pwc-archive/papers-with-abstracts huggingface dataset:
Towards Theoretical Understanding of Weak Supervision for Information Retrieval
Authors: Zamani Hamed, Croft W. Bruce
Date: 2018-06-13 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Retrieval

Neural network approaches have recently shown to be effective in several
information retrieval (IR) tasks. However, neural approaches often require
large volumes of training data to perform effectively, which is not always
available. To mitigate the shortage of labeled data, training neural IR models
with weak supervision has been recently proposed and received considerable
attention in the literature. In weak supervision, an existing model
automatically generates labels for a large set of unlabeled data, and a machine
learning model is further trained on the generated "weak" data. Surprisingly,
it has been shown in prior art that the trained neural model can outperform the
weak labeler by a significant margin. Although these obtained improvements have
been intuitively justified in previous work, the literature still lacks
theoretical justification for the observed empirical findings. In this position
paper, we propose to theoretically study weak supervision, in particular for IR
tasks, e.g., learning to rank. We briefly review a set of our recent
theoretical findings that shed light on learning from weakly supervised data,
and provide guidelines on how train learning to rank models with weak
supervision.
Novel Light Weight Compressed Data Aggregation Using Sparse Measurements for IoT Networks
Date: 2018-06-13 00:00:00
Tasks: compressed sensing

Optimal data aggregation aimed at maximizing IoT network lifetime by
minimizing constrained on-board resource utilization continues to be a
challenging task. The existing data aggregation methods have proven that
compressed sensing is promising for data aggregation. However, they compromise
either on energy efficiency or recovery fidelity and require complex on-node
computations. In this paper, we propose a novel Light Weight Compressed Data
Aggregation (LWCDA) algorithm that randomly divides the entire network into
non-overlapping clusters for data aggregation. The random non-overlapping
clustering offers two important advantages: 1) energy efficiency, as each node
has to send its measurement only to its cluster head, 2) highly sparse
measurement matrix, which leads to a practically implementable framework with
low complexity. We analyze the properties of our measurement matrix using
restricted isometry property, the associated coherence and phase transition.
Through extensive simulations on practical data, we show that the measurement
matrix can reconstruct data with high fidelity. Further, we demonstrate that
the LWCDA algorithm reduces transmission cost significantly against baseline
approaches, implying thereby the enhancement of the network lifetime.
Species tree inference from genomic sequences using the log-det distance
Date: 2018-06-13 00:00:00

The log-det distance between two aligned DNA sequences was introduced as a
tool for statistically consistent inference of a gene tree under simple
non-mixture models of sequence evolution. Here we prove that the log-det
distance, coupled with a distance-based tree construction method, also permits
consistent inference of species trees under mixture models appropriate to
aligned genomic-scale sequences data. Data may include sites from many genetic
loci, which evolved on different gene trees due to incomplete lineage sorting
on an ultrametric species tree, with different time-reversible substitution
processes. The simplicity and speed of distance-based inference suggests
log-det based methods should serve as benchmarks for judging more elaborate and
computationally-intensive species trees inference methods.
Explainable Agreement through Simulation for Tasks with Subjective Labels
Authors: Foley John
Date: 2018-06-13 00:00:00
Tasks: Information Retrieval, Retrieval, User Simulation

The field of information retrieval often works with limited and noisy data in
an attempt to classify documents into subjective categories, e.g., relevance,
sentiment and controversy. We typically quantify a notion of agreement to
understand the difficulty of the labeling task, but when we present final
results, we do so using measures that are unaware of agreement or the inherent
subjectivity of the task. We propose using user simulation to understand the
effect size of this noisy agreement data. By simulating truth and predictions,
we can understand the maximum scores a dataset can support: for if a classifier
is doing better than a reasonable model of a human, we cannot conclude that it
is actually better, but that it may be learning noise present in the dataset.
We present a brief case study on controversy detection that concludes that a
commonly-used dataset has been exhausted: in order to advance the
state-of-the-art, more data must be gathered at the current level of label
agreement in order to distinguish between techniques with confidence.
An inverse analysis of cohesive zone model parameter values for human fibrous cap mode I tearing
Date: 2018-06-13 00:00:00

Atherosclerotic plaque failure results from various pathophysiological
events, with the existence of fibrous cap mode I tearing in the arterial wall,
having the potential to block the aortic lumen and correspondingly induce
serious clinical conditions.The aim of this study was to quantify the
interfacial strength and critical energy release rate of the fibrous tissue
across the thickness. in this study, an inverse analysis method via finite
element modeling and simulation approach was presented. A cohesive zone model
(CZM) was applied to simulate the tearing of the fibrous cap tissue under
uniaxial tensile tests along the circumferential direction. A fiber-reinforced
hyperelastic model (Holzapfel-Gasser-Ogden) was implemented for characterizing
the mechanical properties of bulk material. With the material parameter values
of HGO model from inverse analysis process as the input for the bulk material,
the interfacial strength and critical energy release rate along the tearing
path or failure zones are obtained through the same method as material
identification process of HGO model. Results of this study demonstrate the
fibrous cap tissue tearing failure processes.
Socioeconomic driving forces of scientific research
Date: 2018-06-13 00:00:00

