit as possible, the
effect of mortality uncertainty on the risk-minimizing strategy by determining
the optimal strategy in the enlarged filtration in terms of strategies in the
smaller filtration. Our second main contribution consists of finding
risk-minimizing strategies with insurance securitization by investing in stocks
and one (or more) mortality/longevity derivatives such as longevity bonds. This
generalizes the existing literature on risk-minimization using mortality
securitization in many directions.
On the choice of metric in gradient-based theories of brain function
Date: 2018-12-21 00:00:00

The idea that the brain functions so as to minimize certain costs pervades
theoretical neuroscience. Since a cost function by itself does not predict how
the brain finds its minima, additional assumptions about the optimization
method need to be made to predict the dynamics of physiological quantities. In
this context, steepest descent (also called gradient descent) is often
suggested as an algorithmic principle of optimization potentially implemented
by the brain. In practice, researchers often consider the vector of partial
derivatives as the gradient. However, the definition of the gradient and the
notion of a steepest direction depend on the choice of a metric. Since the
choice of the metric involves a large number of degrees of freedom, the
predictive power of models that are based on gradient descent must be called
into question, unless there are strong constraints on the choice of the metric.
Here we provide a didactic review of the mathematics of gradient descent,
illustrate common pitfalls of using gradient descent as a principle of brain
function with examples from the literature and propose ways forward to
constrain the metric.
Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations
Authors: Kowald Dominik
Date: 2018-05-30 00:00:00
Tasks: Collaborative Filtering, Descriptive, Recommendation Systems, TAG

With the emergence of Web 2.0, tag recommenders have become important tools,
which aim to support users in finding descriptive tags for their bookmarked
resources. Although current algorithms provide good results in terms of tag
prediction accuracy, they are often designed in a data-driven way and thus,
lack a thorough understanding of the cognitive processes that play a role when
people assign tags to resources. This thesis aims at modeling these cognitive
dynamics in social tagging in order to improve tag recommendations and to
better understand the underlying processes.
  As a first attempt in this direction, we have implemented an interplay
between individual micro-level (e.g., categorizing resources or temporal
dynamics) and collective macro-level (e.g., imitating other users' tags)
processes in the form of a novel tag recommender algorithm. The preliminary
results for datasets gathered from BibSonomy, CiteULike and Delicious show that
our proposed approach can outperform current state-of-the-art algorithms, such
as Collaborative Filtering, FolkRank or Pairwise Interaction Tensor
Factorization. We conclude that recommender systems can be improved by
incorporating related principles of human cognition.
How do public research labs use funding for research? A case study
Date: 2018-05-25 00:00:00
Tasks: Management

This paper discusses how public research organizations consume funding for
research, applying a new approach based on economic metabolism of research
labs, in a broad analogy with biology. This approach is applied to a case study
in Europe represented by one of the biggest European public research
organizations, the National Research council of Italy. Results suggest that
funding for research (state subsidy and public contracts) of this public
research organization is mainly consumed for the cost of personnel. In
addition, the analysis shows a disproportionate growth of the cost of personnel
in public research labs in comparison with total revenue from government. In
the presence of shrinking public research lab budgets, this organizational
behavior generates inefficiencies and stress. R&D management and public policy
implications are suggested for improving economic performance of public
research organizations in turbulent markets.
Two-stage Method for Millimeter Wave Channel Estimation
Date: 2018-05-30 00:00:00
Tasks: Vocal Bursts Valence Prediction

The millimeter wave is a promising technique for the next generation of
mobile communication. The large antenna array is able to provide sufficient
precoding gain to overcome the high pathloss at millimeter wave band. However,
the accurate channel state information is the key for the precoding design.
Unfortunately, the channel use overhead and complexity are two major challenges
when estimating the channel with high-dimensional array. In this paper, we
propose a two-stage approach which reduces the channel use overhead and the
computational complexity. Specifically, in the first stage, we estimate the
column subspace of the channel matrix. Based on the estimated column subspace,
we design the training sounders to acquire the remaining coefficient matrix of
the column subspace. By dividing the estimation task into two stages, the
training sounders for the second stages are only targeted for the column
subspace, which will save the channel uses and the computational complexity as
well.
A New Model for Pricing Collateralized Financial Derivatives
Date: 2018-05-29 00:00:00
Tasks: model

