pwc-archive/papers-with-abstracts huggingface dataset:
Multivariate Density Modeling for Retirement Finance
Date: 2017-09-12 00:00:00

Prior to the financial crisis mortgage securitization models increased in
sophistication as did products built to insure against losses. Layers of
complexity formed upon a foundation that could not support it and as the
foundation crumbled the housing market followed. That foundation was the
Gaussian copula which failed to correctly model failure-time correlations of
derivative securities in duress. In retirement, surveys suggest the greatest
fear is running out of money and as retirement decumulation models become
increasingly sophisticated, large financial firms and robo-advisors may
guarantee their success. Similar to an investment bank failure the event of
retirement ruin is driven by outliers and correlations in times of stress. It
would be desirable to have a foundation able to support the increased
complexity before it forms however the industry currently relies upon similar
Gaussian (or lognormal) dependence structures. We propose a multivariate
density model having fixed marginals that is tractable and fits data which are
skewed, heavy-tailed, multimodal, i.e., of arbitrary complexity allowing for a
rich correlation structure. It is also ideal for stress-testing a retirement
plan by fitting historical data seeded with black swan events. A preliminary
section reviews all concepts before they are used and fully documented C/C++
source code is attached making the research self-contained. Lastly, we take the
opportunity to challenge existing retirement finance dogma and also review some
recent criticisms of retirement ruin probabilities and their suggested
replacement metrics.
Query Completion Using Bandits for Engines Aggregation
Authors: Durand Audrey, Beaumont Jean-Alexandre, Gagne Christian, Lemay Michel, Paquet Sebastien
Date: 2017-09-13 00:00:00
Tasks: Diversity

Assisting users by suggesting completed queries as they type is a common
feature of search systems known as query auto-completion. A query
auto-completion engine may use prior signals and available information (e.g.,
user is anonymous, user has a history, user visited the site before the search
or not, etc.) in order to improve its recommendations. There are many possible
strategies for query auto-completion and a challenge is to design one optimal
engine that considers and uses all available information. When different
strategies are used to produce the suggestions, it becomes hard to rank these
heterogeneous suggestions. An alternative strategy could be to aggregate
several engines in order to enhance the diversity of recommendations by
combining the capacity of each engine to digest available information
differently, while keeping the simplicity of each engine. The main objective of
this research is therefore to find such mixture of query completion engines
that would beat any engine taken alone. We tackle this problem under the
bandits setting and evaluate four strategies to overcome this challenge.
Experiments conducted on three real datasets show that a mixture of engines can
outperform a single engine.
Is Smaller Better: A Proposal To Consider Bacteria For Biologically Inspired Modeling
Date: 2017-09-12 00:00:00

Bacteria are easily characterizable model organisms with an impressively
complicated set of capabilities. Among their capabilities is quorum sensing, a
detailed cell-cell signaling system that may have a common origin with
eukaryotic cell-cell signaling. Not only are the two phenomena similar, but
quorum sensing, as is the case with any bacterial phenomenon when compared to
eukaryotes, is also easier to study in depth than eukaryotic cell-cell
signaling. This ease of study is a contrast to the only partially understood
cellular dynamics of neurons. Here we review the literature on the strikingly
neuron-like qualities of bacterial colonies and biofilms, including ion-based
and hormonal signaling, and action potential-like behavior. This allows them to
feasibly act as an analog for neurons that could produce more detailed and more
accurate biologically-based computational models. Using bacteria as the basis
for biologically feasible computational models may allow models to better
harness the tremendous ability of biological organisms to make decisions and
process information. Additionally, principles gleaned from bacterial function
have the potential to influence computational efforts divorced from biology,
just as neuronal function has in the abstract influenced countless machine
learning efforts.
The influence of solvent representation on nuclear shielding calculations of protonation states of small biological molecules
Date: 2017-09-13 00:00:00

