pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian identification of bacterial strains from sequencing data
Date: 2016-02-17 00:00:00
Tasks: Diversity

Rapidly assaying the diversity of a bacterial species present in a sample
obtained from a hospital patient or an evironmental source has become possible
after recent technological advances in DNA sequencing. For several applications
it is important to accurately identify the presence and estimate relative
abundances of the target organisms from short sequence reads obtained from a
sample. This task is particularly challenging when the set of interest includes
very closely related organisms, such as different strains of pathogenic
bacteria, which can vary considerably in terms of virulence, resistance and
spread. Using advanced Bayesian statistical modelling and computation
techniques we introduce a novel pipeline for bacterial identification that is
shown to outperform the currently leading pipeline for this purpose. Our
approach enables fast and accurate sequence-based identification of bacterial
strains while using only modest computational resources. Hence it provides a
useful tool for a wide spectrum of applications, including rapid clinical
diagnostics to distinguish among closely related strains causing nosocomial
infections. The software implementation is available at
https://github.com/PROBIC/BIB
A Generalized Probability Framework to Model Economic Agents' Decisions Under Uncertainty
Date: 2015-11-20 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

The applications of techniques from statistical (and classical) mechanics to
model interesting problems in economics and finance has produced valuable
results. The principal movement which has steered this research direction is
known under the name of `econophysics'. In this paper, we illustrate and
advance some of the findings that have been obtained by applying the
mathematical formalism of quantum mechanics to model human decision making
under `uncertainty' in behavioral economics and finance. Starting from
Ellsberg's seminal article, decision making situations have been experimentally
verified where the application of Kolmogorovian probability in the formulation
of expected utility is problematic. Those probability measures which by
necessity must situate themselves in Hilbert space (such as `quantum
probability') enable a faithful representation of experimental data. We thus
provide an explanation for the effectiveness of the mathematical framework of
quantum mechanics in the modeling of human decision making. We want to be
explicit though that we are not claiming that decision making has microscopic
quantum mechanical features.
Hot RAD: A Tool for Analysis of Next-Gen RAD Tag Data
Date: 2015-11-20 00:00:00
Tasks: TAG

Restriction site Associated DNA (RAD) tagging (also known as RAD-seq, etc.)
is an emerging method for analyzing an organism's genome without completely
sequencing it. This can be applied to a non-model organism without a reference
genome, though this creates the problem of how to begin data analysis on
unmapped and unannotated reads. Our program, Hot RAD, presents a
straightforward and easy-to-use method to take raw Illumina data that has been
RAD tagged and produce consensus contigs or sequence stacks using a distributed
framework, creating a basis on which to begin analyzing an organism's DNA. The
GUI (graphical user interface) element of our tool makes it easy for those not
familiar with the command line to take raw sequence files and produce usable
data in a timely manner.
An Empirical Comparison of the Summarization Power of Graph Clustering Methods
Authors: Liu Yike, Shah Neil, Koutra Danai
Date: 2015-11-21 00:00:00
Tasks: Clustering, Community Detection, Graph Clustering

How do graph clustering techniques compare with respect to their
summarization power? How well can they summarize a million-node graph with a
few representative structures? Graph clustering or community detection
algorithms can summarize a graph in terms of coherent and tightly connected
clusters. In this paper, we compare and contrast different techniques: METIS,
Louvain, spectral clustering, SlashBurn and KCBC, our proposed k-core-based
clustering method. Unlike prior work that focuses on various measures of
cluster quality, we use vocabulary structures that often appear in real graphs
and the Minimum Description Length (MDL) principle to obtain a graph summary
per clustering method. Our main contributions are: (i) Formulation: We propose
a summarization-based evaluation of clustering methods. Our method,
VOG-OVERLAP, concisely summarizes graphs in terms of their important structures
which lead to small edge overlap, and large node/edge coverage; (ii) Algorithm:
we introduce KCBC, a graph decomposition technique, in the heart of which lies
the k-core algorithm (iii) Evaluation: We compare the summarization power of
five clustering techniques on large real graphs, and analyze their compression
performance, summary statistics and runtimes.
Backbone of credit relationships in the Japanese credit market
Date: 2015-11-21 00:00:00

