pwc-archive/papers-with-abstracts huggingface dataset:
Aggression heuristics underlie animal dominance hierarchies and provide evidence of group-level social information
Authors: Elizabeth A. Hobson, Dan Mønster, Simon DeDeo
Date: 2018-10-16 00:00:00

Members of a social species need to make appropriate decisions about who, how, and when to interact with others in their group. However, it has been difficult for researchers to detect the inputs to these decisions and, in particular, how much information individuals actually have about their social context. We present a new method that can serve as a social assay to quantify how patterns of aggression depend upon information about the ranks of individuals within social dominance hierarchies. Applied to existing data on aggression in 172 social groups across 85 species in 23 orders, it reveals three main patterns of rank-dependent social dominance: the downward heuristic (aggress uniformly against lower-ranked opponents), close competitors (aggress against opponents ranked slightly below self), and bullying (aggress against opponents ranked much lower than self). The majority of the groups (133 groups, 77%) follow a downward heuristic, but a significant minority (38 groups, 22%) show more complex social dominance patterns (close competitors or bullying) consistent with higher levels of social information use. These patterns are not phylogenetically constrained and different groups within the same species can use different patterns, suggesting that heuristics use may depend on context and the structuring of aggression by social information should not be considered a fixed characteristic of a species. Our approach provides new opportunities to study the use of social information within and across species and the evolution of social complexity and cognition.
A Retrieval Framework and Implementation for Electronic Documents with Similar Layouts
Authors: Chung Hyunji
Date: 2018-10-16 00:00:00
Tasks: Retrieval

As the number of digital documents requiring investigation increases, it has
become more important to identify relevant documents to a given case. There
have been continual demands for finding relevant files in order to overcome
this kind of issues. Regarding finding similar files, there can be a situation
where there is no available metadata such as timestamp, file size, title,
subject, template, author, etc. In this situation, investigators will focus on
searching document files having specific keywords related to a given case.
Although the traditional keyword search with elaborate regular expressions is
useful for digital forensics, there is a possibility that closely related
documents are missing because they have totally different body contents. In
this paper, we introduce a recent actual case on handling large amounts of
document files. This case suggests that similar layout search will be useful
for more efficient digital investigations if it can be utilized appropriately
for supplementing results of the traditional keyword search. Until now,
research involving electronic-document similarity has mainly focused on byte
streams, format structures and body contents. However, there has been little
research on the similarity of visual layouts from the viewpoint of digital
forensics. In order to narrow this gap, this study demonstrates a novel
framework for retrieving electronic document files having similar layouts, and
implements a tool for finding similar Microsoft OOXML files using
user-controlled layout queries based on the framework.
Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward
Authors: Usama Muhammad, Qadir Junaid, Al-Fuqaha Ala
Date: 2018-09-26 00:00:00

Future communications and data networks are expected to be largely cognitive
self-organizing networks (CSON). Such networks will have the essential property
of cognitive self-organization, which can be achieved using machine learning
techniques (e.g., deep learning). Despite the potential of these techniques,
these techniques in their current form are vulnerable to adversarial attacks
that can cause cascaded damages with detrimental consequences for the whole
network. In this paper, we explore the effect of adversarial attacks on CSON.
Our experiments highlight the level of threat that CSON have to deal with in
order to meet the challenges of next-generation networks and point out
promising directions for future work.
Optimal policy design for the sugar tax
Date: 2018-10-16 00:00:00
Tasks: CoLA, Nutrition

Healthy nutrition promotions and regulations have long been regarded as a
tool for increasing social welfare. One of the avenues taken in the past decade
is sugar consumption regulation by introducing a sugar tax. Such a tax
increases the price of extensive sugar containment in products such as soft
drinks. In this article we consider a typical problem of optimal regulatory
policy design, where the task is to determine the sugar tax rate maximizing the
social welfare. We model the problem as a sequential game represented by the
three-level mathematical program. On the upper level, the government decides
upon the tax rate. On the middle level, producers decide on the product
pricing. On the lower level, consumers decide upon their preferences towards
the products. While the general problem is computationally intractable, the
problem with a few product types is polynomially solvable, even for an
arbitrary number of heterogeneous consumers. This paper presents a simple,
intuitive and easily implementable framework for computing optimal sugar tax in
a market with a few products. This resembles the reality as the soft drinks,
for instance, are typically categorized in either regular or no-sugar drinks,
e.g. Coca-Cola and Coca-Cola Zero. We illustrate the algorithm using an example
based on the real data and draw conclusions for a specific local market.
Statistical Estimation of Malware Detection Metrics in the Absence of Ground Truth
Authors: Du Pang, Sun Zheyuan, Chen Huashan, Cho Jin-Hee, Xu Shouhuai
Date: 2018-09-24 00:00:00
Tasks: Malware Detection

