pwc-archive/papers-with-abstracts huggingface dataset:
Options as Silver Bullets: Valuation of Term Loans, Inventory Management, Emissions Trading and Insurance Risk Mitigation using Option Theory
Authors: Ravi Kashyap
Date: 2016-09-06 00:00:00
Tasks: Management, Time Series, Time Series Analysis

Models to price long term loans in the securities lending business are developed. These longer horizon deals can be viewed as contracts with optionality embedded in them. This insight leads to the usage of established methods from derivatives theory to price such contracts. Numerical simulations are used to demonstrate the practical applicability of these models. The techniques advanced here can lead to greater synergies between the management of derivative and delta-one trading desks, perhaps even being able to combine certain aspects of the day to day operations of these seemingly disparate entities. These models are part of one of the least explored, yet profit laden, areas of modern investment management. A heuristic is developed to mitigate any loss of information, which might set in when parameters are estimated first and then the valuations are performed, by directly calculating valuations using the historical time series. This approach to valuations can lead to reduced models errors, robust estimation systems, greater financial stability and economic strength. An illustration is provided regarding how the methodologies developed here could be useful for inventory management, emissions trading and insurance risk mitigation. All these techniques could have applications for dealing with other financial instruments, non-financial commodities and many forms of uncertainty.
The Complexity of Molecular Interactions and Bindings between Cyclic Peptide and Inhibit Polymerase A and B1 (PAC-PB1N) H1N1
Date: 2015-10-27 00:00:00

The influenza/H1N1 virus has caused hazard in the public health of many
countries. Hence, existing influenza drugs could not cope with H1N1 infection
due to the high mutation rate of the virus. In this respect, new method to
block the virus was devised. The polymerase PAC-PB1N enzyme is responsible for
the replication of H1N1 virus. Thus, novel inhibitors were developed to ward
off the functionality of the enzyme. In this research, cyclic peptides has been
chosen to inhibit PAC-PB1N due to its proven stability in reaching the drug
target. Thus, computational method for elucidating the molecular interaction
between cyclic peptides and PAC-PB1N has been developed by using the LigX tools
from MOE 2008.10 software. The tools could render the bindings that involved in
the interactions. The interactions between individual amino acid in the
inhibitor and enzyme could be seen as well. Thus, the peptide sequences of
CKTTC and CKKTC were chosen as the lead compounds. In this end, the feasibility
of cyclic peptides to act as drug candidate for H1N1 could be exposed by the 2d
and 3d modeling of the molecular interactions.
Deterministic Criteria for the Absence and Existence of Arbitrage in Multi-Dimensional Diffusion Markets
Date: 2017-12-21 00:00:00

We derive deterministic criteria for the existence and non-existence of
equivalent (local) martingale measures for financial markets driven by
multi-dimensional time-inhomogeneous diffusions. Our conditions can be used to
construct financial markets in which the \emph{no unbounded profit with bounded
risk} condition holds, while the classical \emph{no free lunch with vanishing
risk} condition fails.
The canonical semantic network supports residual language function in chronic post-stroke aphasia
Date: 2016-10-26 00:00:00

Current theories of language recovery after stroke are limited by a reliance
on small studies. Here, we aimed to test predictions of current theory and
resolve inconsistencies regarding right hemispheric contributions to long-term
recovery. We first defined the canonical semantic network in 43 healthy
controls. Then, in a group of 43 patients with chronic post-stroke aphasia, we
tested whether activity in this network predicted performance on measures of
semantic comprehension, naming, and fluency while controlling for lesion volume
effects. Canonical network activation accounted for 22-33% of the variance in
language test scores. Whole-brain analyses corroborated these findings, and
revealed a core set of regions showing positive relationships to all language
measures. We next evaluated the relationship between activation magnitudes in
left and right hemispheric portions of the network, and how right hemispheric
activation related to the extent of left hemispheric damage. Activation
magnitudes in the each hemispheric network were strongly correlated, but four
right frontal regions showed heightened activity in patients with large
lesions. Activity in two of these regions (inferior frontal gyrus pars
opercularis and supplementary motor area) was associated with better language
abilities in patients with larger lesions, but poorer language abilities in
patients with smaller lesions. Our results indicate that bilateral language
networks support language processing after stroke, and that right hemispheric
activations related to extensive left hemisphere damage occur outside of the
canonical semantic network and differentially contribute to in their
relationship to behavior depending on the extent of left hemispheric damage
A superhedging approach to stochastic integration
Date: 2017-09-28 00:00:00