We detect the backbone of the weighted bipartite network of the Japanese
credit market relationships. The backbone is detected by adapting a general
method used in the investigation of weighted networks. With this approach we
detect a backbone that is statistically validated against a null hypothesis of
uniform diversification of loans for banks and firms. Our investigation is done
year by year and it covers more than thirty years during the period from 1980
to 2011. We relate some of our findings with economic events that have
characterized the Japanese credit market during the last years. The study of
the time evolution of the backbone allows us to detect changes occurred in
network size, fraction of credit explained, and attributes characterizing the
banks and the firms present in the backbone.
Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach
Date: 2015-11-21 00:00:00

We investigate the trading behavior of Finnish individual investors trading
the stocks selected to compute the OMXH25 index in 2003 by tracking the
individual daily investment decisions. We verify that the set of investors is a
highly heterogeneous system under many aspects. We introduce a correlation
based method that is able to detect a hierarchical structure of the trading
profiles of heterogeneous individual investors. We verify that the detected
hierarchical structure is highly overlapping with the cluster structure
obtained with the approach of statistically validated networks when an
appropriate threshold of the hierarchical trees is used. We also show that the
combination of the correlation based method and of the statistically validated
method provides a way to expand the information about the clusters of investors
with similar trading profiles in a robust and reliable way.
An identifiability problem in a state model for partly undetected chronic diseases
Date: 2015-11-21 00:00:00
Tasks: Open-Ended Question Answering

Recently, we proposed an state model (compartment model) to describe the
progression of a chronic disease with an pre-clinical (undiagnosed) state
before clinical diagnosis. It is an open question, if a sequence of
cross-sectional studies with mortality follow-up is sufficient to estimate the
true incidence rate of the disease, i.e. the incidence of the undiagnosed and
diagnosed disease. In this note, we construct a counterexample and show that
this cannot be achieved in general.
Cell assembly dynamics of sparsely-connected inhibitory networks: a simple model for the collective activity of striatal projection neurons
Date: 2015-11-21 00:00:00

Striatal projection neurons form a sparsely-connected inhibitory network, and
this arrangement may be essential for the appropriate temporal organization of
behavior. Here we show that a simplified, sparse inhibitory network of
Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal
population activity, as observed in brain slices [Carrillo-Reid et al., J.
Neurophysiology 99 (2008) 1435{1450]. In particular we develop a new metric to
determine the conditions under which sparse inhibitory networks form
anti-correlated cell assemblies with time-varying activity of individual cells.
We found that under these conditions the network displays an input-specific
sequence of cell assembly switching, that effectively discriminates similar
inputs. Our results support the proposal [Ponzi and Wickens, PLoS Comp Biol 9
(2013) e1002954] that GABAergic connections between striatal projection neurons
allow stimulus-selective, temporally-extended sequential activation of cell
assemblies. Furthermore, we help to show how altered intrastriatal GABAergic
signaling may produce aberrant network-level information processing in
disorders such as Parkinson's and Huntington's diseases.
A composition between risk and deviation measures
Date: 2018-05-24 00:00:00

The intuition of risk is based on two main concepts: loss and variability. In
this paper, we present a composition of risk and deviation measures, which
contemplate these two concepts. Based on the proposed Limitedness axiom, we
prove that this resulting composition, based on properties of the two
components, is a coherent risk measure. Similar results for the cases of convex
and co-monotone risk measures are exposed. We also provide examples of known
and new risk measures constructed under this framework in order to highlight
the importance of our approach, especially the role of the Limitedness axiom.
Vulnerability of geriatric patients to biomaterial associated infections: in vitro study of biofilm formation by Pseudomonas aeruginosa on orthopedic implants
Date: 2015-11-22 00:00:00