The accurate measurement of security metrics is a critical research problem
because an improper or inaccurate measurement process can ruin the usefulness
of the metrics, no matter how well they are defined. This is a highly
challenging problem particularly when the ground truth is unknown or noisy. In
contrast to the well perceived importance of defining security metrics, the
measurement of security metrics has been little understood in the literature.
In this paper, we measure five malware detection metrics in the {\em absence}
of ground truth, which is a realistic setting that imposes many technical
challenges. The ultimate goal is to develop principled, automated methods for
measuring these metrics at the maximum accuracy possible. The problem naturally
calls for investigations into statistical estimators by casting the measurement
problem as a {\em statistical estimation} problem. We propose statistical
estimators for these five malware detection metrics. By investigating the
statistical properties of these estimators, we are able to characterize when
the estimators are accurate, and what adjustments can be made to improve them
under what circumstances. We use synthetic data with known ground truth to
validate these statistical estimators. Then, we employ these estimators to
measure five metrics with respect to a large dataset collected from VirusTotal.
We believe our study touches upon a vital problem that has not been paid due
attention and will inspire many future investigations.
Active unidirectional network flow generates a packet molecular transport in cells
Authors: Matteo Dora, David Holcman
Date: 2018-10-16 00:00:00

Internet, social media, neuronal or blood vessel are organized in complex networks. These networks are characterized by several quantities such as the underlying graph connectivity (topology), how they grow in time, scaling laws or by the mean time a random search can cover a network and also by shortest path between two nodes. We present here a novel type of network property based on a unidirectional transport mechanism occurring in the Endoplasmic Reticulum network found in the cell cytoplasm. This mechanism is an active-waiting transportation, where molecules have to wait a random time before being transported from one node to the next one. We find that the consequence of this unusual network transportation is that molecules travel together by recurrent packets, which quite a large deviation behavior compared to classical propagation in graphs. To conclude, this form of transportation is associated with an efficient and robust molecular redistribution inside cells.
Advancing Standards-Free Methods for the Identification of Small Molecules in Complex Samples
Date: 2018-10-17 00:00:00
Tasks: Attribute

The current gold standard for unambiguous identification in metabolomics
analysis is based on comparing two or more orthogonal properties from the
analysis of authentic, pure reference materials (standards) to experimental
data acquired in the same laboratory with the same analytical methods. This
represents a significant limitation for comprehensive chemical identification
of small molecules in complex samples since this process is time-consuming and
costly, and the majority of molecules are not yet represented by standards,
leading to a need for standards-free identification. To address this need, we
are advancing chemical property calculations and developing multi-attribute
scoring and matching algorithms to utilize data from multiple analytical
platforms through the utilization and creation of the in silico Chemical
Library Engine (ISiCLE) and the Multi-Attribute Matching Engine (MAME). Here,
we describe our results in a blinded analysis of synthetic chemical mixtures as
part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis
Collaborative Trial (ENTACT). The blinded false negative rate (FNR), false
discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For
high confidence identifications, the FDR was 35%. After unblinding of the
sample compositions, we improved our approach by optimizing the scoring
parameters used to increase confidence. The final FNR, FDR, and accuracy were
67%, 53%, and 96%, respectively. For high confidence identifications, the FDR
was 10%. This study demonstrates that standards-free small molecule
identification and multi-attribute matching methods can significantly reduce
reliance on standards.
Vanna-Volga Method for Normal Volatilities
Authors: Volodymyr Perederiy
Date: 2018-10-17 00:00:00

Vanna-Volga is a popular method for the interpolation/extrapolation of volatility smiles. The technique is widely used in the FX markets context, due to its ability to consistently construct the entire Lognormal smile using only three Lognormal market quotes. However, the derivation of the Vanna-Volga method itself is free of distributional assumptions. With this is mind, it is surprising there have been no attempts to apply the method to Normal volatilities (the current standard for interest rate markets). We show how the method can be modified to build Normal volatility smiles. As it turns out, only minor modifications are required compared to the Lognormal case. Moreover, as the inversion of Normal volatilities from option prices is easier in the Normal case, the smile construction can occur at a machine-precision level using analytical formulae, making the approximations via Taylor-series unnecessary. Apart from being based on practical and intuitive hedging arguments, the Vanna-Volga has further important advantages. In comparison to the Normal SABR model, the Vanna-Volga can easily fit both classical convex and atypical concave smiles (frowns). Concave smile patterns are sometimes observed around ATM strikes in the interest rate markets, particularly in the situations of anticipated jumps (with an unclear outcome) in interest rates. Besides, concavity is often observed towards the lower/left end of the Normal volatility smiles of interest rates. At least in these situations, the Vanna-Volga can be expected to interpolate/extrapolate better than SABR.
Domain-Informed Spline Interpolation
Date: 2019-06-27 00:00:00
Tasks: Anatomy

