pwc-archive/papers-with-abstracts huggingface dataset:
Static Internal Representation Of Dynamic Situations Reveals Time Compaction In Human Cognition
Date: 2018-06-27 00:00:00
Tasks: Decision Making

The time-changing nature of our world demands processing of huge amounts of
information in fast and reliable way to generate successful behaviors.
Therefore, significant brain resources are devoted to process spatiotemporal
information. Neural basis of spatial processing and their cognitive correlates
are well established mostly for static environments. Nonetheless, in
time-changing situations the brain exploits specific processing mechanisms for
temporal information based on prediction and anticipation, as time compression
during visual perception and mental navigation. Alternative hypothesis of time
compaction integrates both views, postulating that dynamic situations are
internally represented as static spatial maps where temporal information is
extracted by predicting and structuring the relevant interactions.
Nevertheless, empirical approaches tackling the biological soundness of time
compaction are still lacking. Here we show that performance in a discrimination
learning task involving dynamic situations can be either favored or hampered
via previous exposition to interfering static scenes. In this sense, men were
effectively conditioned in contrast to a control group, in coherence with the
hypothesis. Meanwhile, women performed on par with control men, regardless of
the previous conditioning. This suggests time compaction is a salient cognitive
strategy in men when dealing with dynamic situations, while women seem to rely
on a broader range of information processing strategies. Finally, we further
corroborated the time compaction mechanism involved in these experimental
findings through a mathematical model of the experimental process. Our results
point to some form of static internal representation mechanism at cognitive
level involved in decision-making and strategy planning in dynamic situations
[...]
Stochastic-Geometry Based Characterization of Aggregate Interference in TVWS Cognitive Radio Networks
Date: 2018-06-27 00:00:00

In this paper, we characterize the worst-case interference for a finite-area
TV white space heterogeneous network using the tools of stochastic geometry. We
derive closed-form expressions on the probability distribution function (PDF)
and an average value of the aggregate interference for various values of path
loss exponent. The proposed characterization of the interference is simple and
can be used in improving the spectrum access techniques. Using the derived PDF,
we demonstrate the performance gain in the spectrum detection of an
eigenvalue-based detector for cognitive radio networks.
What is Wrong with Net Promoter Score
Date: 2018-06-12 00:00:00

Net-Promoter Score (NPS) is now ubiquitous as an easily-collected market
research metric, having displaced many serious market research processes.
Unfortunately, this has been its sole success. It possesses few, if any, of the
characteristics that might be regarded as highly desirable in a high-level
market research metric; on the contrary, it has done considerable damage both
to companies and to their customers.
Employee Attrition Prediction
Authors: Yedida Rahul, Reddy Rahul, Vahi Rakshit, Jana Rahul, GV Abhilash, Kulkarni Deepti
Date: 2018-06-19 00:00:00
Tasks: Prediction, regression

We aim to predict whether an employee of a company will leave or not, using
the k-Nearest Neighbors algorithm. We use evaluation of employee performance,
average monthly hours at work and number of years spent in the company, among
others, as our features. Other approaches to this problem include the use of
ANNs, decision trees and logistic regression. The dataset was split, using 70%
for training the algorithm and 30% for testing it, achieving an accuracy of
94.32%.
Evaluating author name disambiguation for digital libraries: A case of DBLP
Authors: Kim Jinseok
Date: 2018-07-30 00:00:00