Fibronectin, a glycoprotein secreted by connective tissue cells is found in
the human plasma as well as in the ECM. It is known to have an adhesive
property and plays a role in cell-to-cell and cell-to-substratum adhesions. In
rheumatoid arthritis (RA) and osteoarthritis (OA), fibronectin is locally
synthesized by the synovial cells, and the synovial fluid level of fibronectin
is found to be double that in plasma. The concentration of fibronectin in the
synovial fluid under such conditions is found escalate to 2-3 times higher than
in the corresponding plasma. The present article demonstrates that the protein
has a strong tendency to get adsorbed on biomaterials after an implant surgery
in preference to lighter proteins like albumin, which in turn enhances the
growth of robust biofilms. The present article demonstrates that, heightened
risk of dangerous implant infections due to the formation of such biofilms
coupled with the degrading immunity levels of the geriatric patients have the
potential to transform an implant surgery to a 'life threatening' event.
An Integrated Framework to Recommend Personalized Retention Actions to Control B2C E-Commerce Customer Churn
Authors: Shini Renjith
Date: 2015-11-22 00:00:00
Tasks: Marketing

Considering the level of competition prevailing in Business-to-Consumer (B2C) E-Commerce domain and the huge investments required to attract new customers, firms are now giving more focus to reduce their customer churn rate. Churn rate is the ratio of customers who part away with the firm in a specific time period. One of the best mechanism to retain current customers is to identify any potential churn and respond fast to prevent it. Detecting early signs of a potential churn, recognizing what the customer is looking for by the movement and automating personalized win back campaigns are essential to sustain business in this era of competition. E-Commerce firms normally possess large volume of data pertaining to their existing customers like transaction history, search history, periodicity of purchases, etc. Data mining techniques can be applied to analyse customer behaviour and to predict the potential customer attrition so that special marketing strategies can be adopted to retain them. This paper proposes an integrated model that can predict customer churn and also recommend personalized win back actions.
Early Warning Signs of the Economic Crisis in Greece: A Warning for Other Countries and Regions
Date: 2015-11-22 00:00:00

Warning signs about the developing economic crisis in Greece were present in
the growth rate of the Gross Domestic Product (GDP) and in the growth of the
GDP well before the economic collapse. The growth rate was strongly unstable.
On average, in less than 50 years, it decreased 10-folds but after reaching a
low minimum it quickly increased 6-folds only to crash before completing the
full cycle. The decreasing growth rate was leading to an asymptotic maximum of
the GDP but it was soon replaced by a fast-increasing growth rate propelling
the GDP along a pseudo-hyperbolic trajectory, which if continued would have
escaped to infinity in 2017. Such a growth could not have been possibly
supported. Under these conditions, the economic collapse in Greece was
inevitable.
"MS-Patch-Clamp" or the Possibility of Mass Spectrometry Hybridization with Patch-Clamp Setups for Single Cell Metabolomics and Channelomics
Date: 2015-11-22 00:00:00
Tasks: Informativeness

In this projecting work we propose a mass spectrometric patch-clamp equipment
with the capillary performing both a local potential registration at the cell
membrane and the analyte suction simultaneously. This paper provides a current
literature analysis comparing the possibilities of the novel approach proposed
with the known methods, such as scanning patch-clamp, scanning ion conductance
microscopy, patch clamp based on scanning probe microscopy technology,
quantitative subcellular secondary ion mass spectrometry or "ion microscopy",
live single-cell mass spectrometry, in situ cell-by-cell imaging, single-cell
video-mass spectrometry, etc. We also consider the ways to improve the
informativeness of these methods and particularly emphasize the trend at the
increasing of the analysis complexity. We propose here the way to improve the
efficiency of the cell trapping to the capillary during MS-path-clamp, as well
as to provide laser surface ionization using laser trapping and tweezing of
cells with the laser beam transmitted through the capillary as a waveguide. It
is also possible to combine the above system with the microcolumn separation
system or capillary electrophoresis as an optional direction of further
development of the complex of analytical techniques emerging from the MS
variation of patch-clamp.
Understanding Music Playlists
Authors: Choi Keunwoo, Fazekas George, Sandler Mark
Date: 2015-11-22 00:00:00
Tasks: BIG-bench Machine Learning, Music Recommendation, Recommendation Systems