Standard interpolation techniques are implicitly based on the assumption that
the signal lies on a single homogeneous domain. In contrast, many naturally
occurring signals lie on an inhomogeneous domain, such as brain activity
associated to different brain tissue. We propose an interpolation method that
instead exploits prior information about domain inhomogeneity, characterized by
different, potentially overlapping, subdomains. As proof of concept, the focus
is put on extending conventional shift-invariant B-spline interpolation. Given
a known inhomogeneous domain, B-spline interpolation of a given order is
extended to a domain-informed, shift-variant interpolation. This is done by
constructing a domain-informed generating basis that satisfies stability
properties. We illustrate example constructions of domain-informed generating
basis, and show their property in increasing the coherence between the
generating basis and the given inhomogeneous domain. By advantageously
exploiting domain knowledge, we demonstrate the benefit of domain-informed
interpolation over standard B-spline interpolation through Monte Carlo
simulations across a range of B-spline orders. We also demonstrate the
feasibility of domain-informed interpolation in a neuroimaging application
where the domain information is available by a complementary image contrast.
The results show the benefit of incorporating domain knowledge so that an
interpolant consistent to the anatomy of the brain is obtained.
Modeling and Analysis of Wildfire Detection using Wireless Sensor Network with Poisson Deployment
Date: 2018-10-16 00:00:00
Tasks: Fire Detection

In many forest fire incidences, late detection of the fire has lead to severe
damages to the forest and human property requiring more resources to gain
control over the fire. An early warning and immediate response system can be a
promising solution to avoid such massive losses. This paper considers a network
consisting of multiple wireless sensors randomly deployed throughout the forest
for early prompt detection of fire. We present a framework to model fire
propagation in a forest and analyze the performance of considered wireless
sensor network in terms of fire detection probability. In particular, this
paper models sensor deployment as a Poisson point process (PPP) and models the
forest fire as a dynamic event which expands with time. We also present various
insights to the system including required sensor density and impact of wind
velocity on the detection performance. We show that larger wind velocity may
not necessarily imply bad sensing performance or the requirement of a denser
deployment.
Optimizing Beams and Bits: A Novel Approach for Massive MIMO Base-Station Design
Date: 2019-02-27 00:00:00

We consider the problem of jointly optimizing ADC bit resolution and analog
beamforming over a frequency-selective massive MIMO uplink. We build upon a
popular model to incorporate the impact of low bit resolution ADCs, that
hitherto has mostly been employed over flat-fading systems. We adopt weighted
sum rate (WSR) as our objective and show that WSR maximization under finite
buffer limits and important practical constraints on choices of beams and ADC
bit resolutions can equivalently be posed as constrained submodular set
function maximization. This enables us to design a constant-factor
approximation algorithm. Upon incorporating further enhancements we obtain an
efficient algorithm that significantly outperforms state-of-the-art ones.
Evolutionary multiplayer games on graphs with edge diversity
Date: 2018-10-17 00:00:00
Tasks: Diversity

Evolutionary game dynamics in structured populations has been extensively
explored in past decades. However, most previous studies assume that payoffs of
individuals are fully determined by the strategic behaviors of interacting
parties and social ties between them only serve as the indicator of the
existence of interactions. This assumption neglects important information
carried by inter-personal social ties such as genetic similarity, geographic
proximity, and social closeness, which may crucially affect the outcome of
interactions. To model these situations, we present a framework of evolutionary
multiplayer games on graphs with edge diversity, where different types of edges
describe diverse social ties. Strategic behaviors together with social ties
determine the resulting payoffs of interactants. Under weak selection, we
provide a general formula to predict the success of one behavior over the
other. We apply this formula to various examples which cannot be dealt with
using previous models, including the division of labor and relationship- or
edge-dependent games. We find that labor division facilitates collective
cooperation by decomposing a many-player game into several games of smaller
sizes. The evolutionary process based on relationship-dependent games can be
approximated by interactions under a transformed and unified game. Our work
stresses the importance of social ties and provides effective methods to reduce
the calculating complexity in analyzing the evolution of realistic systems.
Mode Division Multiplexing (MDM) Weight Bank Design for Use in Photonic Neural Networks
Date: 2018-10-17 00:00:00

