pwc-archive/papers-with-abstracts huggingface dataset:
Eco-evolutionary feedbacks - theoretical models and perspectives
Date: 2018-06-20 00:00:00

1. Theoretical models pertaining to feedbacks between ecological and
evolutionary processes are prevalent in multiple biological fields. An
integrative overview is currently lacking, due to little crosstalk between the
fields and the use of different methodological approaches.
  2. Here we review a wide range of models of eco-evolutionary feedbacks and
highlight their underlying assumptions. We discuss models where feedbacks occur
both within and between hierarchical levels of ecosystems, including
populations, communities, and abiotic environments, and consider feedbacks
across spatial scales.
  3. Identifying the commonalities among feedback models, and the underlying
assumptions, helps us better understand the mechanistic basis of
eco-evolutionary feedbacks. Eco-evolutionary feedbacks can be readily modelled
by coupling demographic and evolutionary formalisms. We provide an overview of
these approaches and suggest future integrative modelling avenues.
  4. Our overview highlights that eco-evolutionary feedbacks have been
incorporated in theoretical work for nearly a century. Yet, this work does not
always include the notion of rapid evolution or concurrent ecological and
evolutionary time scales. We discuss the importance of density- and
frequency-dependent selection for feedbacks, as well as the importance of
dispersal as a central linking trait between ecology and evolution in a spatial
context.
Mean Field Analysis of Personalized PageRank with Implications for Local Graph Clustering
Authors: Avrachenkov Konstantin, Kadavankandy Arun, Litvak Nelly
Date: 2018-06-20 00:00:00
Tasks: Clustering, Graph Clustering

We analyse a mean-field model of Personalized PageRank on the Erdos-Renyi
random graph containing a denser planted Erdos-Renyi subgraph. We investigate
the regimes where the values of Personalized PageRank concentrate around the
mean-field value. We also study the optimization of the damping factor, the
only parameter in Personalized PageRank. Our theoretical results help to
understand the applicability of Personalized PageRank and its limitations for
local graph clustering.
Credit Value Adjustment for Counterparties with Illiquid CDS
Date: 2018-06-20 00:00:00

Credit Value Adjustment (CVA) is the difference between the value of the
default-free and credit-risky derivative portfolio, which can be regarded as
the cost of the credit hedge. Default probabilities are therefore needed, as
input parameters to the valuation. When liquid CDS are available, then implied
probabilities of default can be derived and used. However, in small markets,
like the Nordic region of Europe, there are practically no CDS to use. We study
the following problem: given that no liquid contracts written on the default
event are available, choose a model for the default time and estimate the model
parameters. We use the minimum variance hedge to show that we should use the
real-world probabilities, first in a discrete time setting and later in the
continuous time setting. We also argue that this approach should fulfil the
requirements of IFRS 13, which means it could be used in accounting as well. We
also present a method that can be used to estimate the real-world probabilities
of default, making maximal use of market information (IFRS requirement).
Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality
Authors: Wu Zhipeng, Tian Hui, Zhu Xuzhen, Wang Shuo
Date: 2018-06-20 00:00:00
Tasks: Recommendation Systems

Matrix factorization (MF) is extensively used to mine the user preference
from explicit ratings in recommender systems. However, the reliability of
explicit ratings is not always consistent, because many factors may affect the
user's final evaluation on an item, including commercial advertising and a
friend's recommendation. Therefore, mining the reliable ratings of user is
critical to further improve the performance of the recommender system. In this
work, we analyze the deviation degree of each rating in overall rating
distribution of user and item, and propose the notion of user-based rating
centrality and item-based rating centrality, respectively. Moreover, based on
the rating centrality, we measure the reliability of each user rating and
provide an optimized matrix factorization recommendation algorithm.
Experimental results on two popular recommendation datasets reveal that our
method gets better performance compared with other matrix factorization
recommendation algorithms, especially on sparse datasets.
Optimal mechanical operation in the vicinity of curved vasculature
Date: 2018-06-20 00:00:00
Tasks: 3D Reconstruction

