pwc-archive/papers-with-abstracts huggingface dataset:
Visual pathways from the perspective of cost functions and multi-task deep neural networks
Date: 2017-09-16 00:00:00

Vision research has been shaped by the seminal insight that we can understand
the higher-tier visual cortex from the perspective of multiple functional
pathways with different goals. In this paper, we try to give a computational
account of the functional organization of this system by reasoning from the
perspective of multi-task deep neural networks. Machine learning has shown that
tasks become easier to solve when they are decomposed into subtasks with their
own cost function. We hypothesize that the visual system optimizes multiple
cost functions of unrelated tasks and this causes the emergence of a ventral
pathway dedicated to vision for perception, and a dorsal pathway dedicated to
vision for action. To evaluate the functional organization in multi-task deep
neural networks, we propose a method that measures the contribution of a unit
towards each task, applying it to two networks that have been trained on either
two related or two unrelated tasks, using an identical stimulus set. Results
show that the network trained on the unrelated tasks shows a decreasing degree
of feature representation sharing towards higher-tier layers while the network
trained on related tasks uniformly shows high degree of sharing. We conjecture
that the method we propose can be used to analyze the anatomical and functional
organization of the visual system and beyond. We predict that the degree to
which tasks are related is a good descriptor of the degree to which they share
downstream cortical-units.
Eigenvector Centrality Distribution for Characterization of Protein Allosteric Pathways
Date: 2018-06-25 00:00:00

Determining the principal energy pathways for allosteric communication in
biomolecules, that occur as a result of thermal motion, remains challenging due
to the intrinsic complexity of the systems involved. Graph theory provides an
approach for making sense of such complexity, where allosteric proteins can be
represented as networks of amino acids. In this work, we establish the
eigenvector centrality metric in terms of the mutual information, as a mean of
elucidating the allosteric mechanism that regulates the enzymatic activity of
proteins. Moreover, we propose a strategy to characterize the range of the
physical interactions that underlie the allosteric process. In particular, the
well known enzyme, imidazol glycerol phosphate synthase (IGPS), is utilized to
test the proposed methodology. The eigenvector centrality measurement
successfully describes the allosteric pathways of IGPS, and allows to pinpoint
key amino acids in terms of their relevance in the momentum transfer process.
The resulting insight can be utilized for refining the control of IGPS
activity, widening the scope for its engineering. Furthermore, we propose a new
centrality metric quantifying the relevance of the surroundings of each
residue. In addition, the proposed technique is validated against experimental
solution NMR measurements yielding fully consistent results. Overall, the
methodologies proposed in the present work constitute a powerful and cost
effective strategy to gain insight on the allosteric mechanism of proteins.
Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms
Date: 2017-08-18 00:00:00
Tasks: Decision Making

Currently, diagnosis of skin diseases is based primarily on visual pattern
recognition skills and expertise of the physician observing the lesion. Even
though dermatologists are trained to recognize patterns of morphology, it is
still a subjective visual assessment. Tools for automated pattern recognition
can provide objective information to support clinical decision-making.
Noninvasive skin imaging techniques provide complementary information to the
clinician. In recent years, optical coherence tomography has become a powerful
skin imaging technique. According to specific functional needs, skin
architecture varies across different parts of the body, as do the textural
characteristics in OCT images. There is, therefore, a critical need to
systematically analyze OCT images from different body sites, to identify their
significant qualitative and quantitative differences. Sixty-three optical and
textural features extracted from OCT images of healthy and diseased skin are
analyzed and in conjunction with decision-theoretic approaches used to create
computational models of the diseases. We demonstrate that these models provide
objective information to the clinician to assist in the diagnosis of
abnormalities of cutaneous microstructure, and hence, aid in the determination
of treatment. Specifically, we demonstrate the performance of this methodology
on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)
from healthy tissue.
Self-Organization and The Origins of Life: The Managed-Metabolism Hypothesis
Date: 2017-05-18 00:00:00
Tasks: Management