As music streaming services dominate the music industry, the playlist is
becoming an increasingly crucial element of music consumption. Con- sequently,
the music recommendation problem is often casted as a playlist generation prob-
lem. Better understanding of the playlist is there- fore necessary for
developing better playlist gen- eration algorithms. In this work, we analyse
two playlist datasets to investigate some com- monly assumed hypotheses about
playlists. Our findings indicate that deeper understanding of playlists is
needed to provide better prior infor- mation and improve machine learning
algorithms in the design of recommendation systems.
Risk-return relationship: An empirical study of different statistical methods for estimating the Capital Asset Pricing Models (CAPM) and the Fama-French model for large cap stocks
Date: 2015-11-23 00:00:00

The Capital Asset Pricing Model (CAPM) is one of the original models in
explaining risk-return relationship in the financial market. However, when
applying the CAPM into reality, it demonstrates a lot of shortcomings. While
improving the performance of the model, many studies, on one hand, have
attempted to apply different statistical methods to estimate the model, on the
other hand, have added more predictors to the model. First, the thesis focuses
on reviewing the CAPM and comparing popular statistical methods used to
estimate it, and then, the thesis compares predictive power of the CAPM and the
Fama-French model, which is an important extension of the CAPM. Through an
empirical study on the data set of large cap stocks, we have demonstrated that
there is no statistical method that would recover the expected relationship
between systematic risk (represented by beta) and return from the CAPM, and
that the Fama-French model does not have a better predictive performance than
the CAPM on individual stocks. Therefore, the thesis provides more evidence to
support the incorrectness of the CAPM and the limitation of the Fama-French
model in explaining risk-return relationship.
NearBucket-LSH: Efficient Similarity Search in P2P Networks
Authors: Kraus Naama, Carmel David, Keidar Idit, Orenbach Meni
Date: 2015-11-23 00:00:00

We present NearBucket-LSH, an effective algorithm for similarity search in
large-scale distributed online social networks organized as peer-to-peer
overlays. As communication is a dominant consideration in distributed systems,
we focus on minimizing the network cost while guaranteeing good search quality.
Our algorithm is based on Locality Sensitive Hashing (LSH), which limits the
search to collections of objects, called buckets, that have a high probability
to be similar to the query. More specifically, NearBucket-LSH employs an LSH
extension that searches in near buckets, and improves search quality but also
significantly increases the network cost. We decrease the network cost by
considering the internals of both LSH and the P2P overlay, and harnessing their
properties to our needs. We show that our NearBucket-LSH increases search
quality for a given network cost compared to previous art. In many cases, the
search quality increases by more than 50%.
Some Dynamic Market Models
Date: 2015-11-23 00:00:00

In this text, we study the temporal behavior of markets using models
expressible as ordinary differential equations. The markets studied are those
where each customer buys only one copy of the good, for example, subscription
of smartphone service, journals and newspapers, and goods such as books, music
and games. The underlying model is the diffusion model of Frank Bass. Evolution
of markets with no competitors and markets with several competitors are
analyzed where, in particulat, the effects of churning upon the market
evolution is investigated. Analytic solutions are given for the temporal
evolution of several types of interactive games.
A predictive structural model of the primate connectome
Date: 2016-05-30 00:00:00