It has been shown that geometrical, structural properties vary along the
length of the aortic arch. There is a scarcity of studies focus on the
variation in the vessel wall thickness of aortic arch. The central premise of
this study is that considering the variation in the vessel wall thickness along
the circumference of the aortic arch to be governed by the uniform stress
distribution across the vessel wall, meeting the principle of optimal
mechanical operation of which the distribution of stress across the vessel wall
is assumed to be uniform so as to create a favorable mechanical environment for
the mechanosensitive resident vascular cells. Aortic arch was created with
image-derived three-dimensional (3D) reconstruction technique. A
structure-motivated constitutive model was utilized in the numerical modeling
and direct boundary value problem was solved. Stress distribution across the
vessel wall under physiological loading condition was predicted in
circumferential direction to test the role of the wall thickness. The results
showed the variation of the vessel wall thickness in the circumferential
direction and uniform distribution of the circumferential stress in the aortic
wall which implies a favorable mechanical environment for the resident
mechano-sensitive vascular smooth muscle cells. Correlation of geometrical and
simulation data support the proposed principle of optimal mechanical operation
for the aortic arch.
Stylized innovation: generating timelines by interrogating incrementally available randomised dictionaries
Authors: Paul Kinsler
Date: 2018-06-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

A key challenge when trying to understand innovation is that it is a dynamic, ongoing process, which can be highly contingent on ephemeral factors such as culture, economics, or luck. This means that any analysis of the real-world process must necessarily be historical - and thus probably too late to be most useful - but also cannot be sure what the properties of the web of connections between innovations is or was. Here I try to address this by designing and generating a set of synthetic innovation web "dictionaries" that can be used to host sampled innovation timelines, probe the overall statistics and behaviours of these processes, and determine the degree of their reliance on the structure or generating algorithm. Thus, inspired by the work of Fink, Reeves, Palma and Farr (2017) on innovation in language, gastronomy, and technology, I study how new symbol discovery manifests itself in terms of additional "word" vocabulary being available from dictionaries generated from a finite number of symbols. Several distinct dictionary generation models are investigated using numerical simulation, with emphasis on the scaling of knowledge as dictionary generators and parameters are varied, and the role of which order the symbols are discovered in.
The Impact of IR-based Classifier Configuration on the Performance and the Effort of Method-Level Bug Localization
Authors: Tantithamthavorn Chakkrit, Abebe Surafel Lemma, Hassan Ahmed E., Ihara Akinori, Matsumoto Kenichi
Date: 2018-06-20 00:00:00

Context: IR-based bug localization is a classifier that assists developers in
locating buggy source code entities (e.g., files and methods) based on the
content of a bug report. Such IR-based classifiers have various parameters that
can be configured differently (e.g., the choice of entity representation).
Objective: In this paper, we investigate the impact of the choice of the
IR-based classifier configuration on the top-k performance and the required
effort to examine source code entities before locating a bug at the method
level. Method: We execute a large space of classifier configuration, 3,172 in
total, on 5,266 bug reports of two software systems, i.e., Eclipse and Mozilla.
Results: We find that (1) the choice of classifier configuration impacts the
top-k performance from 0.44% to 36% and the required effort from 4,395 to
50,000 LOC; (2) classifier configurations with similar top-k performance might
require different efforts; (3) VSM achieves both the best top-k performance and
the least required effort for method-level bug localization; (4) the likelihood
of randomly picking a configuration that performs within 20% of the best top-k
classifier configuration is on average 5.4% and that of the least effort is on
average 1%; (5) configurations related to the entity representation of the
analyzed data have the most impact on both the top-k performance and the
required effort; and (6) the most efficient classifier configuration obtained
at the method-level can also be used at the file-level (and vice versa).
Conclusion: Our results lead us to conclude that configuration has a large
impact on both the top-k performance and the required effort for method-level
bug localization, suggesting that the IR-based configuration settings should be
carefully selected and the required effort metric should be included in future
bug localization studies.
Effect of the Approximation of Voltage Angle Difference on the OPF algorithms in the Power Network
Date: 2018-06-20 00:00:00