Why do nations produce scientific research? This is a fundamental problem in
the field of social studies of science. The paper confronts this question here
by showing vital determinants of science to explain the sources of social power
and wealth creation by nations. Firstly, this study suggests a new general
definition of science and scientific research that synthetizes previous
concepts and endeavors to extend them: Science discovers the root causes of
phenomena to explain and predict them in a context of adaptation of life to new
economic and social bases, whereas scientific research is a systematic process,
applying methods of scientific inquiry, to solve consequential problems, to
satisfy human wants, to take advantage of important opportunities and/or to
cope with environmental threats. In particular, science and scientific research
are driven by an organized social effort that inevitably reflect the concerns
and interests of nations to achieve advances and discoveries that are spread to
the rest of humankind. This study reveals that scientific research is produced
for social and economic interests of nations (power, wealth creation,
technological superiority, etc.), rather than philosophical inquiries. A main
implication of this study is that the immense growth of science in modern
society is not only due to activity of scientists but rather to general social
efforts of nations to take advantage of important opportunities and/or to cope
with environmental threats, such as war. Empirical evidence endeavors to
support the sources of scientific research for nations, described here.
Finally, relationships between R&D investment and productivity, and research
policy implications are discussed.
LASSO-Driven Inference in Time and Space
Date: 2020-05-15 00:00:00
Tasks: Variable Selection

We consider the estimation and inference in a system of high-dimensional
regression equations allowing for temporal and cross-sectional dependency in
covariates and error processes, covering rather general forms of weak temporal
dependence. A sequence of regressions with many regressors using LASSO (Least
Absolute Shrinkage and Selection Operator) is applied for variable selection
purpose, and an overall penalty level is carefully chosen by a block multiplier
bootstrap procedure to account for multiplicity of the equations and
dependencies in the data. Correspondingly, oracle properties with a jointly
selected tuning parameter are derived. We further provide high-quality
de-biased simultaneous inference on the many target parameters of the system.
We provide bootstrap consistency results of the test procedure, which are based
on a general Bahadur representation for the $Z$-estimators with dependent data.
Simulations demonstrate good performance of the proposed inference procedure.
Finally, we apply the method to quantify spillover effects of textual sentiment
indices in a financial market and to test the connectedness among sectors.
Order-book modelling and market making strategies
Date: 2018-06-13 00:00:00
Tasks: Algorithmic Trading

Market making is one of the most important aspects of algorithmic trading,
and it has been studied quite extensively from a theoretical point of view. The
practical implementation of so-called "optimal strategies" however suffers from
the failure of most order book models to faithfully reproduce the behaviour of
real market participants.
  This paper is twofold. First, some important statistical properties of order
driven markets are identified, advocating against the use of purely Markovian
order book models. Then, market making strategies are designed and their
performances are compared, based on simulation as well as backtesting. We find
that incorporating some simple non-Markovian features in the limit order book
greatly improves the performances of market making strategies in a realistic
context.
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials
Authors: Max Tabord-Meehan
Date: 2018-06-13 00:00:00

This paper proposes an adaptive randomization procedure for two-stage randomized controlled trials. The method uses data from a first-wave experiment in order to determine how to stratify in a second wave of the experiment, where the objective is to minimize the variance of an estimator for the average treatment effect (ATE). We consider selection from a class of stratified randomization procedures which we call stratification trees: these are procedures whose strata can be represented as decision trees, with differing treatment assignment probabilities across strata. By using the first wave to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assignment probabilities within these strata. Our main result shows that using this randomization procedure with an appropriate estimator results in an asymptotic variance which is minimal in the class of stratification trees. Moreover, the results we present are able to accommodate a large class of assignment mechanisms within strata, including stratified block randomization. In a simulation study, we find that our method, paired with an appropriate cross-validation procedure ,can improve on ad-hoc choices of stratification. We conclude by applying our method to the study in Karlan and Wood (2017), where we estimate stratification trees using the first wave of their experiment.
Dual-Polarization OFDM-OQAM Wireless Communication System
Date: 2018-06-30 00:00:00