Author name ambiguity in a digital library may affect the findings of
research that mines authorship data of the library. This study evaluates author
name disambiguation in DBLP, a widely used but insufficiently evaluated digital
library for its disambiguation performance. In doing so, this study takes a
triangulation approach that author name disambiguation for a digital library
can be better evaluated when its performance is assessed on multiple labeled
datasets with comparison to baselines. Tested on three types of labeled data
containing 5,000 ~ 700K disambiguated names and 6M pairs of disambiguated
names, DBLP is shown to assign author names quite accurately to distinct
authors, resulting in pairwise precision, recall, and F1 measures around 0.90
or above overall. DBLP's author name disambiguation performs well even on large
ambiguous name blocks but deficiently on distinguishing authors with the same
names. When compared to other disambiguation algorithms, DBLP's disambiguation
performance is quite competitive, possibly due to its hybrid disambiguation
approach combining algorithmic disambiguation and manual error correction. A
discussion follows on strengths and weaknesses of labeled datasets used in this
study for future efforts to evaluate author name disambiguation on a digital
library scale.
A Tunable Particle Swarm Size Optimization Algorithm for Feature Selection
Authors: Mallenahalli Naresh, Sarma T. Hitendra
Date: 2018-06-20 00:00:00
Tasks: Classification, feature selection, General Classification

Feature selection is the process of identifying statistically most relevant
features to improve the predictive capabilities of the classifiers. To find the
best features subsets, the population based approaches like Particle Swarm
Optimization(PSO) and genetic algorithms are being widely employed. However, it
is a general observation that not having right set of particles in the swarm
may result in sub-optimal solutions, affecting the accuracies of classifiers.
To address this issue, we propose a novel tunable swarm size approach to
reconfigure the particles in a standard PSO, based on the data sets, in real
time. The proposed algorithm is named as Tunable Particle Swarm Size
Optimization Algorithm (TPSO). It is a wrapper based approach wherein an
Alternating Decision Tree (ADT) classifier is used for identifying influential
feature subset, which is further evaluated by a new objective function which
integrates the Classification Accuracy (CA) with a modified F-Score, to ensure
better classification accuracy over varying population sizes. Experimental
studies on bench mark data sets and Wilcoxon statistical test have proved the
fact that the proposed algorithm (TPSO) is efficient in identifying optimal
feature subsets that improve classification accuracies of base classifiers in
comparison to its standalone form.
Exact traveling wave solutions of one-dimensional models of cancer invasion
Authors: Maria Shubina
Date: 2018-06-15 00:00:00

In this paper we consider continuous mathematical models of tumour growth and invasion based on the model introduced by Chaplain and Lolas \cite{Chaplain&Lolas2006}, for the case of one space dimension. The models consist of a system of three coupled nonlinear reaction-diffusion-taxis partial differential equations describing the interactions between cancer cells, the matrix degrading enzyme and the tissue. For these models under certain conditions on the model parameters we obtain exact analytical solutions in terms of traveling wave variables. These solutions are smooth positive definite functions for some of which whose profiles agree with those obtained from numerical computations \cite{Chaplain&Lolas2006} for not very large time intervals.
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference
Date: 2018-06-27 00:00:00
Tasks: Bayesian Inference, Time Series, Time Series Analysis

Hidden Markov models (HMMs) are popular time series model in many fields
including ecology, economics and genetics. HMMs can be defined over discrete or
continuous time, though here we only cover the former. In the field of movement
ecology in particular, HMMs have become a popular tool for the analysis of
movement data because of their ability to connect observed movement data to an
underlying latent process, generally interpreted as the animal's unobserved
behavior. Further, we model the tendency to persist in a given behavior over
time. Notation presented here will generally follow the format of Zucchini et
al. (2016) and cover HMMs applied in an unsupervised case to animal movement
data, specifically positional data. We provide Stan code to analyze movement
data of the wild haggis as presented first in Michelot et al. (2016).
Author-Based Analysis of Conference versus Journal Publication in Computer Science
Authors: Kim Jinseok
Date: 2018-06-27 00:00:00

Conference publications in computer science (CS) have attracted scholarly
attention due to their unique status as a main research outlet unlike other
science fields where journals are dominantly used for communicating research
findings. One frequent research question has been how different conference and
journal publications are, considering a paper as a unit of analysis. This study
takes an author-based approach to analyze publishing patterns of 517,763
scholars who have ever published both in CS conferences and journals for the
last 57 years, as recorded in DBLP. The analysis shows that the majority of CS
scholars tend to make their scholarly debut, publish more papers, and
collaborate with more coauthors in conferences than in journals. Importantly,
conference papers seem to serve as a distinct channel of scholarly
communication, not a mere preceding step to journal publications: coauthors and
title words of authors across conferences and journals tend not to overlap
much. This study corroborates findings of previous studies on this topic from a
distinctive perspective and suggests that conference authorship in CS calls for
more special attention from scholars and administrators outside CS who have
focused on journal publications to mine authorship data and evaluate scholarly
performance.
Prototype of a multi-host type DAQ front-end system for RI-beam experiments
Date: 2018-06-28 00:00:00
Tasks: Vocal Bursts Type Prediction