In real-time applications involving power flow equations, measuring of
voltage phase angle difference of the connected buses is essential. However, it
needs special techniques to measure voltage angle difference, which may enlarge
the computational burden of the working controller and hence, may make the
control process slow. In this paper, authors investigate the approximation of
angle difference to zero and its effects on the convergence speed and optimal
solutions of a distributed algorithm. To test this approximation, a distributed
nonlinear algorithm is proposed to optimize the multi-objective function which
includes power loss, voltage deviation and cost of reactive power generation,
by controlling the reactive power generations from distributed generators.
Authors investigate the reasons which may outlaw making this approximation and
finally, propose a condition to make such approximation. Importance of making
this approximation in terms of fast convergence of the algorithms is also
illustrated.
The Multivariate Kyle model: More is different
Date: 2018-12-20 00:00:00
Tasks: model

We reconsider the multivariate Kyle model in a risk-neutral setting with a
single, perfectly informed rational insider and a rational competitive market
maker, setting the price of n correlated securities. We prove the unicity of a
symmetric, positive definite solution for the impact matrix and provide
insights on its interpretation. We explore its implications from the
perspective of empirical market microstructure, and argue that it provides a
sensible inference procedure to cure some pathologies encountered in recent
attempts to calibrate cross-impact matrices. As an illustration, we determine
the empirical cross impact matrix of US. Treasuries, and compare the results
with recent alternative calibration methods.
Identifying viruses from metagenomic data by deep learning
Date: 2018-06-20 00:00:00
Tasks: Deep Learning

The recent development of metagenomic sequencing makes it possible to
sequence microbial genomes including viruses in an environmental sample.
Identifying viral sequences from metagenomic data is critical for downstream
virus analyses. The existing reference-based and gene homology-based methods
are not efficient in identifying unknown viruses or short viral sequences. Here
we have developed a reference-free and alignment-free machine learning method,
DeepVirFinder, for predicting viral sequences in metagenomic data using deep
learning techniques. DeepVirFinder was trained based on a large number of viral
sequences discovered before May 2015. Evaluated on the sequences after that
date, DeepVirFinder outperformed the state-of-the-art method VirFinder at all
contig lengths. Enlarging the training data by adding millions of purified
viral sequences from environmental metavirome samples significantly improves
the accuracy for predicting under-represented viruses. Applying DeepVirFinder
to real human gut metagenomic samples from patients with colorectal carcinoma
(CRC) identified 51,138 viral sequences belonging to 175 bins. Ten bins were
associated with the cancer status, indicating their potential use for
non-invasive diagnosis of CRC. In summary, DeepVirFinder greatly improved the
precision and recall rates of viral identification, and it will significantly
accelerate the discovery rate of viruses.
The evolving networks of debtor-creditor relationships with addition and deletion of nodes: a case of P2P lending
Date: 2018-06-12 00:00:00

P2P lending activities have grown rapidly and have caused the huge and
complex networks of debtor-creditor relationships. The aim of this study was to
study the underlying structural characteristics of networks formed by
debtor-creditor relationships. According attributes of P2P lending, this paper
model the networks of debtor-creditor relationships as an evolving networks
with addition and deletion of nodes. It was found that networks of
debtor-creditor relationships are scale-free networks. Moreover, the exponent
of power-law was calculated by an empirical study. In addition, this paper
study what factors impact on the exponent of power-law besides the number of
nodes. It was found that the both interest rate and term have significantly
influence on the exponent of power-law. Interest rate is negatively correlated
with the exponent of power-law and term is positively correlated with the
exponent of power-law. Our results enriches the application of complex networks
Finding influential nodes for integration in brain networks using optimal percolation theory
Date: 2018-06-20 00:00:00

