pwc-archive/papers-with-abstracts huggingface dataset:
Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
Authors: De Boom Cedric, Agrawal Rohan, Hansen Samantha, Kumar Esh, Yon Romain, Chen Ching-Wei, Demeester Thomas, Dhoedt Bart
Date: 2017-08-22 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

The amount of content on online music streaming platforms is immense, and
most users only access a tiny fraction of this content. Recommender systems are
the application of choice to open up the collection to these users.
Collaborative filtering has the disadvantage that it relies on explicit
ratings, which are often unavailable, and generally disregards the temporal
nature of music consumption. On the other hand, item co-occurrence algorithms,
such as the recently introduced word2vec-based recommenders, are typically left
without an effective user representation. In this paper, we present a new
approach to model users through recurrent neural networks by sequentially
processing consumed items, represented by any type of embeddings and other
context features. This way we obtain semantically rich user representations,
which capture a user's musical taste over time. Our experimental analysis on
large-scale user data shows that our model can be used to predict future songs
a user will likely listen to, both in the short and long term.
Unemployment: Study of Causes and Possible Solutions
Date: 2017-08-21 00:00:00

The following measures against unemployment are proposed: In the short term,
to promote greater income for the poorest sectors. It is shown that this can be
paid with the resulting increased production, without losing income to the
other economic agents. In the mid term, the creation of ad-hoc companies for
investment in projects profitable but long lasting. And in the long run, the
abandonment of the competitive models. As these proposals go against current
ideas (liberalisation, labour market flexibility, free market, etc.), the
statements are rigorously demonstrated, even at the risk of making the lecture
harder.
  Part 1 explores the problem and uses a simple model and others heuristic
arguments to create familiarity with macroeconomic models. Part 2 is a
simplified summary of Macroeconomic Theory textbook. It serves as a review to
the reader whose knowledge in economy are out of date, or as a first
approximation to the topic if he or she does not have them. In the light of the
theory, economic policies are evaluated for the Argentine case in the 90's. The
work accepts the Keynesian explanation of unemployment (insufficient demand),
but we disagree on its solution (public expenditure). Finally, in Part 3 we
elaborate and justify the proposals.
Risk and interaction aversion: screening mechanisms in the Prisoner's Dilemma game
Date: 2017-08-22 00:00:00

When the interactions between cooperators (C) and defectors (D) can be
partially avoided within a population, there may be an overall enhancement of
cooperation. One example of such screening mechanism occurs in the presence of
risk-averse agents (loners, L) that are neutral towards others, i.e., both L
and its opponent, whatever its strategy, receive the same payoff. Their
presence in the Prisoner's Dilemma (PD) game sustains the coexistence of
cooperators and defectors far beyond the level attained in their absence.
Another screening mechanism is a heterogeneous landscape obtained, for example,
by site diluting the lattice. In this case, cooperation is enhanced with some
fraction of such inactive, interaction-averse sites. By considering the
interplay of both mechanisms, we show that there is an explosive increase in
the range of densities, just above the percolation threshold, where neutrality
is prevented and loners become extinct, the behavior reverting to the pure PD
game. Interestingly, this occurs despite defectors being usually abundant in
that region. This has to be compared with the corresponding loner-free region
in the undiluted case that, besides being very small, is dominatedby
cooperators.
Systematic Noise: Micro-movements in Equity Options Markets
Date: 2017-08-23 00:00:00

Equity options are known to be notoriously difficult to price accurately, and
even with the development of established mathematical models there are many
assumptions that must be made about the underlying processes driving market
movements. As such, the theoretical prices outputted by these models are often
slightly different from the realized or actual market price. The choice of
model traders use can create many different valuations on the same asset, which
may lead to a form of systematic micro-movement or noise. The analysis in this
paper demonstrates that approximately 1.7%-4.5% of market volume for options
written on the SPY ETF within the last two years could potentially be due to
systematic noise.
Interplay of synergy and redundancy in diamond motif
Date: 2018-09-14 00:00:00

The formalism of partial information decomposition provides independent or
non-overlapping components constituting total information content provided by a
set of source variables about the target variable. These components are
recognised as unique information, synergistic information and, redundant
information. The metric of net synergy, conceived as the difference between
synergistic and redundant information, is capable of detecting synergy,
redundancy and, information independence among stochastic variables. And it can
be quantified, as it is done here, using appropriate combinations of different
Shannon mutual information terms. Utilisation of such a metric in network
motifs with the nodes representing different biochemical species, involved in
information sharing, uncovers rich store for interesting results. In the
current study, we make use of this formalism to obtain a comprehensive
understanding of the relative information processing mechanism in a diamond
motif and two of its sub-motifs namely bifurcation and integration motif
embedded within the diamond motif. The emerging patterns of synergy and
redundancy and their effective contribution towards ensuring high fidelity
information transmission are duly compared in the sub-motifs and independent
motifs (bifurcation and integration). In this context, the crucial roles played
by various time scales and activation coefficients in the network topologies
are especially emphasised. We show that the origin of synergy and redundancy in
information transmission can be physically justified by decomposing diamond
motif into bifurcation and integration motif.
Relationship between Remittances and Macroeconomic Variables in Times of Political and Social Upheaval: Evidence from Tunisia's Arab Spring
Date: 2017-07-28 00:00:00