Using Vovk's outer measure, which corresponds to a minimal superhedging
price, the existence of quadratic variation is shown for "typical price paths"
in the space of c\`adl\`ag functions possessing a mild restriction on the jumps
directed downwards. In particular, this result includes the existence of
quadratic variation of "typical price paths" in the space of non-negative
c\`adl\`ag paths and implies the existence of quadratic variation in the sense
of F\"ollmer quasi surely under all martingale measures. Based on the robust
existence of the quadratic variation, a model-free It\^o integration is
developed.
A Flexible Recommendation System for Cable TV
Authors: Goncalves Diogo, Costa Miguel, Couto Francisco M.
Date: 2016-09-13 00:00:00
Tasks: Diversity, Learning-To-Rank, Recommendation Systems

Recommendation systems are being explored by Cable TV operators to improve
user satisfaction with services, such as Live TV and Video on Demand (VOD)
services. More recently, Catch-up TV has been introduced, allowing users to
watch recent broadcast content whenever they want to. These services give users
a large set of options from which they can choose from, creating an information
overflow problem. Thus, recommendation systems arise as essential tools to
solve this problem by helping users in their selection, which increases not
only user satisfaction but also user engagement and content consumption. In
this paper we present a learning to rank approach that uses contextual
information and implicit feedback to improve recommendation systems for a Cable
TV operator that provides Live and Catch-up TV services. We compare our
approach with existing state-of-the-art algorithms and show that our approach
is superior in accuracy, while maintaining high scores of diversity and
serendipity.
A Large-Scale Characterization of User Behaviour in Cable TV
Authors: Goncalves Diogo, Costa Miguel, Couto Francisco M.
Date: 2016-09-13 00:00:00
Tasks: Recommendation Systems

Nowadays, Cable TV operators provide their users multiple ways to watch TV
content, such as Live TV and Video on Demand (VOD) services. In the last years,
Catch-up TV has been introduced, allowing users to watch recent broadcast
content whenever they want to. Understanding how the users interact with such
services is important to develop solutions that may increase user satisfaction
, user engagement and user consumption. In this paper, we characterize, for the
first time, how users interact with a large European Cable TV operator that
provides Live TV, Catch-up TV and VOD services. We analyzed many
characteristics, such as the service usage, user engagement, program type,
program genres and time periods. This characterization will help us to have a
deeper understanding on how users interact with these different services, that
may be used to enhance the recommendation systems of Cable TV providers.
A phenomenological spatial model for macro-ecological patterns in species-rich ecosystems
Date: 2016-09-12 00:00:00
Tasks: Numerical Integration

Over the last few decades, ecologists have come to appreciate that key
ecological patterns, which describe ecological communities at relatively large
spatial scales, are not only scale dependent, but also intimately intertwined.
The relative abundance of species, which informs us about the commonness and
rarity of species, changes its shape from small to large spatial scales. The
average number of species as a function of area has a steep initial increase,
followed by decreasing slopes at large scales. Finally, if we find a species in
a given location, it is more likely we find an individual of the same species
close-by, rather than farther apart. Such spatial turnover depends on the
geographical distribution of species, which often are spatially aggregated.
This reverberates on the abundances as well as the richness of species within a
region, but so far it has been difficult to quantify such relationships. Within
a neutral framework, which considers all individuals competitively equivalent,
we introduce a spatial stochastic model, which phenomenologically accounts for
birth, death, immigration and local dispersal of individuals. We calculate the
pair correlation function, which encapsulates spatial turnover, and the
conditional probability to find a species with a certain population within a
given circular area. Also, we calculate the macro-ecological patterns, which we
have referred to above, and compare the analytical formul{\ae} with the
numerical integration of the model. Finally, we contrast the model predictions
with the empirical data for two lowland tropical forest inventories, showing
always a good agreement.
Renormalization Group Transformation for Hamiltonian Dynamical Systems in Biological Networks
Date: 2016-09-10 00:00:00

We apply the renormalization group theory to the dynamical systems with the
simplest example of basic biological motifs. This includes the interpretation
of complex networks as the perturbation to simple network. This is the first
step to build our original framework to infer the properties of biological
networks, and the basis work to see its effectiveness to actual complex
systems.
Covariance of random stock prices in the Stochastic Dividend Discount Model
Date: 2017-04-21 00:00:00

Dividend discount models have been developed in a deterministic setting. Some
authors (Hurley and Johnson, 1994 and 1998; Yao, 1997) have introduced
randomness in terms of stochastic growth rates, delivering closed-form
expressions for the expected value of stock prices. This paper extends such
previous results by determining a formula for the covariance between random
stock prices when the dividends' rates of growth are correlated. The formula is
eventually applied to real market data.
Quantifying the informativeness for biomedical literature summarization: An itemset mining method
Authors: Moradi Milad, Ghadiri Nasser
Date: 2017-05-26 00:00:00
Tasks: Informativeness, Sentence, Text Summarization