In this study, we assess the influence of solvation on the accuracy and
reliability of nuclear shielding calculations for amino acids in comparison to
experimental data. We focus particularly on the performance of solvation
methods for different protonation states, as biological molecules occur almost
exclusively in aqueous solution and are subject to protonation with pH. We
identify significant shortcomings of current implicit solvent models and
present a hybrid solvation approach that improves agreement with experimental
data by taking into account the presence of direct interactions between amino
acid protonation state and water molecules.
Welfare effects of information and rationality in portfolio decisions under parameter uncertainty
Date: 2017-09-13 00:00:00
Tasks: Management

We analyze and quantify, in a financial market with parameter uncertainty and
for a Constant Relative Risk Aversion investor, the utility effects of two
different boundedly rational (i.e., sub-optimal) investment strategies (namely,
myopic and unconditional strategies) and compare them between each other and
with the utility effect of full information. We show that effects are mainly
caused by full information and predictability, being the effect of learning
marginal. We also investigate the saver's decision of whether to manage her/his
portfolio personally (DIY investor) or hire, against the payment of a
management fee, a professional investor and find that delegation is mainly
motivated by the belief that professional advisors are, depending on investment
horizon and risk aversion, either better informed ("insiders") or more capable
of gathering and processing information rather than their ability of learning
from financial data. In particular, for very short investment horizons,
delegation is primarily, if not exclusively, motivated by the beliefs that
professional investors are better informed.
Registering the evolutionary history in individual-based models of speciation
Date: 2017-12-19 00:00:00

Understanding the emergence of biodiversity patterns in nature is a central
problem in biology. Theoretical models of speciation have addressed this
question in the macroecological scale, but little has been investigated in the
macroevolutionary context. Knowledge of the evolutionary history allows the
study of patterns underlying the processes considered in these models,
revealing their signatures and the role of speciation and extinction in shaping
macroevolutionary patterns. In this paper we introduce two algorithms to record
the evolutionary history of populations in individual-based models of
speciation, from which genealogies and phylogenies can be constructed. The
first algorithm relies on saving ancestral-descendant relationships, generating
a matrix that contains the times to the most recent common ancestor between all
pairs of individuals at every generation (the Most Recent Common Ancestor Time
matrix, MRCAT). The second algorithm directly records all speciation and
extinction events throughout the evolutionary process, generating a matrix with
the true phylogeny of species (the Sequential Speciation and Extinction Events,
SSEE). We illustrate the use of these algorithms in a spatially explicit
individual-based model of speciation. We compare the trees generated via MRCAT
and SSEE algorithms with trees inferred by methods that use only genetic
distance among extant species, commonly used in empirical studies and applied
here to simulated genetic data. Comparisons between tress are performed with
metrics describing the overall topology, branch length distribution and
imbalance of trees. We observe that both MRCAT and distance-based trees differ
from the true phylogeny, with the first being closer to the true tree than the
second.
Distinguishing between convergent evolution and violation of the molecular clock
Date: 2017-09-13 00:00:00

We give a non-technical introduction to convergence-divergence models, a new
modeling approach for phylogenetic data that allows for the usual divergence of
species post speciation but also allows for species to converge, i.e. become
more similar over time. By examining the $3$-taxon case in some detail we
illustrate that phylogeneticists have been "spoiled" in the sense of not having
to think about the structural parameters in their models by virtue of the
strong assumption that evolution is treelike. We show that there are not always
good statistical reasons to prefer the usual class of treelike models over more
general convergence-divergence models. Specifically we show many $3$-taxon
datasets can be equally well explained by supposing violation of the molecular
clock due to change in the rate of evolution along different edges, or by
keeping the assumption of a constant rate of evolution but instead assuming
that evolution is not a purely divergent process. Given the abundance of
evidence that evolution is not strictly treelike, our discussion is an
illustration that as phylogeneticists we often need to think clearly about the
structural form of the models we use.
Pairwise versus multiple network alignment
Authors: Vipin Vijayan, Shawn Gu, Eric Krebs, Lei Meng, Tijana Milenkovic
Date: 2017-09-13 00:00:00