The multi-host type DAQ front-end system is proposed and a prototype system
is developed. In general, CAMAC/VME type ADC modules have a single
trigger-input (or gate-input) port. In contrast, a prototype of a new system
has multiple trigger-input ports. In addition, the Wilkinson-type and
successive approximation ADCs have the dead time, whereas, this prototype
system utilizes the combination of Flash-ADC and FPGA that enabling the
dead-time free system. Corresponding to the trigger-input ports, data are sent
to different back-end systems. So, a legacy ADC module is a 1-to-1 system, but,
this proposing system is a 1-to-X system without loss. This system will be
applied for nuclear physics experiments at RIKEN RIBF which produces intense
RI-beams. This multi-host type DAQ front-end system enables us to perform
different experiments simultaneously at the same beam line. In this
contribution, the concept and the performance-test results of the front-end
system is shown.
Biochemical Coupling Through Emergent Conservation Laws
Date: 2018-06-28 00:00:00

Bazhin has analyzed ATP coupling in terms of quasiequilibrium states where
fast reactions have reached an approximate steady state while slow reactions
have not yet reached equilibrium. After an expository introduction to the
relevant aspects of reaction network theory, we review his work and explain the
role of emergent conserved quantities in coupling. These are quantities, left
unchanged by fast reactions, whose conservation forces exergonic processes such
as ATP hydrolysis to drive desired endergonic processes.
Impact of the Query Set on the Evaluation of Expert Finding Systems
Authors: Brochier Robin  ERIC, Guille Adrien  ERIC, Rothan Benjamin  ERIC, Velcin Julien  ERIC
Date: 2018-06-28 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

Expertise is a loosely defined concept that is hard to formalize. Much
research has focused on designing efficient algorithms for expert finding in
large databases in various application domains. The evaluation of such
recommender systems lies most of the time on human-annotated sets of experts
associated with topics. The protocol of evaluation consists in using the
namings or short descriptions of these topics as raw queries in order to rank
the available set of candidates. Several measures taken from the field of
information retrieval are then applied to rate the rankings of candidates
against the ground truth set of experts. In this paper, we apply this
topic-query evaluation methodology with the AMiner data and explore a new
document-query methodology to evaluate experts retrieval from a set of queries
sampled directly from the experts documents. Specifically, we describe two
datasets extracted from AMiner, three baseline algorithms from the literature
based on several document representations and provide experiment results to
show that using a wide range of more realistic queries provides different
evaluation results to the usual topic-queries.
Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations
Authors: Xiao Yan, Guo Jiafeng, Fan Yixing, Lan Yanyan, Xu Jun, Cheng Xueqi
Date: 2018-07-22 00:00:00
Tasks: Information Retrieval, Re-Ranking, Retrieval

Vocabulary mismatch is a central problem in information retrieval (IR), i.e.,
the relevant documents may not contain the same (symbolic) terms of the query.
Recently, neural representations have shown great success in capturing semantic
relatedness, leading to new possibilities to alleviate the vocabulary mismatch
problem in IR. However, most existing efforts in this direction have been
devoted to the re-ranking stage. That is to leverage neural representations to
help re-rank a set of candidate documents, which are typically obtained from an
initial retrieval stage based on some symbolic index and search scheme (e.g.,
BM25 over the inverted index). This naturally raises a question: if the
relevant documents have not been found in the initial retrieval stage due to
vocabulary mismatch, there would be no chance to re-rank them to the top
positions later. Therefore, in this paper, we study the problem how to employ
neural representations to improve the recall of relevant documents in the
initial retrieval stage. Specifically, to meet the efficiency requirement of
the initial stage, we introduce a neural index for the neural representations
of documents, and propose two hybrid search schemes based on both neural and
symbolic indices, namely the parallel search scheme and the sequential search
scheme. Our experiments show that both hybrid index and search schemes can
improve the recall of the initial retrieval stage with small overhead.
Data on the annual aggregated income taxes of the Italian municipalities over the quinquennium 2007-2011
Date: 2018-06-16 00:00:00