Objective: Automatic text summarization tools can help users in the
biomedical domain to access information efficiently from a large volume of
scientific literature and other sources of text documents. In this paper, we
propose a summarization method that combines itemset mining and domain
knowledge to construct a concept-based model and to extract the main subtopics
from an input document. Our summarizer quantifies the informativeness of each
sentence using the support values of itemsets appearing in the sentence.
Methods: To address the concept-level analysis of text, our method initially
maps the original document to biomedical concepts using the UMLS. Then, it
discovers the essential subtopics of the text using a data mining technique,
namely itemset mining, and constructs the summarization model. The employed
itemset mining algorithm extracts a set of frequent itemsets containing
correlated and recurrent concepts of the input document. The summarizer selects
the most related and informative sentences and generates the final summary.
Results: We evaluate the performance of our itemset-based summarizer using the
Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, performing a
set of experiments. The results show that the itemset-based summarizer performs
better than the compared methods. The itemset-based summarizer achieves the
best scores for all the assessed ROUGE metrics . Conclusion: Compared to the
statistical, similarity, and word frequency methods, the proposed method
demonstrates that the summarization model obtained from the concept extraction
and itemset mining provides the summarizer with an effective metric for
measuring the informative content of sentences. This can lead to an improvement
in the performance of biomedical literature summarization.
Bulging brains
Date: 2016-09-11 00:00:00

Brain swelling is a serious condition associated with an accumulation of
fluid inside the brain that can be caused by trauma, stroke, infection, or
tumors. It increases the pressure inside the skull and reduces blood and oxygen
supply. To relieve the intracranial pressure, neurosurgeons remove part of the
skull and allow the swollen brain to bulge outward, a procedure known as
decompressive craniectomy. Decompressive craniectomy has been preformed for
more than a century; yet, its effects on the swollen brain remain poorly
understood. Here we characterize the deformation, strain, and stretch in
bulging brains using the nonlinear field theories of mechanics. Our study shows
that even small swelling volumes of 28 to 56 ml induce maximum principal
strains in excess of 30%. For radially outward-pointing axons, we observe
maximal normal stretches of 1.3 deep inside the bulge and maximal tangential
stretches of 1.3 around the craniectomy edge. While the stretch magnitude
varies with opening site and swelling region, our study suggests that the
locations of maximum stretch are universally shared amongst all bulging brains.
Our model can inform neurosurgeons and rationalize the shape and position of
the skull opening, with the overall goal to reduce brain damage and improve the
structural and functional outcomes of decompressive craniectomy in trauma
patients.
E3 : Keyphrase based News Event Exploration Engine
Authors: Jain Nikita, Gupta Swati, Patel Dhaval
Date: 2016-09-11 00:00:00

This paper presents a novel system E3 for extracting keyphrases from news
content for the purpose of offering the news audience a broad overview of news
events, with especially high content volume. Given an input query, E3 extracts
keyphrases and enrich them by tagging, ranking and finding role for frequently
associated keyphrases. Also, E3 finds the novelty and activeness of keyphrases
using news publication date, to identify the most interesting and informative
keyphrases.
The Solution to Science's Replication Crisis
Date: 2016-09-11 00:00:00

The solution to science's replication crisis is a new ecosystem in which
scientists sell what they learn from their research. In each pairwise
transaction, the information seller makes (loses) money if he turns out to be
correct (incorrect). Responsibility for the determination of correctness is
delegated, with appropriate incentives, to the information purchaser. Each
transaction is brokered by a central exchange, which holds money from the
anonymous information buyer and anonymous information seller in escrow, and
which enforces a set of incentives facilitating the transfer of useful, bluntly
honest information from the seller to the buyer. This new ecosystem, capitalist
science, directly addresses socialist science's replication crisis by
explicitly rewarding accuracy and penalizing inaccuracy.
Bayesian Model Averaging for Ensemble-Based Estimates of Solvation Free Energies
Date: 2016-12-15 00:00:00

This paper applies the Bayesian Model Averaging (BMA) statistical ensemble
technique to estimate small molecule solvation free energies. There is a wide
range of methods available for predicting solvation free energies, ranging from
empirical statistical models to ab initio quantum mechanical approaches. Each
of these methods is based on a set of conceptual assumptions that can affect
predictive accuracy and transferability. Using an iterative statistical
process, we have selected and combined solvation energy estimates using an
ensemble of 17 diverse methods from the fourth Statistical Assessment of
Modeling of Proteins and Ligands (SAMPL) blind prediction study to form a
single, aggregated solvation energy estimate. The ensemble design process
evaluates the statistical information in each individual method as well as the
performance of the aggregate estimate obtained from the ensemble as a whole.
Methods that possess minimal or redundant information are pruned from the
ensemble and the evaluation process repeats until aggregate predictive
performance can no longer be improved. We show that this process results in a
final aggregate estimate that outperforms all individual methods by reducing
estimate errors by as much as 91% to 1.2 kcal/mol accuracy. We also compare our
iterative refinement approach to other statistical ensemble approaches and
demonstrate that this iterative process reduces estimate errors by as much as
61%. This work provides a new approach for accurate solvation free energy
prediction and lays the foundation for future work on aggregate models that can
balance computational cost with prediction accuracy.
Identification of Human Proteins vulnerable to multiple Organisms and their disease associations
Date: 2016-09-12 00:00:00