The managed-metabolism hypothesis suggests that a cooperation barrier must be
overcome if self-producing chemical organizations are to transition from
non-life to life. This barrier prevents un-managed, self-organizing,
autocatalytic networks of molecular species from individuating into complex,
cooperative organizations. The barrier arises because molecular species that
could otherwise make significant cooperative contributions to the success of an
organization will often not be supported within the organization, and because
side reactions and other free-riding processes will undermine cooperation. As a
result, the barrier seriously limits the possibility space that can be explored
by un-managed organizations, impeding individuation, complex functionality and
the transition to life. The barrier can be overcome comprehensively by
appropriate management which implements a system of evolvable constraints. The
constraints support beneficial co-operators and suppress free riders. In this
way management can manipulate the chemical processes of an autocatalytic
organization, producing novel processes that serve the interests of the
organization as a whole and that could not arise and persist spontaneously in
an un-managed chemical organization. Management self-organizes because it is
able to capture some of the benefits that are produced when its management of
an autocatalytic organization promotes beneficial cooperation. Selection
therefore favours the emergence of managers that take over and manage chemical
organizations so as to overcome the cooperation barrier. The managed-metabolism
hypothesis shows that if management is to overcome the cooperation barrier
comprehensively, its interventions must be digitally coded. In this way, the
hypothesis accounts for the two-tiered structure of all living cells in which a
digitally-coded genetic apparatus manages an analogically-informed metabolism.
Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks
Date: 2018-02-24 00:00:00

Correlation-based Hebbian plasticity is thought to shape neuronal
connectivity during development and learning, whereas homeostatic plasticity
would stabilize network activity. Here we investigate another, new aspect of
this dichotomy: Can Hebbian associative properties also emerge as a network
effect from a plasticity rule based on homeostatic principles on the neuronal
level? To address this question, we simulated a recurrent network of leaky
integrate-and-fire neurons, in which excitatory connections are subject to a
structural plasticity rule based on firing rate homeostasis. We show that a
subgroup of neurons develop stronger within-group connectivity as a consequence
of receiving stronger external stimulation. In an experimentally
well-documented scenario we show that feature specific connectivity, similar to
what has been observed in rodent visual cortex, can emerge from such a
plasticity rule. The experience-dependent structural changes triggered by
stimulation are long-lasting and decay only slowly when the neurons are exposed
again to unspecific external inputs.
A machine learning approach to drug repositioning based on drug expression profiles: Applications to schizophrenia and depression/anxiety disorders
Date: 2017-12-12 00:00:00
Tasks: Drug Discovery

Development of new medications is a very lengthy and costly process. Finding
novel indications for existing drugs, or drug repositioning, can serve as a
useful strategy to shorten the development cycle. In this study, we present an
approach to drug discovery or repositioning by predicting indication for a
particular disease based on expression profiles of drugs, with a focus on
applications in psychiatry. Drugs that are not originally indicated for the
disease but with high predicted probabilities serve as good candidates for
repurposing. This framework is widely applicable to any chemicals or drugs with
expression profiles measured, even if the drug targets are unknown. It is also
highly flexible as virtually any supervised learning algorithms can be used. We
applied this approach to identify repositioning opportunities for schizophrenia
as well as depression and anxiety disorders. We applied various
state-of-the-art machine learning (ML) approaches for prediction, including
deep neural networks, support vector machines (SVM), elastic net, random forest
and gradient boosted machines. The performance of the five approaches did not
differ substantially, with SVM slightly outperformed the others. However,
methods with lower predictive accuracy can still reveal literature-supported
candidates that are of different mechanisms of actions. As a further
validation, we showed that the repositioning hits are enriched for psychiatric
medications considered in clinical trials. Notably, many top repositioning hits
are supported by previous preclinical or clinical studies. Finally, we propose
that ML approaches may provide a new avenue to explore drug mechanisms via
examining the variable importance of gene features.
Aftershocks following crash of currency exchange rate: The case of RUB/USD in 2014
Date: 2018-04-13 00:00:00