Biological network alignment (NA) aims to identify similar regions between molecular networks of different species. NA can be local or global. Just as the recent trend in the NA field, we also focus on global NA, which can be pairwise (PNA) and multiple (MNA). PNA produces aligned node pairs between two networks. MNA produces aligned node clusters between more than two networks. Recently, the focus has shifted from PNA to MNA, because MNA captures conserved regions between more networks than PNA (and MNA is thus considered to be more insightful), though at higher computational complexity. The issue is that, due to the different outputs of PNA and MNA, a PNA method is only compared to other PNA methods, and an MNA method is only compared to other MNA methods. Comparison of PNA against MNA must be done to evaluate whether MNA's higher complexity is justified by its higher accuracy. We introduce a framework that allows for this. We evaluate eight prominent PNA and MNA methods, on synthetic and real-world biological networks, using topological and functional alignment quality measures. We compare PNA against MNA in both a pairwise (native to PNA) and multiple (native to MNA) manner. PNA is expected to perform better under the pairwise evaluation framework. Indeed this is what we find. MNA is expected to perform better under the multiple evaluation framework. Shockingly, we find this not to always hold; PNA is often better than MNA in this framework, depending on the choice of evaluation test.
Bayesian support for Evolution: detecting phylogenetic signal in a subset of the primate family
Date: 2017-09-14 00:00:00
Tasks: Diversity

The theory of evolution states that the diversity of species can be explained
by descent with modification. Therefore, all living beings are related through
a common ancestor. This evolutionary process must have left traces in our
molecular composition. In this work, we present a randomization procedure in
order to determine if a group of 5 species of the primate family, namely,
macaque, guereza, orangutan, chimpanzee and human, has retained these traces in
its molecules. Firstly, we present the randomization methodology through two
toy examples, which allow to understand its logic. We then carry out a DNA data
analysis to assess if the group of primates contains phylogenetic information
which links them in a joint evolutionary history. This is carried out by
monitoring a Bayesian measure, called marginal likelihood, which we estimate by
using nested sampling. We found that it would be unusual to get the
relationship observed in the data among these primate species if they had not
shared a common ancestor. The results are in total agreement with the theory of
evolution.
Deep Predictive Learning: A Comprehensive Model of Three Visual Streams
Date: 2017-09-14 00:00:00
Tasks: Prediction

How does the neocortex learn and develop the foundations of all our
high-level cognitive abilities? We present a comprehensive framework spanning
biological, computational, and cognitive levels, with a clear theoretical
continuity between levels, providing a coherent answer directly supported by
extensive data at each level. Learning is based on making predictions about
what the senses will report at 100 msec (alpha frequency) intervals, and
adapting synaptic weights to improve prediction accuracy. The pulvinar nucleus
of the thalamus serves as a projection screen upon which predictions are
generated, through deep-layer 6 corticothalamic inputs from multiple brain
areas and levels of abstraction. The sparse driving inputs from layer 5
intrinsic bursting neurons provide the target signal, and the temporal
difference between it and the prediction reverberates throughout the cortex,
driving synaptic changes that approximate error backpropagation, using only
local activation signals in equations derived directly from a detailed
biophysical model. In vision, predictive learning requires a
carefully-organized developmental progression and anatomical organization of
three pathways (What, Where, and What * Where), according to two central
principles: top-down input from compact, high-level, abstract representations
is essential for accurate prediction of low-level sensory inputs; and the
collective, low-level prediction error must be progressively and
opportunistically partitioned to enable extraction of separable factors that
drive the learning of further high-level abstractions. Our model self-organized
systematic invariant object representations of 100 different objects from
simple movies, accounts for a wide range of data, and makes many testable
predictions.
T${}^2$K${}^2$: The Twitter Top-K Keywords Benchmark
Authors: Truică Ciprian-Octavian  UPB, Darmont Jérôme  ERIC
Date: 2017-09-14 00:00:00
Tasks: Benchmarking, Information Retrieval, Retrieval