Global integration of information in the brain results from complex
interactions of segregated brain networks. Identifying the most influential
neuronal populations that efficiently bind these networks is a fundamental
problem of systems neuroscience. Here we apply optimal percolation theory and
pharmacogenetic interventions in-vivo to predict and subsequently target nodes
that are essential for global integration of a memory network in rodents. The
theory predicts that integration in the memory network is mediated by a set of
low-degree nodes located in the nucleus accumbens. This result is confirmed
with pharmacogenetic inactivation of the nucleus accumbens, which eliminates
the formation of the memory network, while inactivations of other brain areas
leave the network intact. Thus, optimal percolation theory predicts essential
nodes in brain networks. This could be used to identify targets of
interventions to modulate brain function.
Resource-Efficient Neural Architect
Authors: Zhou Yanqi, Ebrahimi Siavash, Arık Sercan Ö., Yu Haonan, Liu Hairong, Diamos Greg
Date: 2018-06-12 00:00:00
Tasks: Keyword Spotting, Network Embedding, Neural Architecture Search

Neural Architecture Search (NAS) is a laborious process. Prior work on
automated NAS targets mainly on improving accuracy, but lacks consideration of
computational resource use. We propose the Resource-Efficient Neural Architect
(RENA), an efficient resource-constrained NAS using reinforcement learning with
network embedding. RENA uses a policy network to process the network embeddings
to generate new configurations. We demonstrate RENA on image recognition and
keyword spotting (KWS) problems. RENA can find novel architectures that achieve
high performance even with tight resource constraints. For CIFAR10, it achieves
2.95% test error when compute intensity is greater than 100 FLOPs/byte, and
3.87% test error when model size is less than 3M parameters. For Google Speech
Commands Dataset, RENA achieves the state-of-the-art accuracy without resource
constraints, and it outperforms the optimized architectures with tight resource
constraints.
Distribution Power Network Reconfiguration in the Smart Grid
Date: 2018-06-20 00:00:00

The power network reconfiguration algorithm with an "R" modeling approach
evaluates its behavior in computing new reconfiguration topologies for the
power grid in the context of the Smart Grid. The power distribution network
modelling with the R language is used to represent the network and support
computation of different algorithm configurations for the evaluation of new
reconfiguration topologies. This work presents a reconfiguration solution of
distribution networks, with a construction of an algorithm that receiving the
network configuration data and the nodal measurements and from these data build
a radial network, after this and using a branch exchange algorithm And
verifying the best configuration of the network through artificial
intelligence, so that there are no unnecessary changes during the operation,
and applied an algorithm that analyses the load levels, to suggest changes in
the network.
An Efficient Scheduling for Security Constraint Unit Commitment Problem Via Modified Genetic Algorithm Based on Multicellular Organisms Mechanisms
Authors: Yazdandoost Ali, Khazaei Peyman, Kamali Rahim, Saadatian Salar
Date: 2018-05-25 00:00:00
Tasks: Scheduling

Security Constraint Unit commitment (SCUC) is one of the significant
challenges in operation of power grids which tries to regulate the status of
the generation units (ON or OFF) and providing an efficient power dispatch
within the grid. While many researches tried to address the SCUC challenges, it
is a mixed-integer optimization problem that is difficult to reach global
optimum. In this study, a novel modified genetic algorithm based on
Multicellular Organisms Mechanisms (GAMOM) is developed to find an optimal
solution for SCUC problem. The presentation of the GAMOM on the SCUC contain
two sections, the GA and modified GAMOM sections. Hence, a set of population is
considered for the SCUC problem. Next, an iterative process is used to obtain
the greatest SCUC population. Indeed, the best population is selected so that
the total operating cost is minimized and also all system and units constraints
are satisfied. The effectiveness of the proposed GAMOM algorithm is determined
by the simulation studies which demonstrate the convergence speed. Finally, the
proposed technique is compared with well-known existing approaches.
Optimal Radix-2 FFT Compatible Filters for GFDM
Date: 2018-06-20 00:00:00