The dynamical behavior of the currency exchange rate after its large-scale
catastrophe is discussed through a case study of the rate of Russian rubles to
US dollars after its crash in 2014. It is shown that, similarly to the case of
the stock market crash, the relaxation is characterized by a power law, which
is in analogy with the Omori-Utsu law for earthquake aftershocks. The
waiting-time distribution is found to also obey a power law. Furthermore, the
event-event correlation is discussed, and the aging phenomenon and scaling
property are observed. Comments are made on (non-)Markovianity of the
aftershock process and on a possible relevance of glassy dynamics to the market
system after the crash.
Characterizing and Predicting Supply-side Engagement on Crowd-contributed Video Sharing Platforms
Authors: Mehrotra Rishabh, Bhattacharya Prasanta
Date: 2017-06-10 00:00:00

Video sharing and entertainment websites have rapidly grown in popularity and
now constitute some of the most visited websites on the Internet. Despite the
active user engagement on these online video-sharing platforms, most of recent
research on online media platforms have restricted themselves to networking
based social media sites, like Facebook or Twitter. We depart from previous
studies in the online media space that have focused exclusively on demand-side
user engagement, by modeling the supply-side of the crowd-contributed videos on
this platform. The current study is among the first to perform a large-scale
empirical study using longitudinal video upload data from a large online video
platform. The modeling and subsequent prediction of video uploads is made
complicated by the heterogeneity of video types (e.g. popular vs. niche video
genres), and the inherent time trend effects associated with media uploads. We
identify distinct genre-clusters from our dataset and employ a self-exciting
Hawkes point-process model on each of these clusters to fully specify and
estimate the video upload process. Additionally, we go beyond prediction to
disentangle potential factors that govern user engagement and determine the
video upload rates, which improves our analysis with additional explanatory
power. Our findings show that using a relatively parsimonious point-process
model, we are able to achieve higher model fit, and predict video uploads to
the platform with a higher accuracy than competing models. The findings from
this study can benefit platform owners in better understanding how their
supply-side users engage with their site over time. We also offer a robust
method for performing media upload prediction that is likely to be
generalizable across media platforms which demonstrate similar temporal and
genre-level heterogeneity.
An Empirical Study of Some Selected IR Models for Bengali Monolingual Information Retrieval
Authors: Sarkar Kamal, Gupta Avisek
Date: 2017-06-10 00:00:00
Tasks: Information Retrieval, Retrieval

This paper presents an evaluation and an analysis of some selected
information retrieval models for Bengali monolingual information retrieval
task. Two models, TF-IDF model and the Okapi BM25 model have been considered
for our study. The developed IR models are tested on FIRE ad hoc retrieval data
sets released for different years from 2008 to 2012 and the obtained results
have been reported in this paper.
Short-term effect of hyperbaric exposure on Ventilation: A Control Study of 12m-depth Single No-decompression Dive Experiment
Date: 2017-06-11 00:00:00

Objective: To study to what extent or durations of ventilation effect in a
single no-decompression dive of 12 meters to a diver. Methods: There are 29
healthy volunteers divers assigned into SCUBA diving of 12m-depth underwater
(the Experimental Group, EG)and chamber dive under 2.2 ATA for 20min (the
Control Group, CG) matched with the factors of the age,gender,BMI and Forced
Vital Capacity (FVC).Ventilation functions were measured by spirometer before
diving and in 1h and 24h of post-hyperbaric exposure. Used independent samples
T tests to compare the differences between the EG and CG.Analyzed of variance
through repeated measurement data of different time point before or after high
pressure exposure by SPSS 20.0. Results: The Inspiratory Reserve Volume(IRV)
rises while the Expiratory Reserve Volume(ERV) falls significantly in 1h after
high pressure release(p<0.05).So as with the Inspiratory Capacity (IC) and the
Vital Capacity (VC) increased accordingly. The Ratio of FEV1.0 to VC (FEV1.0%t)
is higher in CG than EG (t=-2.189,p=0.033) due to the change of VC. But the
effects did not last for 24 h after high pressure relief. Conclusions:
Ventilation is restricted during the 20min of hyperbaric exposure whether under
12m-depth water or in a 2.2ATA hyperbaric chamber. But the effect recovered
close to normal within 24 h. But the effect recovered close to normal within 24
h. The extent of restriction of underwater diving is larger than the dry air
hyperbaric chamber dive. Higher water medium density, submerged compressing
blood volume of lower limbs and raising inertia added by portable underwater
breathing apparatus all might be attributable to the ventilation effects.
Analysis of order book flows using a nonparametric estimation of the branching ratio matrix
Date: 2017-06-11 00:00:00