If Tunisia was hailed as a success story with its high rankings on economic,
educational, and other indicators compared to other Arab countries, the 2011
popular uprisings demonstrate the need for political reforms but also major
economic reforms. The Arab spring highlights the fragility of its main economic
pillars including the tourism and the foreign direct investment. In such
turbulent times, the paper examines the economic impact of migrant'
remittances, expected to have a countercyclical behavior. Our results reveal
that prior to the Arab Spring, the impacts of remittances on growth and
consumption seem negative and positive respectively, while they varyingly
influence local investment. These three relationships held in the short-run. By
considering the period surrounding the 2011 uprisings, the investment effect of
remittances becomes negative and weak in the short-and medium-run, whereas
positive and strong remittances' impacts on growth and consumption are found in
the long term.
Multiscale virtual particle based elastic network model (MVP-ENM) for biomolecular normal mode analysis
Date: 2017-07-31 00:00:00

In this paper, a multiscale virtual particle based elastic network model
(MVP-ENM) is proposed for biomolecular normal mode analysis. The multiscale
virtual particle model is proposed for the discretization of biomolecular
density data in different scales. Essentially, the model works as the
coarse-graining of the biomolecular structure, so that a delicate balance
between biomolecular geometric representation and computational cost can be
achieved. To form "connections" between these multiscale virtual particles, a
new harmonic potential function, which considers the influence from both mass
distributions and distance relations, is adopted between any two virtual
particles. Unlike the previous ENMs that use a constant spring constant, a
particle-dependent spring parameter is used in MVP-ENM. Two independent models,
i.e., multiscale virtual particle based Gaussian network model (MVP-GNM) and
multiscale virtual particle based anisotropic network model (MVP-ANM), are
proposed. Even with a rather coarse grid and a low resolution, the MVP-GNM is
able to predict the Debye-Waller factors (B-factors) with considerable good
accuracy. Similar properties have also been observed in MVP-ANM. More
importantly, in B-factor predictions, the mismatch between the predicted
results and experimental ones is predominantly from higher fluctuation regions.
Further, it is found that MVP-ANM can deliver a very consistent low-frequency
eigenmodes in various scales. This demonstrates the great potential of MVP-ANM
in the deformation analysis of low resolution data. With the multiscale
rigidity function, the MVP-ENM can be applied to biomolecular data represented
in density distribution and atomic coordinates. Further, the great advantage of
my MVP-ENM model in computational cost has been demonstrated by using two
poliovirus virus structures. Finally, the paper ends with a conclusion.
Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds
Date: 2017-08-10 00:00:00
Tasks: Benchmarking

As the development of new classes of antibiotics slows, bacterial resistance
to existing antibiotics is becoming an increasing problem. A potential solution
is to develop treatment strategies with an alternative mode of action. We
consider one such strategy: anti-adhesion therapy. Whereas antibiotics act
directly upon bacteria, either killing them or inhibiting their growth,
anti-adhesion therapy impedes the binding of bacteria to host cells. This
prevents bacteria from deploying their arsenal of virulence mechanisms, while
simultaneously rendering them more susceptible to natural and artificial
clearance. In this paper, we consider a particular form of anti-adhesion
therapy, involving biomimetic multivalent adhesion molecule (MAM) 7 coupled
polystyrene microbeads, which competitively inhibit the binding of bacteria to
host cells. We develop a mathematical model, formulated as a system of ordinary
differential equations, to describe inhibitor treatment of a Pseudomonas
aeruginosa burn wound infection in the rat. Benchmarking our model against in
vivo data from an ongoing experimental programme, we use the model to explain
bacteria population dynamics and to predict the efficacy of a range of
treatment strategies, with the aim of improving treatment outcome. The model
consists of two physical compartments: the epithelium and the exudate. It is
found that, when effective in reducing the bacterial burden, inhibitor
treatment operates both by preventing bacteria from binding to the epithelium
and by reducing the flux of daughter cells from the epithelium into the
exudate. Our model predicts that inhibitor treatment cannot eliminate the
bacterial burden when used in isolation; however, when combined with regular or
continuous debridement of the exudate, elimination is theoretically possible.
Lastly, we present ways to improve therapeutic efficacy, as predicted by our
mathematical model.
Integrate Multi-omic Data Using Affinity Network Fusion (ANF) for Cancer Patient Clustering
Date: 2017-08-23 00:00:00
Tasks: Clustering, feature selection