Neural networks provide a powerful tool for applications from classification
and regression to general purpose alternative computing. Photonics have the
potential to provide enormous speed benefits over electronic and software
networks, allowing such networks to be used in real-time applications at radio
frequencies. Mode division multiplexing (MDM) is one method to increase the
total information capacity of a single on-chip waveguide and, by extension, the
information density of the photonic neural network (PNN). This Independent Work
consists of three experimental designs ready for fabrication, each of which
investigates the process of expanding current PNN technology to include MDM.
Experiment 1 determines the optimal waveguide geometry to couple optical power
into different spacial modes within a single waveguide. Experiment 2 combines
MDM and previous wavelength division multiplexing (WDM) technology into a
single weight bank for use as the dendrite of a photonic neuron. Finally,
Experiment 3 puts two full neurons in a folded bus, or "hairpin," network
topology to provide a platform for training calibration schemes that can be
applied to larger networks.
Implementation of Digital Image Processing and Computation Technology on Measurement and Testing of Various Yarn Parameters
Date: 2018-09-28 00:00:00

Along with the development of science and technology, the application of
various computational processes in the field of science engineering is
something that is being developed, even today. Currently, many studies have
applied image analysis technology to the field of textile evaluation and
identification, one of which is yarn. Computer image analysis techniques can be
used to identify textile products, especially textile products in the form of
yarn. This chapter has reviewed various methods and application of image
processing technology in the field of identification and evaluation of various
thread parameters, together with a brief review of historical developments, as
well as the need for the application of these methods.
  Keywords: yarn, digital image processing, evaluation and identification,
textiles.
Implementation of Digital Image Processing and Computation Technology on Measurement and Testing of Non Woven Fabric Parameters
Date: 2018-09-28 00:00:00

One of the big challenges of the industry today is how to produce quality
products, one of which is in the non-woven industry. The improvement of the
evaluation and quality control processes of non woven production has been
widely developed to support the improvement of the quality of production. The
use of information and computational technology has now been widely applied to
the quality control process of textile material production, one of which is the
use of image processing technology in the evaluation process of non-woven
materials. This paper will explain various methods of applying image processing
technology in the field of evaluation and quality control of textile
production.
  Keywords: textile fiber, image processing, textile evaluation.
Implementation of Digital Image Processing and Computation Technology on Measurement and Testing of Various Woven Fabric Parameters
Date: 2018-09-28 00:00:00

Quality is one of the important things to be maintained in a weaving
industry. Along with the times, technological developments in the field of
image processing and computing have changed the old method of visual evaluation
of woven fabric to be better. This chapter will explain the implementation of
image and computational processing techniques on woven fabric.
  Keywords: woven fabric, digital image processing.
Forecasting financial crashes with quantum computing
Date: 2019-06-22 00:00:00

A key problem in financial mathematics is the forecasting of financial
crashes: if we perturb asset prices, will financial institutions fail on a
massive scale? This was recently shown to be a computationally intractable
(NP-hard) problem. Financial crashes are inherently difficult to predict, even
for a regulator which has complete information about the financial system. In
this paper we show how this problem can be handled by quantum annealers. More
specifically, we map the equilibrium condition of a toy-model financial network
to the ground-state problem of a spin-1/2 quantum Hamiltonian with 2-body
interactions, i.e., a quadratic unconstrained binary optimization (QUBO)
problem. The equilibrium market values of institutions after a sudden shock to
the network can then be calculated via adiabatic quantum computation and, more
generically, by quantum annealers. Our procedure could be implemented on
near-term quantum processors, thus providing a potentially more efficient way
to assess financial equilibrium and predict financial crashes.
Implied and Realized Volatility: A Study of the Ratio Distribution
Date: 2018-10-17 00:00:00

We analyze correlations between squared volatility indices, VIX and VXO, and
realized variances -- the known one, for the current month, and the predicted
one, for the following month. We show that the ratio of the two is best fitted
by a Beta Prime distribution, whose shape parameters depend strongly on which
of the two months is used.
Study of non-linear viscoelastic behavior of the human red blood cell
Date: 2018-10-17 00:00:00
Tasks: Time Series, Time Series Analysis

The non-linear behavior of human erythrocytes subjected to shear stress was
analyzed using data series from the Erythrocyte Rheometer and a theoretical
model was developed. Linear behavior was eliminated by means of a slot filter
and a sixth order Savisky-Golay filter was applied to the resulting time series
that allows the elimination of any possible white noise in the data. A fast
Fourier transform was performed on the processed data, which resulted in a
series of frequency dominant peaks. Results suggest the presence of a
non-linear quadratic term in the Kelvin-Voigt phenomenological model. The
correlation dimension studied through recurrence quantification analysis gave
C2=2.58. Results suggest that the underlying dynamics is the same in each RBC
sample corresponding to healthy donors.