Anatomical connectivity imposes strong constraints on brain function, but
there is no general agreement about principles that govern its organization.
Based on extensive quantitative data we tested the power of three models to
predict connections of the primate cerebral cortex: architectonic similarity
(structural model), spatial proximity (distance model) and thickness similarity
(thickness model). Architectonic similarity showed the strongest and most
consistent influence on connection features. This parameter was strongly
associated with the presence or absence of inter-areal connections and when
integrated with spatial distance, the model allowed predicting the existence of
projections with very high accuracy. Moreover, architectonic similarity was
strongly related to the laminar pattern of projections origins, and the
absolute number of cortical connections of an area. By contrast, cortical
thickness similarity and distance were not systematically related to connection
features. These findings suggest that cortical architecture provides a general
organizing principle for connections in the primate brain.
Predicting Relevance based on Assessor Disagreement: Analysis and Practical Applications for Search Evaluation
Authors: Demeester Thomas, Aly Robin, Hiemstra Djoerd, Nguyen Dong, Develder Chris
Date: 2015-11-23 00:00:00
Tasks: Information Retrieval, Retrieval

Evaluation of search engines relies on assessments of search results for
selected test queries, from which we would ideally like to draw conclusions in
terms of relevance of the results for general (e.g., future, unknown) users. In
practice however, most evaluation scenarios only allow us to conclusively
determine the relevance towards the particular assessor that provided the
judgments. A factor that cannot be ignored when extending conclusions made from
assessors towards users, is the possible disagreement on relevance, assuming
that a single gold truth label does not exist. This paper presents and analyzes
the Predicted Relevance Model (PRM), which allows predicting a particular
result's relevance for a random user, based on an observed assessment and
knowledge on the average disagreement between assessors. With the PRM, existing
evaluation metrics designed to measure binary assessor relevance, can be
transformed into more robust and effectively graded measures that evaluate
relevance towards a random user. It also leads to a principled way of
quantifying multiple graded or categorical relevance levels for use as gains in
established graded relevance measures, such as normalized discounted cumulative
gain (nDCG), which nowadays often use heuristic and data-independent gain
values. Given a set of test topics with graded relevance judgments, the PRM
allows evaluating systems on different scenarios, such as their capability of
retrieving top results, or how well they are able to filter out non-relevant
ones. Its use in actual evaluation scenarios is illustrated on several
information retrieval test collections.
Statistical Properties of Gray Color Categorization: Asymptotics of Psychometric Function
Date: 2015-11-15 00:00:00

The results of our experiments on categorical perception of different shades
of gray are reported. A special color generator was created for conducting the
experiments on categorizing a random sequence of colors into two classes,
light-gray and dark-gray. The collected data are analyzed based on constructing
(i) the asymptotics of the corresponding psychometric functions and (ii) the
mean decision time in categorizing a given shade of gray depending on the shade
brightness (shade number). Conclusions about plausible mechanisms governing
categorical perception, at least for the analyzed system, are drawn.
Noise Induced Phenomena in the Dynamics of Two Competing Species
Date: 2016-09-23 00:00:00
Tasks: Vocal Bursts Valence Prediction

Noise through its interaction with the nonlinearity of the living systems can
give rise to counter-intuitive phenomena. In this paper we shortly review noise
induced effects in different ecosystems, in which two populations compete for
the same resources. We also present new results on spatial patterns of two
populations, while modeling real distributions of anchovies and sardines. The
transient dynamics of these ecosystems are analyzed through generalized
Lotka-Volterra equations in the presence of multiplicative noise, which models
the interaction between the species and the environment. We find noise induced
phenomena such as quasi-deterministic oscillations, stochastic resonance, noise
delayed extinction, and noise induced pattern formation. In addition, our
theoretical results are validated with experimental findings. Specifically the
results, obtained by a coupled map lattice model, well reproduce the spatial
distributions of anchovies and sardines, observed in a marine ecosystem.
Moreover, the experimental dynamical behavior of two competing bacterial
populations in a meat product and the probability distribution at long times of
one of them are well reproduced by a stochastic microbial predictive model.