Clustering cancer patients into subgroups and identifying cancer subtypes is
an important task in cancer genomics. Clustering based on comprehensive
multi-omic molecular profiling can often achieve better results than those
using a single data type, since each omic data type (representing one view of
patients) may contain complementary information. However, it is challenging to
integrate heterogeneous omic data types directly. Based on one popular method
-- Similarity Network Fusion (SNF), we presented Affinity Network Fusion (ANF)
in this paper, an "upgrade" of SNF with several advantages. Similar to SNF, ANF
treats each omic data type as one view of patients and learns a fused affinity
(transition) matrix for clustering. We applied ANF to a carefully processed
harmonized cancer dataset downloaded from GDC data portals consisting of 2193
patients, and generated promising results on clustering patients into correct
disease types. Our experimental results also demonstrated the power of feature
selection and transformation combined with using ANF in patient clustering.
Moreover, eigengap analysis suggests that the learned affinity matrices of four
cancer types using our proposed framework may have successfully captured
patient group structure and can be used for discovering unknown cancer
subtypes.
Evaluation Measures for Relevance and Credibility in Ranked Lists
Authors: Lioma Christina, Simonsen Jakob Grue, Larsen Birger
Date: 2017-08-23 00:00:00
Tasks: Information Retrieval, Retrieval

Recent discussions on alternative facts, fake news, and post truth politics
have motivated research on creating technologies that allow people not only to
access information, but also to assess the credibility of the information
presented to them by information retrieval systems. Whereas technology is in
place for filtering information according to relevance and/or credibility, no
single measure currently exists for evaluating the accuracy or precision (and
more generally effectiveness) of both the relevance and the credibility of
retrieved results. One obvious way of doing so is to measure relevance and
credibility effectiveness separately, and then consolidate the two measures
into one. There at least two problems with such an approach: (I) it is not
certain that the same criteria are applied to the evaluation of both relevance
and credibility (and applying different criteria introduces bias to the
evaluation); (II) many more and richer measures exist for assessing relevance
effectiveness than for assessing credibility effectiveness (hence risking
further bias).
  Motivated by the above, we present two novel types of evaluation measures
that are designed to measure the effectiveness of both relevance and
credibility in ranked lists of retrieval results. Experimental evaluation on a
small human-annotated dataset (that we make freely available to the research
community) shows that our measures are expressive and intuitive in their
interpretation.
Family Shopping Recommendation System Using User Profile and Behavior Data
Authors: Jiacheng Xu
Date: 2017-08-24 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

With the arrival of the big data era, recommendation system has been a hot
technology for enterprises to streamline their sales. Recommendation algorithms
for individual users have been extensively studied over the past decade. Most
existing recommendation systems also focus on individual user recommendations,
however in many daily activities, items are recommended to the groups not one
person. As an effective means to solve the problem of group recommendation
problem,we extend the single user recommendation to group recommendation.
Specifically we propose a novel approach for family-based shopping
recommendation system. We use the dataset from the real shopping mall
consisting of shopping records table, client-profile table and family
relationship table. Our algorithm integrates user behavior similarity and user
profile similarity to build the user based collaborative filtering model. We
evaluate our approach on a real-world shopping mall dataset.
Structural Identifiability of Cyclic Graphical Models of Biological Networks with Latent Variables
Date: 2017-08-24 00:00:00

An efficient structural identifiability analysis algorithm is developed in
this study for a broad range of network structures. The proposed method adopts
the Wright's path coefficient method to generate identifiability equations in
forms of symbolic polynomials, and then converts these symbolic equations to
binary matrices (called identifiability matrix). Several matrix operations are
introduced for identifiability matrix reduction with system equivalency
maintained. Based on the reduced identifiability matrices, the structural
identifiability of each parameter is determined. A number of benchmark models
are used to verify the validity of the proposed approach. Finally, the network
module for influenza A virus replication is employed as a real example to
illustrate the application of the proposed approach in practice. The proposed
approach can deal with cyclic networks with latent variables. The key advantage
is that it intentionally avoids symbolic computation and is thus highly
efficient. Also, this method is capable of determining the identifiability of
each single parameter and is thus of higher resolution in comparison with many
existing approaches. Overall, this study provides a basis for systematic
examination and refinement of graphical models of biological networks from the
identifiability point of view, and it has a significant potential to be
extended to more complex network structures or high-dimensional systems.
Which spike train distance is most suitable for distinguishing rate and temporal coding?
Date: 2018-02-21 00:00:00