We introduce a new non parametric method that allows for a direct, fast and
efficient estimation of the matrix of kernel norms of a multivariate Hawkes
process, also called branching ratio matrix. We demonstrate the capabilities of
this method by applying it to high-frequency order book data from the EUREX
exchange. We show that it is able to uncover (or recover) various relationships
between all the first level order book events associated with some asset when
mapped to a 12-dimensional process. We then scale up the model so as to account
for events on two assets simultaneously and we discuss the joint high-frequency
dynamics.
RARD: The Related-Article Recommendation Dataset
Authors: Beel Joeran, Carevic Zeljko, Schaible Johann, Neusch Gabor
Date: 2017-06-20 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

Recommender-system datasets are used for recommender-system evaluations,
training machine-learning algorithms, and exploring user behavior. While there
are many datasets for recommender systems in the domains of movies, books, and
music, there are rather few datasets from research-paper recommender systems.
In this paper, we introduce RARD, the Related-Article Recommendation Dataset,
from the digital library Sowiport and the recommendation-as-a-service provider
Mr. DLib. The dataset contains information about 57.4 million recommendations
that were displayed to the users of Sowiport. Information includes details on
which recommendation approaches were used (e.g. content-based filtering,
stereotype, most popular), what types of features were used in content based
filtering (simple terms vs. keyphrases), where the features were extracted from
(title or abstract), and the time when recommendations were delivered and
clicked. In addition, the dataset contains an implicit item-item rating matrix
that was created based on the recommendation click logs. RARD enables
researchers to train machine learning algorithms for research-paper
recommendations, perform offline evaluations, and do research on data from Mr.
DLib's recommender system, without implementing a recommender system
themselves. In the field of scientific recommender systems, our dataset is
unique. To the best of our knowledge, there is no dataset with more (implicit)
ratings available, and that many variations of recommendation algorithms. The
dataset is available at http://data.mr-dlib.org, and published under the
Creative Commons Attribution 3.0 Unported (CC-BY) license.
Economics of limiting cumulative CO2 emissions
Date: 2017-06-12 00:00:00

Global warming from carbon dioxide (CO2) is known to depend on cumulative CO2
emissions. We introduce a model of global expenditures on limiting cumulative
CO2 emissions, taking into account effects of decarbonization and rising global
income and making an approximation to the marginal abatement costs (MAC) of
CO2. Discounted mitigation expenditures are shown to be a convex function of
cumulative CO2 emissions. We also consider minimum-expenditure solutions for
meeting cumulative emissions goals, using a regularized variational method
yielding an initial value problem in the integrated decarbonization rate. A
quasi-stationary solution to this problem can be obtained for a special case,
yielding decarbonization rate that is proportional to annual CO2 emissions.
Minimum-expenditure trajectories in scenarios where CO2 emissions decrease must
begin with rapid decarbonization at rate decreasing with time. Due to the shape
of global MAC the fraction of global income spent on CO2 mitigation ("burden")
generally increases with time, as cheaper avenues for mitigation are exhausted.
Therefore failure to rapidly decarbonize early on reduces expenditures by a
small fraction (on the order of 0.01 %) of income in the present, but leads to
much higher burden to future generations (on the order of 1 % of income).
Awkt: A Physicochemical Parameter Estimation Tool for Capillary Zone Electrophoresis
Authors: Christian Löffeld
Date: 2017-06-12 00:00:00
Tasks: Model Selection, parameter estimation