This paper presents a new model for pricing financial derivatives subject to
collateralization. It allows for collateral arrangements adhering to bankruptcy
laws. As such, the model can back out the market price of a collateralized
contract. This framework is very useful for valuing outstanding derivatives.
Using a unique dataset, we find empirical evidence that credit risk alone is
not overly important in determining credit-related spreads. Only accounting for
both collateral posting and credit risk can sufficiently explain unsecured
credit costs. This finding suggests that failure to properly account for
collateralization may result in significant mispricing of derivatives. We also
empirically gauge the impact of collateral agreements on risk measurements. Our
findings indicate that there are important interactions between market and
credit risk.
Reference-free Calibration in Sensor Networks
Authors: Rajan Raj Thilak, Schaijk Rob-van, Das Anup, Romme Jac, Pasveer Frank
Date: 2018-05-30 00:00:00

Sensor calibration is one of the fundamental challenges in large-scale IoT
networks. In this article, we address the challenge of reference-free
calibration of a densely deployed sensor network. Conventionally, to calibrate
an in-place sensor network (or sensor array), a reference is arbitrarily chosen
with or without prior information on sensor performance. However, an arbitrary
selection of a reference could prove fatal, if an erroneous sensor is
inadvertently chosen. To avert single point of dependence, and to improve
estimator performance, we propose unbiased reference-free algorithms. Although,
our focus is on reference-free solutions, the proposed framework, allows the
incorporation of additional references, if available. We show with the help of
simulations that the proposed solutions achieve the derived statistical lower
bounds asymptotically. In addition, the proposed algorithms show improvements
on real-life datasets, as compared to prevalent algorithms.
Combined MEG and fMRI Exponential Random Graph Modeling for inferring functional Brain Connectivity
Date: 2018-05-23 00:00:00

Estimated connectomes by the means of neuroimaging techniques have enriched
our knowledge of the organizational properties of the brain leading to the
development of network-based clinical diagnostics. Unfortunately, to date, many
of those network-based clinical diagnostics tools, based on the mere
description of isolated instances of observed connectomes are noisy estimates
of the true connectivity network. Modeling brain connectivity networks is
therefore important to better explain the functional organization of the brain
and allow inference of specific brain properties. In this report, we present
pilot results on the modeling of combined MEG and fMRI neuroimaging data
acquired during an n-back memory task experiment. We adopted a pooled
Exponential Random Graph Model (ERGM) as a network statistical model to capture
the underlying process in functional brain networks of 9 subjects MEG and fMRI
data out of 32 during a 0-back vs 2-back memory task experiment. Our results
suggested strong evidence that all the functional connectomes of the 9 subjects
have small world properties. A group level comparison using comparing the
conditions pairwise showed no significant difference in the functional
connectomes across the subjects. Our pooled ERGMs successfully reproduced
important brain properties such as functional segregation and functional
integration. However, the ERGMs reproducing the functional segregation of the
brain networks discriminated between the 0-back and 2-back conditions while the
models reproducing both properties failed to successfully discriminate between
both conditions. Our results are promising and would improve in robustness with
a larger sample size. Nevertheless, our pilot results tend to support previous
findings that functional segregation and integration are sufficient to
statistically reproduce the main properties of brain network.
The effect of prudence on the optimal allocation in possibilistic and mixed models
Date: 2018-05-15 00:00:00

In this paper two portfolio choice models are studied: a purely possibilistic
model, in which the return of a risky asset is a fuzzy number, and a mixed
model in which a probabilistic background risk is added. For the two models an
approximate formula of the optimal allocation is computed, with respect to the
possibilistic moments associated with fuzzy numbers and the indicators of the
investor risk preferences (risk aversion, prudence).
Unravelling Airbnb Predicting Price for New Listing
Date: 2018-05-25 00:00:00

This paper analyzes Airbnb listings in the city of San Francisco to better
understand how different attributes such as bedrooms, location, house type
amongst others can be used to accurately predict the price of a new listing
that optimal in terms of the host's profitability yet affordable to their
guests. This model is intended to be helpful to the internal pricing tools that
Airbnb provides to its hosts. Furthermore, additional analysis is performed to
ascertain the likelihood of a listings availability for potential guests to
consider while making a booking. The analysis begins with exploring and
examining the data to make necessary transformations that can be conducive for
a better understanding of the problem at large while helping us make
hypothesis. Moving further, machine learning models are built that are
intuitive to use to validate the hypothesis on pricing and availability and run
experiments in that context to arrive at a viable solution. The paper then
concludes with a discussion on the business implications, associated risks and
future scope.
A Physical Review on Currency
Date: 2018-04-18 00:00:00