For a linear waveform, a finite condition number of the corresponding
modulation matrix is necessary for the waveform to convey the message without
ambiguity. Based on the Zak transform, this letter presents an analytical
approach to compute the condition number of the modulation matrix for the
multi-carrier waveform generalized frequency division multiplexing (GFDM). On
top, we further propose a filter design that yields non-singular modulation
matrices for an even number of subcarriers and subsymbols, which is not
achievable for any previous work. Such new design has significant impact on
implementation complexity, as the radix-2 FFT operations for conventional
multicarrier waveforms can readily be employed for GFDM. Additionally, we
analytically derive the optimal filter that minimizes the condition number.We
further numerically evaluate the signal-to-interference ratio (SIR) and
noise-enhancement factor (NEF) for matched filter (MF) and zero-forcing (ZF)
GFDM receivers for such design, respectively.
Explaining Controversy on Social Media via Stance Summarization
Authors: Jang Myungha, Allan James
Date: 2018-07-15 00:00:00

In an era in which new controversies rapidly emerge and evolve on social
media, navigating social media platforms to learn about a new controversy can
be an overwhelming task. In this light, there has been significant work that
studies how to identify and measure controversy online. However, we currently
lack a tool for effectively understanding controversy in social media. For
example, users have to manually examine postings to find the arguments of
conflicting stances that make up the controversy.
  In this paper, we study methods to generate a stance-aware summary that
explains a given controversy by collecting arguments of two conflicting
stances. We focus on Twitter and treat stance summarization as a ranking
problem of finding the top k tweets that best summarize the two conflicting
stances of a controversial topic. We formalize the characteristics of a good
stance summary and propose a ranking model accordingly. We first evaluate our
methods on five controversial topics on Twitter. Our user evaluation shows that
our methods consistently outperform other baseline techniques in generating a
summary that explains the given controversy.
Searching for a Single Community in a Graph
Authors: Ray Avik, Sanghavi Sujay, Shakkottai Sanjay
Date: 2018-05-24 00:00:00
Tasks: Clustering, Community Detection, Graph Clustering

In standard graph clustering/community detection, one is interested in
partitioning the graph into more densely connected subsets of nodes. In
contrast, the "search" problem of this paper aims to only find the nodes in a
"single" such community, the target, out of the many communities that may
exist. To do so , we are given suitable side information about the target; for
example, a very small number of nodes from the target are labeled as such.
  We consider a general yet simple notion of side information: all nodes are
assumed to have random weights, with nodes in the target having higher weights
on average. Given these weights and the graph, we develop a variant of the
method of moments that identifies nodes in the target more reliably, and with
lower computation, than generic community detection methods that do not use
side information and partition the entire graph. Our empirical results show
significant gains in runtime, and also gains in accuracy over other graph
clustering algorithms.
Growing Better Graphs With Latent-Variable Probabilistic Graph Grammars
Authors: Wang Xinyi, Aguinaga Salvador, Weninger Tim, Chiang David
Date: 2018-06-11 00:00:00

Recent work in graph models has found that probabilistic hyperedge
replacement grammars (HRGs) can be extracted from graphs and used to generate
new random graphs with graph properties and substructures close to the
original. In this paper, we show how to add latent variables to the model,
trained using Expectation-Maximization, to generate still better graphs, that
is, ones that generalize better to the test data. We evaluate the new method by
separating training and test graphs, building the model on the former and
measuring the likelihood of the latter, as a more stringent test of how well
the model can generalize to new graphs. On this metric, we find that our
latent-variable HRGs consistently outperform several existing graph models and
provide interesting insights into the building blocks of real world networks.