We present a computational parameter estimation tool aimed at assisting molecule characterization applying nonlinear least-squares optimization to a specific closed-form solution of a capillary electrophoretic transport problem. Numerical estimates for the respective physicochemical parameters are generated. Among other features, the tool facilitates automated and user-controllable hypothesis testing and model selection. It can also be used to perform fully automated batch-processing in true concurrent mode.
Plant responses to auxin signals: an operating principle for dynamical sensitivity yet high resilience
Date: 2017-06-12 00:00:00
Tasks: Sensitivity

Plants depend on the signaling of the phytohormone auxin for their
development and for responding to environmental perturbations. The associated
biomolecular signaling network involves a negative feedback at the level of the
Aux/IAA proteins which mediate the influence of auxin (the signal) on the ARF
transcription factors (the drivers of the response). To probe the role of this
feedback, we consider alternative in silico signaling networks implementing
different operating principles. By a comparative analysis, we find that the
presence of a negative regulatory feedback loop allows the system to have a far
larger sensitivity in its dynamical response to auxin. At the same time, this
sensitivity does not prevent the system from being highly resilient. Given this
insight, we reconsider previously published models and build a new quantitative
and calibrated biomolecular model of auxin signaling.
Decline of long-range temporal correlations in the human brain during sustained wakefulness
Date: 2017-06-12 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG)

Sleep is crucial for daytime functioning, cognitive performance and general
well-being. These aspects of daily life are known to be impaired after extended
wake, yet, the underlying neuronal correlates have been difficult to identify.
Accumulating evidence suggests that normal functioning of the brain is
characterized by long-range temporal correlations (LRTCs) in cortex, which are
supportive for decision-making and working memory tasks.
  Here we assess LRTCs in resting state human EEG data during a 40-hour sleep
deprivation experiment by evaluating the decay in autocorrelation and the
scaling exponent of the detrended fluctuation analysis from EEG amplitude
fluctuations. We find with both measures that LRTCs decline as sleep
deprivation progresses. This decline becomes evident when taking changes in
signal power into appropriate consideration.
  Our results demonstrate the importance of sleep to maintain LRTCs in the
human brain. In complex networks, LRTCs naturally emerge in the vicinity of a
critical state. The observation of declining LRTCs during wake thus provides
additional support for our hypothesis that sleep reorganizes cortical networks
towards critical dynamics for optimal functioning.
The interplay between long- and short-range temporal correlations shapes cortex dynamics across vigilance states
Date: 2017-06-12 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG)

Increasing evidence suggests that cortical dynamics during wake exhibits
long-range temporal correlations suitable to integrate inputs over extended
periods of time to increase the signal-to-noise ratio in decision-making and
working memory tasks. Accordingly, sleep has been suggested as a state
characterized by a breakdown of long-range correlations; detailed measurements
of neuronal timescales that support this view, however, have so far been
lacking. Here we show that the long timescales measured at the individual
neuron level in freely-behaving rats during the awake state are abrogated
during non-REM (NREM) sleep. We provide evidence for the existence of two
distinct states in terms of timescale dynamics in cortex: one which is
characterized by long timescales which dominate during wake and REM sleep, and
a second one characterized by the absence of long-range temporal correlations
which characterizes NREM sleep. We observe that both timescale regimes can
co-exist and, in combination, lead to an apparent gradual decline of long
timescales during extended wake which is restored after sleep. Our results
provide a missing link between the observed long timescales in individual
neuron fluctuations during wake and the reported absence of long-term
correlations during deep sleep in EEG and fMRI studies. They furthermore
suggest a network-level function of sleep, to reorganize cortical networks
towards states governed by slow cortex dynamics to ensure optimal function for
the time awake.