pwc-archive/papers-with-abstracts huggingface dataset:
Concerns regarding the deterioration of objectivity in molecular biology
Authors: Tomokazu Konishi
Date: 2018-07-10 00:00:00

Scientific objectivity was not a problem in the early days of molecular biology. However, relativism seems to have invaded some areas of the field, damaging the objectivity of its analyses. This review reports on the status of this issue by investigating a number of cases.
Cross-layer Interference Modeling for 5G MmWave Networks in the Presence of Blockage
Date: 2018-07-11 00:00:00

Fifth generation (5G) wireless technology is expected to utilize highly
directive antennas at millimeter wave (mmWave) spectrum to offer higher data
rates. However, given the high directivity of antennas and adverse propagation
characteristics at mmWave frequencies, these signals are very susceptible to
obstacles. One of the important factors that are highly impacted is
interference behavior. In fact, signals received from other terminals can be
easily blocked or attenuated at the receiver. In addition, higher number of
terminals can transmit signals without introducing much interference and hence
the traffic behavior, maintained by medium access control (MAC) layer, may
change. In this paper, we provide an interference model to evaluate the
interference power received at the physical layer of the receiving terminal,
considering antenna directivity, effect of obstacles and MAC layer constraints
that control the number of terminals transmitting simultaneously. We first
develop a blockage model and then derive the Laplace transform of the
interference power received at a typical receiving node. Subsequently, using
the derived Laplace transform, we evaluate the network error performance using
average bit-error-rate (BER). Analytical results are validated via Monte-Carlo
simulations.
A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion
Authors: Aliannejadi Mohammad, Rafailidis Dimitrios, Crestani Fabio
Date: 2018-07-13 00:00:00
Tasks: Collaborative Filtering, Collaborative Ranking, Learning-To-Rank

Recommending venues plays a critical rule in satisfying users' needs on
location-based social networks. Recent studies have explored the idea of
adopting collaborative ranking (CR) for recommendation, combining the idea of
learning to rank and collaborative filtering. However, CR suffers from the
sparsity problem, mainly because it associates similar users based on exact
matching of the venues in their check-in history. Even though research in
collaborative filtering has shown that considering auxiliary information such
as geographical influence, helps the model to alleviate the sparsity problem,
the same direction still needs to be explored in CR. In this work, we present a
CR framework that focuses on the top of the ranked list while integrating an
arbitrary number of similarity functions between venues as it learns the
model's parameters. We further introduce three example similarity measures
based on venues' contents and locations. Incorporating cross-venue similarity
measures into the model enhances the latent associations between users as
similar venues are also taken into account while associating users with each
other. Our experiments on the TREC Contextual Suggestion dataset show that our
proposed CR model beats other state-of-the-art venue suggestion methods.
Tangled Nature: A model of emergent structure and temporal mode among co-evolving agents
Date: 2018-07-11 00:00:00
Tasks: Sociology

Understanding systems level behaviour of many interacting agents is
challenging in various ways, here we'll focus on the how the interaction
between components can lead to hierarchical structures with different types of
dynamics, or causations, at different levels. We use the Tangled Nature model
to discuss the co-evolutionary aspects connecting the microscopic level of the
individual to the macroscopic systems level. At the microscopic level the
individual agent may undergo evolutionary changes due to mutations of
strategies. The micro-dynamics always run at a constant rate. Nevertheless, the
system's level dynamics exhibit a completely different type of intermittent
abrupt dynamics where major upheavals keep throwing the system between
meta-stable configurations. These dramatic transitions are described by a
log-Poisson time statistics. The long time effect is a collectively adapted of
the ecological network. We discuss the ecological and macroevolutionary
consequences of the adaptive dynamics and briefly describe work using the
Tangled Nature framework to analyse problems in economics, sociology,
innovation and sustainability
Geometric comparison of phylogenetic trees with different leaf sets
Date: 2018-07-11 00:00:00

The metric space of phylogenetic trees defined by Billera, Holmes, and
Vogtmann, which we refer to as BHV space, provides a natural geometric setting
for describing collections of trees on the same set of taxa. However, it is
sometimes necessary to analyze collections of trees on non-identical taxa sets
(i.e., with different numbers of leaves), and in this context it is not evident
how to apply BHV space. Davidson et al. recently approached this problem by
describing a combinatorial algorithm extending tree topologies to regions in
higher dimensional tree spaces, so that one can quickly compute which
topologies contain a given tree as partial data. In this paper, we refine and
adapt their algorithm to work for metric trees to give a full characterization
of the subspace of extensions of a subtree. We describe how to apply our
algorithm to define and search a space of possible supertrees and, for a
collection of tree fragments with different leaf sets, to measure their
compatibility.
A Formal Account of Effectiveness Evaluation and Ranking Fusion
Authors: Amigó Enrique, Giner Fernando, Mizzaro Stefano, Spina Damiano
Date: 2018-09-14 00:00:00
Tasks: Retrieval