In this paper we describe the overall idea and results of a recently proposed
radio access technique based on filter bank multicarrier (FBMC) communication
system using two orthogonal polarizations: dual-polarization FBMC (DP-FBMC).
Using this system we can alleviate the intrinsic interference problem in FBMC
systems. This enables use of all the multicarrier techniques used in
cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM) systems for
channel equalization, multiple-input/multiple-output (MIMO) processing, etc.,
without using the extra processing required for conventional FBMC. DP-FBMC also
provides other interesting advantages over CP-OFDM and FBMC such as more
robustness in multipath fading channels, and more robustness to receiver
carrier frequency offset (CFO) and timing offset (TO). For DP-FBMC we propose
three different structures based on different multiplexing techniques in time,
frequency, and polarization. We will show that one of these structures has
exactly the same system complexity and equipment as conventional FBMC. In our
simulation results DP-FBMC has better bit error ratio (BER) performance in
dispersive channels. Based on these results, DP-FBMC has potential as a
promising candidate for future wireless communication systems.
The importance of the whole: topological data analysis for the network neuroscientist
Date: 2018-06-13 00:00:00
Tasks: Topological Data Analysis

The application of network techniques to the analysis of neural data has
greatly improved our ability to quantify and describe these rich interacting
systems. Among many important contributions, networks have proven useful in
identifying sets of node pairs that are densely connected and that collectively
support brain function. Yet the restriction to pairwise interactions prevents
us from realizing intrinsic topological features such as cavities within the
interconnection structure that may be just as crucial for proper function. To
detect and quantify these topological features we must turn to methods from
algebraic topology that encode data as a simplicial complex built of sets of
interacting nodes called simplices. On this substrate, we can then use the
relations between simplices and higher-order connectivity to expose cavities
within the complex, thereby summarizing its topological nature. Here we provide
an introduction to persistent homology, a fundamental method from applied
topology that builds a global descriptor of system structure by chronicling the
evolution of cavities as we move through a combinatorial object such as a
weighted network. We detail the underlying mathematics and perform
demonstrative calculations on the mouse structural connectome, electrical and
chemical synapses in \textit{C. elegans}, and genomic interaction data. Finally
we suggest avenues for future work and highlight new advances in mathematics
that appear ready for use in revealing the architecture and function of neural
systems.
Impact of atmospheric impairments on mmWave based outdoor communication
Date: 2018-06-13 00:00:00

Substantial growth of demand for mobile data and rapidly increasing spread of
personal communication devices such as smart phones and tablets is the major
challenge faced by the mobile service providers in recent years. The allotted
spectrum for the currently operating mobile communication systems have been
saturated in the last few years by such a considerable rate that the future
fifth generation network would not be able to operate without applying new
frequencies. In accordance with the concept of 5G, millimeter wave frequency
band will also be used along with the currently used frequency bands. In this
particular spectrum however, a new and most significant attenuation factor due
to precipitation and atmospheric absorption is introduced. In this paper the
effects of precipitation, fog and atmospheric absorption on millimeter wave
propagation are investigated using MATLAB.
Analysis of Search Stratagem Utilisation
Authors: Kacem Ameni, Mayr Philipp
Date: 2018-06-13 00:00:00
Tasks: Retrieval

In Interactive IR, researchers consider the user behaviour towards systems
and search tasks in order to adapt search results and to improve the search
experience of users. Analysing the users' past interactions with the system is
one typical approach. In this paper, we analyse the user behaviour in retrieval
sessions towards Marcia Bates' search stratagems such as Footnote Chasing,
Citation Searching, Keyword Searching, Author Searching and Journal Run in a
real-life academic search engine. In fact, search stratagems represent
high-level search behaviour as the users go beyond simple execution of queries
and investigate more of the system functionalities. We performed analyses of
these five search stratagems using two datasets extracted from the social
sciences search engine sowiport. A specific focus was the detection of the
search phase and frequency of the usage of these stratagems. In addition, we
explored the impact of these stratagems on the whole search process
performance. We addressed mainly the usage patterns' observation of the
stratagems, their impact on the conduct of retrieval sessions and explore
whether they are used similarly in both datasets. From the observation and
metrics proposed, we can conclude that the utilisation of search stratagems in
real retrieval sessions leads to an improvement of the precision in terms of
positive interactions. However, the difference is that Footnote Chasing,
Citation Searching and Journal Run appear mostly at the end of a session while
Keyword and Author Searching appear typically at the beginning. Thus, we can
conclude from the log analysis that the improvement of search functionalities
including personalisation and/or recommendation could be achieved by
considering references, citations, and journals in the ranking process.
How much income inequality is fair? Nash bargaining solution and its connection to entropy
Date: 2018-06-13 00:00:00
Tasks: Fairness, Open-Ended Question Answering, Philosophy