Background: It is commonly assumed in neuronal coding that repeated
presentations of a stimulus to a coding neuron elicit similar responses. One
common way to assess similarity are spike train distances. These can be divided
into spike-resolved, such as the Victor-Purpura and the van Rossum distance,
and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance.
  New Method: We use independent steady-rate Poisson processes as surrogates
for spike trains with fixed rate and no timing information to address two basic
questions: How does the sensitivity of the different spike train distances to
temporal coding depend on the rates of the two processes and how do the
distances deal with very low rates?
  Results: Spike-resolved distances always contain rate information even for
parameters indicating time coding. This is an issue for reasonably high rates
but beneficial for very low rates. In contrast, the operational range for
detecting time coding of time-resolved distances is superior at normal rates,
but these measures produce artefacts at very low rates. The RI-SPIKE-distance
is the only measure that is sensitive to timing information only.
  Comparison with Existing Methods: While our results on rate-dependent
expectation values for the spike-resolved distances agree with
\citet{Chicharro11}, we here go one step further and specifically investigate
applicability for very low rates.
  Conclusions: The most appropriate measure depends on the rates of the data
being analysed. Accordingly, we summarize our results in one table that allows
an easy selection of the preferred measure for any kind of data.
The Keynesian Model in the General Theory: A Tutorial
Date: 2017-08-24 00:00:00

This small overview of the General Theory is the kind of summary I would have
liked to have read, before embarking in a comprehensive study of the General
Theory at the time I was a student. As shown here, the main ideas are quite
simple and easy to visualize. Unfortunately, numerous introductions to
Keynesian theory are not actually based on Keynes opus magnum, but in obscure
neoclassical reinterpretations. This is completely pointless since Keynes' book
is so readable.
Haircutting Non-cash Collateral
Date: 2017-08-25 00:00:00

Haircutting non-cash collateral has become a key element of the post-crisis
reform of the shadow banking system and OTC derivatives markets. This article
develops a parametric haircut model by expanding haircut definitions beyond the
traditional value-at-risk measure and employing a double-exponential
jump-diffusion model for collateral market risk. Haircuts are solved to target
credit risk measurements, including probability of default, expected loss or
unexpected loss criteria. Comparing to data-driven approach typically run on
proxy data series, the model enables sensitivity analysis and stress test,
captures market liquidity risk, allows idiosyncratic risk adjustments, and
incorporates relevant market information. Computational results for main
equities, securitization, and corporate bonds show potential for uses in
collateral agreements, e.g. CSAs, and for regulatory capital calculations.
Feedback effect between Volatility of capital flows and financial stability: evidence from Democratic Republic of Congo
Date: 2017-08-25 00:00:00

Financial system being the place of metting capital flows (equality between
saving and investment), a volatility of capital flows can destroy the
robustness and good working of financial system, it means subvert financial
stability. The same a weak financial system, few regulated and bad manage can
exacerbate volatility of capital flows and finely undermine financial
stability. The present study provides evidence on feedback effect between
volatility of capital flows and financial stability in Democratic republic of
Congo (DRC), and estimate the contributions of macroeconomic and
macroprudential policies in the attenuation volatility of capital flows effects
on financial stability and in the prevention of instability financial.
Assessment dynamic regression model a la Feldstein-Horioka we showed that
financial system is widely supplied and financed by internationals capital
flows. This implicate Congolese economy is financially mobile, that can be
dangerous for financial stability. The study dynamic econometric of financial
system's absolute size, we stipulate financial system has a systemic weight on
real economy. Hence a shock of financial system could have devastating effects
on Congolese economy. We estimate a vector autoregressive (VAR) model for prove
the bilateral causality and impacts of macroeconomic and macroprudential
policies. With regard to results, it proved on the one there is a feedback
effect between volatility of capital flows and financial stability, on the
other hand macroeconomic and macroprudential policies can't attenuate
volatility of capital flows and prevent instability financial. It prove
macroprudential approach is given a better result than monetary policy. The
implementation of framework macroprudential by Central Bank of Congo will be
beneficial in the realization of financial stability and attenuation volatility
of capital flows.Keywords: Volatility of capital flows, financial stability,
macroeconomic and macroprudential policies
Trends and Risk Premia: Update and Additional Plots
Date: 2017-08-25 00:00:00

Recently, our group has published two papers that have received some
attention in the finance community. One is about the profitability of trend
following strategies over 200 years, the second is about the correlation
between the profitability of "Risk Premia" and their skewness. In this short
note, we present two additional plots that fully corroborate our findings on
new data.