This dataset contains the annual aggregated income taxes of all the Italian
municipalities over the years 2007-2011. Data are clustered over the Italian
regions and provinces. The source of the data is the Italian Ministry of
Economics and Finance. The administrative variations in Italy over the
quinquennium have been taken into account. Data are useful to understand the
economic structure of Italy at the microscopic level of municipalities. They
can serve also for making comparisons between economical aspects and other
features of the Italian cities.
Time consistency of the mean-risk problem
Date: 2020-01-16 00:00:00

Choosing a portfolio of risky assets over time that maximizes the expected
return at the same time as it minimizes portfolio risk is a classical problem
in Mathematical Finance and is referred to as the dynamic Markowitz problem
(when the risk is measured by variance) or more generally, the dynamic
mean-risk problem. In most of the literature, the mean-risk problem is
scalarized and it is well known that this scalarized problem does not satisfy
the (scalar) Bellman's principle. Thus, the classical dynamic programming
methods are not applicable. For the purpose of this paper we focus on the
discrete time setup, and we will use a time consistent dynamic convex risk
measure to evaluate the risk of a portfolio. We will show that when we do not
scalarize the problem, but leave it in its original form as a vector
optimization problem, the upper images, whose boundary contains the efficient
frontier, recurse backwards in time under very mild assumptions. Thus, the
dynamic mean-risk problem does satisfy a Bellman's principle, but a more
general one, that seems more appropriate for a vector optimization problem: a
set-valued Bellman's principle. We will present conditions under which this
recursion can be exploited directly to compute a solution in the spirit of
dynamic programming. Numerical examples illustrate the proposed method. The
obtained results open the door for a new branch in mathematics: dynamic
multivariate programming.
Vehicles as sensors: high-accuracy rainfall maps from windshield wiper measurements
Date: 2018-06-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Connected vehicles are poised to transform the field of environmental sensing
by enabling acquisition of scientific data at unprecedented scales. Drawing on
a real-world dataset collected from almost 70 connected vehicles, this study
generates improved rainfall estimates by combining weather radar with
windshield wiper observations. Existing methods for measuring precipitation are
subject to spatial and temporal uncertainties that compromise high-precision
applications like flash flood forecasting. Windshield wiper measurements from
connected vehicles correct these uncertainties by providing precise information
about the timing and location of rainfall. Using co-located vehicle dashboard
camera footage, we find that wiper measurements are a stronger predictor of
binary rainfall state than traditional stationary gages or radar-based
measurements. We introduce a Bayesian filtering framework that generates
improved rainfall estimates by updating radar rainfall fields with windshield
wiper observations. We find that the resulting rainfall field estimate captures
rainfall events that would otherwise be missed by conventional measurements. We
discuss how these enhanced rainfall maps can be used to improve flood warnings
and facilitate real-time operation of stormwater infrastructure.
Genesis of the alpha beta T-cell receptor
Date: 2018-12-11 00:00:00
Tasks: Diversity