This paper proposes a theoretical framework which models the information
provided by retrieval systems in terms of Information Theory. The proposed
framework allows to formalize: (i) system effectiveness as an information
theoretic similarity between system outputs and human assessments, and (ii)
ranking fusion as an information quantity measure. As a result, the proposed
effectiveness metric improves popular metrics in terms of formal constraints.
In addition, our empirical experiments suggest that it captures quality aspects
from traditional metrics, while the reverse is not true. Our work also advances
the understanding of theoretical foundations of the empirically known
phenomenon of effectiveness increase when combining retrieval system outputs in
an unsupervised manner.
Agglomerative Hierarchical Clustering Analysis of co/multi-morbidities
Date: 2018-07-11 00:00:00
Tasks: Clustering

Although co/multi-morbidities are associated with significant increase in
mortality, the lack of appropriate quantitative exploratory techniques often
impede their analysis. In the current study, we study the clustering of
multimorbid patients in the Texas patient population. To this end we employ
agglomerative hierarchical clustering to find clusters within the patient
population. The analysis revealed the presence of nine distinct, clinically
relevant clusters of co/multi-morbidities within the study population of
interest. This technique provides a quantitative exploratory analysis of the
co/multi-morbidities present in a specific population.
Analyzing P300 Distractors for Target Reconstruction
Date: 2018-07-11 00:00:00

P300-based brain-computer interfaces (BCIs) are often trained per-user and
per-application space. Training such models requires ground truth knowledge of
target and non-target stimulus categories during model training, which imparts
bias into the model. Additionally, not all non-targets are created equal; some
may contain visual features that resemble targets or may otherwise be visually
salient. Current research has indicated that non-target distractors may elicit
attenuated P300 responses based on the perceptual similarity of these
distractors to the target category. To minimize this bias, and enable a more
nuanced analysis, we use a generalized BCI approach that is fit to neither user
nor task. We do not seek to improve the overall accuracy of the BCI with our
generalized approach; we instead demonstrate the utility of our approach for
identifying target-related image features. When combined with other intelligent
agents, such as computer vision systems, the performance of the generalized
model equals that of the user-specific models, without any user specific data.
Efficient keyword spotting using time delay neural networks
Date: 2018-08-28 00:00:00
Tasks: Keyword Spotting, Transfer Learning

This paper describes a novel method of live keyword spotting using a
two-stage time delay neural network. The model is trained using transfer
learning: initial training with phone targets from a large speech corpus is
followed by training with keyword targets from a smaller data set. The accuracy
of the system is evaluated on two separate tasks. The first is the freely
available Google Speech Commands dataset. The second is an in-house task
specifically developed for keyword spotting. The results show significant
improvements in false accept and false reject rates in both clean and noisy
environments when compared with previously known techniques. Furthermore, we
investigate various techniques to reduce computation in terms of
multiplications per second of audio. Compared to recently published work, the
proposed system provides up to 89% savings on computational complexity.
Exactly Decoupled Kalman Filtering for Multitarget State Estimation with Sensor Bias
Date: 2019-10-15 00:00:00
Tasks: State Estimation

The problem of multisensor multitarget state estimation in the presence of
constant but unknown sensor biases is investigated. The classical approach to
this problem is to augment the state vector to include the states of all the
targets and the sensor biases, and then implement an augmented state Kalman
filter (ASKF). In this paper, we propose a novel decoupled Kalman filtering
algorithm. The decoupled Kalman filtering first processes each target in a
separate branch, namely the single-target Kalman filtering branch, where the
single-target states and the sensor biases are estimated. Then the bias
estimate is refined by fusing the former bias estimates across all the
single-target Kalman filtering branches. Finally, the refined bias estimate is
fed back to each single-target Kalman filtering branch to improve the target
state estimation. We prove that the proposed decoupled Kalman filtering is
exactly equivalent to the ASKF in terms of the estimation results under a usual
initial condition. The equivalence is also confirmed via the numerical example.
Moreover, we further validate the proposed algorithm using the field
experimental data of a multistatic passive radar.
Testing of Binary Regime Switching Models using Squeeze Duration Analysis
Date: 2018-08-25 00:00:00
Tasks: Time Series, Time Series Analysis

We have developed a statistical technique to test the model assumption of
binary regime switching extension of the geometric Brownian motion (GBM) model
by proposing a new discriminating statistics. Given a time series data, we have
identified an admissible class of the regime switching candidate models for the
statistical inference. By performing several systematic experiments, we have
successfully shown that the sampling distribution of the test statistics
differs drastically, if the model assumption changes from GBM to Markov
modulated GBM, or to semi-Markov modulated GBM. Furthermore, we have
implemented this statistics for testing the regime switching hypothesis with
Indian sectoral indices.
Millimeter Wave Line-of-Sight Blockage Analysis
Date: 2018-07-12 00:00:00