Information retrieval from textual data focuses on the construction of
vocabularies that contain weighted term tuples. Such vocabularies can then be
exploited by various text analysis algorithms to extract new knowledge, e.g.,
top-k keywords, top-k documents, etc. Top-k keywords are casually used for
various purposes, are often computed on-the-fly, and thus must be efficiently
computed. To compare competing weighting schemes and database implementations,
benchmarking is customary. To the best of our knowledge, no benchmark currently
addresses these problems. Hence, in this paper, we present a top-k keywords
benchmark, T${}^2$K${}^2$, which features a real tweet dataset and queries with
various complexities and selectivities. T${}^2$K${}^2$ helps evaluate weighting
schemes and database implementations in terms of computing performance. To
illustrate T${}^2$K${}^2$'s relevance and genericity, we successfully performed
tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on
different relational (Oracle, PostgreSQL) and document-oriented (MongoDB)
database implementations, on the other hand.
The rules of long DNA-sequences and tetra-groups of oligonucleotides
Authors: Sergey Petoukhov
Date: 2017-09-14 00:00:00

The article represents a new class of hidden symmetries in long sequences of oligonucleotides of single stranded DNA from their representative set. These symmetries are an addition to symmetries described by the second parity rule of Chargaff. These new symmetries and their rules concern collective probabilities of oligonucleotides from special tetra-groups and their subgroups in long DNA-texts including complete sets of chromosomes of human and some model organisms. These rules of tetra-group probabilities are considered as possible candidacies for the role of universal rules of long DNA-sequences. A quantum-informational model of genetic symmetries of these collective probabilities is proposed on the basis of the known quantum-mechanic statement that quantum state of a multicomponent system is defined by the tensor product of quantum states of its subsystems. In this model, nitrogenous bases C, T, G, A of DNA are represented as computational basis states of 2-qubit quantum CTGA-systems. The biological meaning of these new quantum-information symmetries of long DNA texts is associated with the common ability of all living organisms to grow and develop on the basis of incorporation into their body of new and new molecules of nutrients becoming new quantum-mechanic subsystems of the united quantum-mechanic organism. An important role of resonances, photons and photonic crystals in quantum information genetics is noted.
Comparing Advanced Graph-Theoretical Parameters of the Connectomes of the Lobes of the Human Brain
Date: 2017-09-14 00:00:00

Deep, classical graph-theoretical parameters, like the size of the minimum
vertex cover, the chromatic number, or the eigengap of the adjacency matrix of
the graph were studied widely by mathematicians in the last century. Most
researchers today study much simpler parameters of braingraphs or connectomes
which were defined in the last twenty years for enormous networks -- like the
graph of the World Wide Web -- with hundreds of millions of nodes. Since the
connectomes, describing the connections of the human brain, typically contain
several hundred vertices today, one can compute and analyze the much deeper,
harder-to-compute classical graph parameters for these, relatively small graphs
of the brain. This deeper approach has proven to be very successful in the
comparison of the connectomes of the sexes in our earlier works: we have shown
that graph parameters, deeply characterizing the graph connectivity are
significantly better in women's connectomes than in men's. In the present
contribution we compare numerous graph parameters in the three largest lobes
--- frontal, parietal, temporal --- and in both hemispheres of the brain. We
apply the diffusion weighted imaging data of 423 subjects of the NIH-funded
Human Connectome Project, and present some findings, never described before,
including that the right parietal lobe contains significantly more edges, has
higher average degree, density, larger minimum vertex cover and Hoffman bound
than the left parietal lobe. Similar advantages in the deep graph connectivity
properties are hold for the left frontal vs. the right frontal and for the
right temporal vs. the left temporal lobes.
Pulsing corals: A story of scale and mixing
Date: 2017-09-14 00:00:00