A theoretical self-sustainable economic model is established based on the
fundamental factors of production, consumption, reservation and reinvestment,
where currency is set as a unconditional credit symbol serving as transaction
equivalent and stock means. Principle properties of currency are explored in
this ideal economic system. Physical analysis reveals some facts that were not
addressed by traditional monetary theory, and several basic principles of ideal
currency are concluded: 1. The saving-replacement is a more primary function of
currency than the transaction equivalents; 2. The ideal efficiency of currency
corresponds to the least practical value; 3. The contradiction between constant
face value of currency and depreciable goods leads to intrinsic inflation.
Are Biotechnology Startups Different?
Date: 2018-04-13 00:00:00

In the domain of technology startups, biotechnology has often been considered
as specific. Their unique technology content, the type of founders and managers
they have, the amount of venture capital they raise, the time it takes them to
reach an exit as well as the technology clusters they belong to are seen as
such unique features. Based on extensive research from new databases, the
author claims that the biotechnology startups are not as different as it might
have been claimed: the amount of venture capital raised, the time to exit,
their geography are indeed similar and even their equity structure to founders
and managers have similarities. The differences still exist, for example the
experience of the founders, the revenue and profit level at exit.
Critical factors and enablers of food quality and safety compliance risk management in the Vietnamese seafood supply chain
Date: 2018-04-17 00:00:00
Tasks: Management

Recently, along with the emergence of food scandals, food supply chains have
to face with ever-increasing pressure from compliance with food quality and
safety regulations and standards. This paper aims to explore critical factors
of compliance risk in food supply chain with an illustrated case in Vietnamese
seafood industry. To this end, this study takes advantage of both primary and
secondary data sources through a comprehensive literature research of
industrial and scientific papers, combined with expert interview. Findings
showed that there are three main critical factor groups influencing on
compliance risk including challenges originating from Vietnamese food supply
chain itself, characteristics of regulation and standards, and business
environment. Furthermore, author proposed enablers to eliminate compliance
risks to food supply chain managers as well as recommendations to government
and other influencers and supporters.
A Data-Driven Approach for Modeling Stochasticity in Oil Market
Date: 2018-04-24 00:00:00
Tasks: Time Series, Time Series Analysis

Global oil price is an important factor in determining many economic
variables in the world's economy. It is generally modeled as a stochastic
process and have been studied through different techniques by comparing the
historic time series of demand, supply and the price itself. However, there are
many historic events where the demand or supply changes are not sufficient in
explaining the price changes. In such cases, it is the expectations on the
future changes of demand or supply that causes heavy and quick influences on
the price. There are many parameters and variables that shape these
expectations, and are usually neglected in traditional models. In this paper,
we have proposed a model based on System Dynamics approach that takes into
account these non-traditional factors. The validity of the proposed model is
then evaluated using real and potential scenarios in which the proposed model
follows the trend of the real data.
One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level
Authors: Collins Andrew, Tkaczyk Dominika, Beel Joeran
Date: 2018-11-30 00:00:00
Tasks: Meta-Learning, Recommendation Systems

The effectiveness of recommendation algorithms is typically assessed with
evaluation metrics such as root mean square error, F1, or click through rates,
calculated over entire datasets. The best algorithm is typically chosen based
on these overall metrics. However, there is no single-best algorithm for all
users, items, and contexts. Choosing a single algorithm based on overall
evaluation results is not optimal. In this paper, we propose a
meta-learning-based approach to recommendation, which aims to select the best
algorithm for each user-item pair. We evaluate our approach using the MovieLens
100K and 1M datasets. Our approach (RMSE, 100K: 0.973; 1M: 0.908) did not
outperform the single-best algorithm, SVD++ (RMSE, 100K: 0.942; 1M: 0.887). We
also develop a distinction between meta-learners that operate per-instance
(micro-level), per-data subset (mid-level), and per-dataset (global level). Our
evaluation shows that a hypothetically perfect micro-level meta-learner would
improve RMSE by 25.5% for the MovieLens 100K and 1M datasets, compared to the
overall-best algorithms used.