The T-cell (TCR) repertoire relies on the diversity of receptors composed of
two chains, called $\alpha$ and $\beta$, to recognize pathogens. Using results
of high throughput sequencing and computational chain-pairing experiments of
human TCR repertoires, we quantitively characterize the $\alpha\beta$
generation process. We estimate the probabilities of a rescue recombination of
the $\beta$ chain on the second chromosome upon failure or success on the first
chromosome. Unlike $\beta$ chains, $\alpha$ chains recombine simultaneously on
both chromosomes, resulting in correlated statistics of the two genes which we
predict using a mechanistic model. We find that $\sim 28 \%$ of cells express
both $\alpha$ chains. We report that clones sharing the same $\beta$ chain but
different $\alpha$ chains are overrepresented, suggesting that they respond to
common immune challenges. Altogether, our statistical analysis gives a complete
quantitative mechanistic picture that results in the observed correlations in
the generative process. We learn that the probability to generate any
TCR$\alpha\beta$ is lower than $10^{-12}$ and estimate the generation diversity
and sharing properties of the $\alpha\beta$ TCR repertoire.
Can justice be fair when it is blind? How social network structures can promote or prevent the evolution of despotism
Date: 2018-06-28 00:00:00

Hierarchy is an efficient way for a group to organize, but often goes along
with inequality that benefits leaders. To control despotic behaviour, followers
can assess leaders decisions by aggregating their own and their neighbours
experience, and in response challenge despotic leaders. But in hierarchical
social networks, this interactional justice can be limited by (i) the high
influence of a small clique who are treated better, and (ii) the low
connectedness of followers. Here we study how the connectedness of a social
network affects the co-evolution of despotism in leaders and tolerance to
despotism in followers. We simulate the evolution of a population of agents,
where the influence of an agent is its number of social links. Whether a leader
remains in power is controlled by the overall satisfaction of group members, as
determined by their joint assessment of the leaders behaviour. We demonstrate
that centralization of a social network around a highly influential clique
greatly increases the level of despotism. This is because the clique is more
satisfied, and their higher influence spreads their positive opinion of the
leader throughout the network. Finally, our results suggest that increasing the
connectedness of followers limits despotism while maintaining hierarchy.
Psychophysical laws as reflection of mental space properties
Date: 2018-06-27 00:00:00

The paper is devoted to the relationship between psychophysics and physics of
mind. The basic trends in psychophysics development are briefly discussed with
special attention focused on Teghtsoonian's hypotheses. These hypotheses pose
the concept of the universality of inner psychophysics and enable to speak
about psychological space as an individual object with its own properties.
Turning to the two-component description of human behavior (I. Lubashevsky,
Physics of the Human Mind, Springer, 2017) the notion of mental space is
formulated and human perception of external stimuli is treated as the emergence
of the corresponding images in the mental space. On one hand, these images are
caused by external stimuli and their magnitude bears the information about the
intensity of the corresponding stimuli. On the other hand, the individual
structure of such images as well as their subsistence after emergence is
determined only by the properties of mental space on its own. Finally, the
mental operations of image comparison and their scaling are defined in a way
allowing for the bounded capacity of human cognition. As demonstrated, the
developed theory of stimulus perception is able to explain the basic
regularities of psychophysics, e.g., (i) the regression and range effects
leading to the overestimation of weak stimuli and the underestimation of strong
stimuli, (ii) scalar variability (Weber's and Ekman' laws), and (\textit{iii})
the sequential (memory) effects. As the final result, a solution to the
Fechner-Stevens dilemma is proposed. This solution posits that Fechner's
logarithmic law is not a consequences of Weber's law but stems from the
interplay of uncertainty in evaluating stimulus intensities and the multi-step
scaling required to overcome the stimulus incommensurability.
Combining Radon transform and Electrical Capacitance Tomography for a $2d+1$ imaging device
Date: 2018-06-28 00:00:00

This paper describes a coplanar non invasive non destructive capacitive
imaging device. We first introduce a mathematical model for its output, and
discuss some of its theoretical capabilities. We show that the data obtained
from this device can be interpreted as a weighted Radon transform of the
electrical permittivity of the measured object near its surface. Image
reconstructions from experimental data provide good surface resolution as well
as short depth imaging, making the apparatus a $2d+1$ imager. The quality of
the images leads us to expect that excellent results can be delivered by
\emph{ad-hoc} optimized inversion formulas. There are also interesting, yet
unexplored, theoretical questions on imaging that this sensor will allow to
test.