Effective methods of fluid transport vary across scale. A commonly used
dimensionless number for quantifying the effective scale of fluid transport is
the Reynolds number, Re, which gives the ratio of inertial to viscous forces.
What may work well for one Re regime may not produce significant flows for
another. These differences in scale have implications for many organisms,
ranging from the mechanics of how organisms move through their fluid
environment to how hearts pump at various stages in development. Some
organisms, such as soft pulsing corals, actively contract their tentacles to
generate mixing currents that enhance photosynthesis. Their unique morphology
and intermediate scale where both viscous and inertial forces are significant
make them a unique model organism for understanding fluid mixing. In this
paper, 3D fluid-structure interaction simulations of a pulsing soft coral are
used to quantify fluid transport and fluid mixing across a wide range of Re.
The results show that net transport is negligible for $Re<10$, and continuous
upward flow is produced for $Re\geq 10$.
Network Controllability in the IFG Relates to Controlled Language Variability and Susceptibility to TMS
Date: 2018-01-30 00:00:00
Tasks: Sentence

In language production, humans are confronted with considerable word
selection demands. Often, we must select a word from among similar, acceptable,
and competing alternative words in order to construct a sentence that conveys
an intended meaning. In recent years, the left inferior frontal gyrus (LIFG)
has been identified as critical to this ability. Despite a recent emphasis on
network approaches to understanding language, how the LIFG interacts with the
brain's complex networks to facilitate controlled language performance remains
unknown. Here, we take a novel approach to understand word selection as a
network control process in the brain. Using an anatomical brain network derived
from high-resolution diffusion spectrum imaging (DSI), we computed network
controllability underlying the site of transcranial magnetic stimulation in the
LIFG between administrations of two word selection tasks. We find that a
statistic that quantifies the LIFG's theoretically predicted control of
difficult-to-reach states explains vulnerability to TMS in language tasks that
vary in response (cognitive control) demands: open-response (word generation)
vs. closed-response (number naming) tasks. Moreover, we find that a statistic
that quantifies the LIFG's theoretically predicted control of communication
across modules in the human connectome explains TMS-induced changes in
open-response language task performance only. These findings establish a link
between network controllability, cognitive function, and TMS effects.
Data-driven Job Search Engine Using Skills and Company Attribute Filters
Authors: Muthyala Rohit, Wood Sam, Jin Yi, Qin Yixing, Gao Hua, Rai Amit
Date: 2017-09-15 00:00:00
Tasks: Attribute

According to a report online, more than 200 million unique users search for
jobs online every month. This incredibly large and fast growing demand has
enticed software giants such as Google and Facebook to enter this space, which
was previously dominated by companies such as LinkedIn, Indeed and
CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine",
"Google For Jobs" while Facebook released "Facebook Jobs" within their
platform. These current job search engines and platforms allow users to search
for jobs based on general narrow filters such as job title, date posted,
experience level, company and salary. However, they have severely limited
filters relating to skill sets such as C++, Python, and Java and company
related attributes such as employee size, revenue, technographics and
micro-industries. These specialized filters can help applicants and companies
connect at a very personalized, relevant and deeper level. In this paper we
present a framework that provides an end-to-end "Data-driven Jobs Search
Engine". In addition, users can also receive potential contacts of recruiters
and senior positions for connection and networking opportunities. The high
level implementation of the framework is described as follows: 1) Collect job
postings data in the United States, 2) Extract meaningful tokens from the
postings data using ETL pipelines, 3) Normalize the data set to link company
names to their specific company websites, 4) Extract and ranking the skill
sets, 5) Link the company names and websites to their respective company level
attributes with the EVERSTRING Company API, 6) Run user-specific search queries
on the database to identify relevant job postings and 7) Rank the job search
results. This framework offers a highly customizable and highly targeted search
experience for end users.