Millimeter wave (mmWave) communication systems can provide high data rates
but the system performance may degrade significantly due to mobile blockers and
the user's own body. A high frequency of interruptions and long duration of
blockage may degrade the quality of experience. For example, delays of more
than about 10ms cause nausea to VR viewers. Macro-diversity of base stations
(BSs) has been considered a promising solution where the user equipment (UE)
can handover to other available BSs, if the current serving BS gets blocked.
However, an analytical model for the frequency and duration of dynamic blockage
events in this setting is largely unknown. In this thesis, we consider an open
park-like scenario and obtain closed-form expressions for the blockage
probability, expected frequency and duration of blockage events using
stochastic geometry. Our results indicate that the minimum density of BS that
is required to satisfy the Quality of Service (QoS) requirements of AR/VR and
other low latency applications is largely driven by blockage events rather than
capacity requirements. Placing the BS at a greater height reduces the
likelihood of blockage. We present a closed-form expression for the BS
density-height trade-off that can be used for network planning.
OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs
Date: 2018-11-13 00:00:00

Motivation: High-throughput sequencing of large immune repertoires has
enabled the development of methods to predict the probability of generation by
V(D)J recombination of T- and B-cell receptors of any specific nucleotide
sequence. These generation probabilities are very non-homogeneous, ranging over
20 orders of magnitude in real repertoires. Since the function of a receptor
really depends on its protein sequence, it is important to be able to predict
this probability of generation at the amino acid level. However, brute-force
summation over all the nucleotide sequences with the correct amino acid
translation is computationally intractable. The purpose of this paper is to
present a solution to this problem.
  Results: We use dynamic programming to construct an efficient and flexible
algorithm, called OLGA (Optimized Likelihood estimate of immunoGlobulin
Amino-acid sequences), for calculating the probability of generating a given
CDR3 amino acid sequence or motif, with or without V/J restriction, as a result
of V(D)J recombination in B or T cells. We apply it to databases of
epitope-specific T-cell receptors to evaluate the probability that a typical
human subject will possess T cells responsive to specific disease-associated
epitopes. The model prediction shows an excellent agreement with published
data. We suggest that OLGA may be a useful tool to guide vaccine design.
  Availability: Source code is available at https://github.com/zsethna/OLGA
Analysis of a Dynamic Voluntary Contribution Mechanism Public Good Game
Authors: Dmytro Bogatov
Date: 2018-07-12 00:00:00

I present a dynamic, voluntary contribution mechanism, public good game and derive its potential outcomes. In each period, players endogenously determine contribution productivity by engaging in costly investment. The level of contribution productivity carries from period to period, creating a dynamic link between periods. The investment mimics investing in the stock of technology for producing public goods such as national defense or a clean environment. After investing, players decide how much of their remaining money to contribute to provision of the public good, as in traditional public good games. I analyze three kinds of outcomes of the game: the lowest payoff outcome, the Nash Equilibria, and socially optimal behavior. In the lowest payoff outcome, all players receive payoffs of zero. Nash Equilibrium occurs when players invest any amount and contribute all or nothing depending on the contribution productivity. Therefore, there are infinitely many Nash Equilibria strategies. Finally, the socially optimal result occurs when players invest everything in early periods, then at some point switch to contributing everything. My goal is to discover and explain this point. I use mathematical analysis and computer simulation to derive the results.
Observations and perspectives on the prebiotic sequence evolution
Authors: Dirson Jian Li
Date: 2018-07-12 00:00:00

The post-genomic era has brought opportunities to bridge traditionally separate fields of early history of life and brought new insight into origin and evolution of biodiversity. According to distributions of codons in genome sequences, I found a relationship between the genetic code and the tree of life. This remote and profound relationship involves the origin and evolution of the genetic code and the diversification and expansion of genomes. Here, a prebiotic picture of the triplex nucleic acid evolution is proposed to explain the origin of the genetic code, where the transition from disorder to order in the origin of life might be due to the increasing stabilities of triplex base pairs. The codon degeneracy can be obtained in detail based on the coevolution of the genetic code with amino acids, or equivalently, the coevolution of tRNAs with aaRSs. This theory is based on experimental data such as the stability of triplex base pairs and the statistical features of genomic codon distributions. Several experimentally testable proposals have been developed. This study should be regarded as an exploratory attempt to reveal the early evolution of life based on sequence information in a statistical manner.
Unseeded low-rank graph matching by transform-based unsupervised point registration
Authors: Zhang Yuan
Date: 2018-07-12 00:00:00
Tasks: Graph Matching