While most studies emphasize on certain aspects of Pathogen-Host Interactions
(PHI), such as the preferential attachment of bacteria or virus to its human
receptor homolog, studies have attempted to methodically classify interactions
among pathogenic proteins and their host proteins. Here we have analyzed 182
pathogens from The Pathogen-Host Interaction Search Tool (PHISTO) and could
identify the proteins/protein coding genes that act on both virus and bacteria.
Importantly there were few proteins viz. P53 (Tumor protein p53), NFKB1
(Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1), GBLP
(Guanine nucleotide-binding protein subunit beta-2-like-1), TOX4 (TOX high
mobility group box family member 4), PDIA1 (Protein disulfide-isomerase
precursor), MHY9 (Myosin 9), RAC1 (Ras-related C3 botulinum toxin substrate 1),
CCAR2 (Cell cycle and apoptosis regulator protein 2) and ILF3 (Interleukin
enhancer binding factor 3) that were more susceptible to both bacterial and
viral pathogens. Identification of such important interacting proteins (IIPs)
can elicit significant insights for better understanding the molecular
mechanisms of such pathogens that interact with the human host.
Differentially Categorized Structural Connectome Hubs are Involved in Differential Microstructural Basis and Functional Implications and Contribute to Individual Identification
Date: 2016-09-12 00:00:00
Tasks: Diffusion  MRI

Human brain structural networks contain sets of centrally embedded hub
regions that enable efficient information communication. However, it remains
largely unknown about categories of structural brain hubs and their
microstructural, functional and cognitive characteristics as well as
contributions to individual identification. Here, we employed three multi-modal
imaging data sets with structural MRI, diffusion MRI and resting-state
functional MRI to construct individual structural brain networks, identify
brain hubs based on eight commonly used graph-nodal metrics, and perform
comprehensive validation analysis. We found three categories of structural hubs
in the brain networks, namely, aggregated, distributed and connector hubs.
Spatially, these distinct categories of hubs were primarily located in the
default-mode system and additionally in the visual and limbic systems for
aggregated hubs, in the frontoparietal system for distributed hubs, and in the
sensorimotor and ventral attention systems for connector hubs. Importantly,
these three categories of hubs exhibited various distinct characteristics, with
the highest level of microstructural organization in the aggregated hubs, the
largest wiring cost and topological vulnerability in the distributed hubs, and
the highest functional associations and cognitive flexibility in the connector
hubs, although they behaved better regarding these characteristics compared to
non-hubs. Finally, all three categories of hub indices displayed high
across-session spatial similarities and acted as a structural fingerprint with
high predictive rates (100%, 100% and 84.2%) for individual identification.
Collectively, our findings highlighted three categories of brain hubs with
differential microstructural, functional and cognitive associations, which may
shed light on the topological mechanisms of the human connectome.
The Informational Content of the Limit Order Book: An Empirical Study of Prediction Markets
Date: 2016-09-12 00:00:00
Tasks: Prediction

In this paper I empirically investigate prediction markets for binary
options. Advocates of prediction markets have suggested that asset prices are
consistent estimators of the "true" probability of a state of the world being
realized. I test whether the market reaches a "consensus." I find little
evidence for convergence in beliefs. I then determine whether an econometrician
using data beyond execution prices can leverage this data to estimate the
consensus belief. I use an incomplete specification of equilibrium outcomes to
derive bounds on beliefs from order submission decisions. Interval estimates of
mean beliefs cannot exclude aggregate beliefs equal to 0.5.
A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing
Date: 2016-09-12 00:00:00
Tasks: Boundary Detection

Most cortical inhibitory cell types exclusively express one of three genes,
parvalbumin, somatostatin and 5HT3a. The visual responses of cortical neurons
are affected not only by local cues, but also by visual context. As the
inhibitory neuron types have distinctive synaptic sources and targets over
different spatial extents and from different areas, we conjecture that they
possess distinct roles in contextual processing. We use modeling to relate
structural information to function in primary visual cortex (V1) of the mouse,
and investigate their role in contextual visual processing. Our findings are
threefold. First, the inhibition mediated by parvalbumin positive (PV) cells
mediates local processing and could underlie their role in boundary detection.
Second, the inhibition mediated by somatostatin-positive (SST) cells
facilitates longer range spatial competition among receptive fields. Third,
non-specific top-down modulation to interneurons expressing vasoactive
intestinal polypeptide (VIP), a subclass of 5HT3a neurons, can selectively
enhance V1 responses.