The question about fair income inequality has been an important open question
in economics and in political philosophy for over two centuries with only
qualitative answers such as the ones suggested by Rawls, Nozick, and Dworkin.
We provided a quantitative answer recently, for an ideal free-market society,
by developing a game-theoretic framework that proved that the ideal inequality
is a lognormal distribution of income at equilibrium. In this paper, we develop
another approach, using the Nash Bargaining Solution (NBS) framework, which
also leads to the same conclusion. Even though the conclusion is the same, the
new approach, however, reveals the true nature of NBS, which has been of
considerable interest for several decades. Economists have wondered about the
economic meaning or purpose of the NBS. While some have alluded to its fairness
property, we show more conclusively that it is all about fairness. Since the
essence of entropy is also fairness, we see an interesting connection between
the Nash product and entropy for a large population of rational economic
agents.
Generalized framework for applying the Kelly criterion to stock markets
Date: 2018-06-13 00:00:00

We develop a general framework for applying the Kelly criterion to stock
markets. By supplying an arbitrary probability distribution modeling the future
price movement of a set of stocks, the Kelly fraction for investing each stock
can be calculated by inverting a matrix involving only first and second
moments. The framework works for one or a portfolio of stocks and the Kelly
fractions can be efficiently calculated. For a simple model of geometric
Brownian motion of a single stock we show that our calculated Kelly fraction
agrees with existing results. We demonstrate that the Kelly fractions can be
calculated easily for other types of probabilities such as the Gaussian
distribution and correlated multivariate assets.
Multi-View Networks for Denoising of Arbitrary Numbers of Channels
Date: 2018-07-23 00:00:00
Tasks: Denoising

We propose a set of denoising neural networks capable of operating on an
arbitrary number of channels at runtime, irrespective of how many channels they
were trained on. We coin the proposed models multi-view networks since they
operate using multiple views of the same data. We explore two such
architectures and show how they outperform traditional denoising models in
multi-channel scenarios. Additionally, we demonstrate how multi-view networks
can leverage information provided by additional recordings to make better
predictions, and how they are able to generalize to a number of recordings not
seen in training.
RPM-Drive: A robust, safe, and reversible gene drive system that remains functional after 200+ generations
Date: 2018-07-31 00:00:00

Despite the advent of several novel, synthetic gene drive mechanisms and
their potential to one-day control a number of devastating diseases, among
other applications, practical use of these systems remains contentious and
risky. In particular, there is little in the way of empirical evidence of the
long-term robustness of these synthetic systems against mutational breakdown.
Rather, most existing systems are either known or predicted to be susceptible
to rapid inactivation, though methodological designs continue to be refined.
Here we evaluate a currently existing synthetic, underdominance-based gene
drive system 200+ generations after it was first established in a laboratory
colony of Drosophila melanogaster. Not only do we find that the system is still
functioning as designed, we also show evidence that disruptions to the genetic
construct are highly likely to be removed by natural selection, contributing to
the system's robust, long-term stability. This stability appears to be a result
of a fundamental relationship between ribosomal proteins (a novel target of the
system) and natural cellular defenses that protect against cancer development.
As far as we are aware, this is the longest continually functioning synthetic
gene drive system thus verified, making it highly appropriate for additional
research into its eventual suitability for field trials. Due to inherent
properties of this gene drive, it is also likely to be adaptable for use in
many different species. The insect lines established and used to test this
system have been deposited at a Drosophila stock center, and are available to
labs for further, independent testing.
Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites across Demographic and Food Variables
Date: 2018-06-14 00:00:00
Tasks: Sensitivity

This paper describes a study to test the accuracy of a method that tracks
wrist motion during eating to detect and count bites. The purpose was to assess
its accuracy across demographic (age, gender, ethnicity) and bite (utensil,
container, hand used, food type) variables. Data were collected in a cafeteria
under normal eating conditions. A total of 271 participants ate a single meal
while wearing a watch-like device to track their wrist motion. Video was
simultaneously recorded of each participant and subsequently reviewed to
determine the ground truth times of bites. Bite times were operationally
defined as the moment when food or beverage was placed into the mouth. Food and
beverage choices were not scripted or restricted. Participants were seated in
groups of 2-4 and were encouraged to eat naturally. A total of 24,088 bites of
374 different food and beverage items were consumed. Overall the method for
automatically detecting bites had a sensitivity of 75% with a positive
predictive value of 89%. A range of 62-86% sensitivity was found across
demographic variables, with slower eating rates trending towards higher
sensitivity. Variations in sensitivity due to food type showed a modest
correlation with the total wrist motion during the bite, possibly due to an
increase in head-towards-plate motion and decrease in hand-towards-mouth motion
for some food types. Overall, the findings provide the largest evidence to date
that the method produces a reliable automated measure of intake during
unrestricted eating.