The problem of learning a correspondence relationship between nodes of two
networks has drawn much attention of the computer science community and
recently that of statisticians. The unseeded version of this problem, in which
we do not know any part of the true correspondence, is a long-standing
challenge. For low-rank networks, the problem can be translated into an
unsupervised point registration problem, in which two point sets generated from
the same distribution are matchable by an unknown orthonormal transformation.
Conventional methods generally lack consistency guarantee and are usually
computationally costly.
  In this paper, we propose a novel approach to this problem. Instead of
simultaneously estimating the unknown correspondence and orthonormal
transformation to match up the two point sets, we match their distributions via
minimizing our designed loss function capturing the discrepancy between their
Laplace transforms, thus avoiding the optimization over all possible
correspondences. This dramatically reduces the dimension of the optimization
problem from $\Omega(n^2)$ parameters to $O(d^2)$ parameters, where $d$ is the
fixed rank, and enables convenient theoretical analysis. In this paper, we
provide arguably the first consistency guarantee and explicit error rate for
general low-rank models. Our method provides control over the computational
complexity ranging from $\omega(n)$ (any growth rate faster than $n$) to
$O(n^2)$ while pertaining consistency. We demonstrate the effectiveness of our
method through several numerical examples.
The Importance of Song Context and Song Order in Automated Music Playlist Generation
Authors: Vall Andreu, Quadrana Massimo, Schedl Markus, Widmer Gerhard
Date: 2018-07-12 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

The automated generation of music playlists can be naturally regarded as a
sequential task, where a recommender system suggests a stream of songs that
constitute a listening session. In order to predict the next song in a
playlist, some of the playlist models proposed so far consider the current and
previous songs in the playlist (i.e., the song context) and possibly the order
of the songs in the playlist. We investigate the impact of the song context and
the song order on next-song recommendations by conducting dedicated off-line
experiments on two datasets of hand-curated music playlists. Firstly, we
compare three playlist models, each able to consider a different song context
length: a popularity-based model, a song-based Collaborative Filtering (CF)
model and a Recurrent-Neural-Network-based model (RNN). We also consider a
model that predicts next songs at random as a reference. Secondly, we challenge
the RNN model (the only model from the first experiment able to consider the
song order) by manipulating the order of songs within playlists. Our results
indicate that the song context has a positive impact on the quality of
next-song recommendations, even though this effect can be masked by the bias
towards very popular songs. Furthermore, in our experiments the song order does
not appear as a crucial variable to predict better next-song recommendations.
Spatial Embedding Imposes Constraints on the Network Architectures of Neural Systems
Date: 2018-07-12 00:00:00
Tasks: Anatomy

A fundamental understanding of the network architecture of the brain is
necessary for the further development of theories explicating circuit function.
Perhaps as a derivative of its initial application to abstract informational
systems, network science provides many methods and summary statistics that
address the network's topological characteristics with little or no thought to
its physical instantiation. Recent progress has capitalized on quantitative
tools from network science to parsimoniously describe and predict neural
activity and connectivity across multiple spatial and temporal scales. Yet, for
embedded systems, physical laws can directly constrain processes of network
growth, development, and function, and an appreciation of those physical laws
is therefore critical to an understanding of the system. Recent evidence
demonstrates that the constraints imposed by the physical shape of the brain,
and by the mechanical forces at play in its development, have marked effects on
the observed network topology and function. Here, we review the rules imposed
by space on the development of neural networks and show that these rules give
rise to a specific set of complex topologies. We present evidence that these
fundamental wiring rules affect the repertoire of neural dynamics that can
emerge from the system, and thereby inform our understanding of network
dysfunction in disease. We also discuss several computational tools,
mathematical models, and algorithms that have proven useful in delineating the
effects of spatial embedding on a given networked system and are important
considerations for addressing future problems in network neuroscience. Finally,
we outline several open questions regarding the network architectures that
support circuit function, the answers to which will require a thorough and
honest appraisal of the role of physical space in brain network anatomy and
physiology.
Predicting Task and Subject Differences with Functional Connectivity and BOLD Variability
Date: 2018-07-12 00:00:00
Tasks: Functional Connectivity

Previous research has found that functional connectivity (FC) can accurately
predict the identity of a subject performing a task and the type of task being
performed. We replicate these results using a large dataset collected at the
OSU Center for Cognitive and Behavioral Brain Imaging. We also introduce a
novel perspective on task and subject identity prediction: BOLD Variability
(BV). Conceptually, BV is a region-specific measure based on the variance
within each brain region. BV is simple to compute, interpret, and visualize. We
show that both FC and BV are predictive of task and subject, even across
scanning sessions separated by multiple years. Subject differences rather than
task differences account for the majority of changes in BV and FC. Similar to
results in FC, we show that BV is reduced during cognitive tasks relative to
rest.