pwc-archive/papers-with-abstracts huggingface dataset:
True Contextuality in a Psychophysical Experiment
Date: 2019-02-22 00:00:00

Recent crowdsourcing experiments have shown that true contextuality of the
kind found in quantum mechanics can also be present in human behavior. In these
experiments simple human choices were aggregated over large numbers of
respondents, with each respondent dealing with a single context (set of
questions asked). In this paper we present experimental evidence of
contextuality in individual human behavior, in a psychophysical experiment with
repeated presentations of visual stimuli in randomly varying contexts
(arrangements of stimuli). The analysis is based on the
Contextuality-by-Default (CbD) theory whose relevant aspects are reviewed in
the paper. CbD allows one to detect contextuality in the presence of direct
influences, i.e., when responses to the same stimuli have different
distributions in different contexts. The experiment presented is also the first
one in which contextuality is demonstrated for responses that are not
dichotomous, with five options to choose among. CbD requires that random
variables representing such responses be dichotomized before they are subjected
to contextuality analysis. A theorem says that a system consisting of all
possible dichotomizations of responses has to be contextual if these responses
violate a certain condition, called nominal dominance. In our experiment
nominal dominance was violated in all data sets, with very high statistical
reliability established by bootstrapping.
The Extended Evolutionary Synthesis Facilitates Evolutionary Models of Culture Change
Date: 2019-07-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The Extended Evolutionary Synthesis (EES) is beginning to fulfill the whole
promise of Darwinian insight through its extension of evolutionary
understanding from the biological domain to include cultural information
evolution. Several decades of important foundation-laying work took a social
Darwinist approach and exhibited and ecologically-deterministic elements. This
is not the case with more recent developments to the evolutionary study of
culture, which emphasize non-Darwinian processes such as self-organization,
potentiality, and epigenetic change.
Expected Density of Cooperative Bacteria in a 2D Quorum Sensing Based Molecular Communication System
Authors: Yuting Fang, Adam Noel, Andrew W. Eckford, Nan Yang
Date: 2018-12-01 00:00:00

The exchange of small molecular signals within microbial populations is generally referred to as quorum sensing (QS). QS is ubiquitous in nature and enables microorganisms to respond to fluctuations in living environments by working together. In this study, a QS-based molecular communication system within a microbial population in a two-dimensional (2D) environment is analytically modeled. Microorganisms are randomly distributed on a 2D circle where each one releases molecules at random times. The number of molecules observed at each randomly-distributed bacterium is first derived by characterizing the diffusion and degradation of signaling molecules within the population. Using the derived result and some approximation, the expected density of cooperative bacteria is derived. Our model captures the basic features of QS. The analytical results for noisy signal propagation agree with simulation results where the Brownian motion of molecules is simulated by a particle-based method. Therefore, we anticipate that our model can be used to predict the density of cooperative bacteria in a variety of QS-coordinated activities, e.g., biofilm formation and antibiotic resistance.
Interpreting Encoding and Decoding Models
Date: 2019-04-26 00:00:00

Encoding and decoding models are widely used in systems, cognitive, and
computational neuroscience to make sense of brain-activity data. However, the
interpretation of their results requires care. Decoding models can help reveal
whether particular information is present in a brain region in a format the
decoder can exploit. Encoding models make comprehensive predictions about
representational spaces. In the context of sensory systems, encoding models
enable us to test and compare brain-computational models, and thus directly
constrain computational theory. Encoding and decoding models typically include
fitted linear-model components. Sometimes the weights of the fitted linear
combinations are interpreted as reflecting, in an encoding model, the
contribution of different sensory features to the representation or, in a
decoding model, the contribution of different measured brain responses to a
decoded feature. Such interpretations can be problematic when the predictor
variables or their noise components are correlated and when priors (or
penalties) are used to regularize the fit. Encoding and decoding models are
evaluated in terms of their generalization performance. The correct
interpretation depends on the level of generalization a model achieves (e.g. to
new response measurements for the same stimuli, to new stimuli from the same
population, or to stimuli from a different population). Significant decoding or
encoding performance of a single model (at whatever level of generality) does
not provide strong constraints for theory. Many models must be tested and
inferentially compared for analyses to drive theoretical progress.
Building Trust Takes Time: Limits to Arbitrage for Blockchain-Based Assets
Authors: Nikolaus Hautsch, Christoph Scheuch, Stefan Voigt
Date: 2018-12-03 00:00:00
Tasks: Friction

A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk.
A Static Distributed-parameter Circuit Model Explains Electrical Stimulation on the Neuromuscular System
Date: 2019-08-21 00:00:00

Finite Element Modeling (FEM) has been widely used to model the electric
field distribution, to study the interaction between stimulation electrodes and
neural tissue. However, due to the insufficient computational capability to
represent neural tissue down to an atom-level, the existing FEM fails to model
the real electric field that is perpendicular to neuron membrane to initiate an
action potential. Thus, to reveal the real electrode-tissue interactions, we
developed a circuit to model transmembrane voltage waveforms. Here, we show a
distributed-parameter circuit model to systematically study how
electrode-tissue interaction is affected by electrode position, input current
waveform, and biological structures in the neuromuscular system. Our model
explains and predicts various phenomena in neuromuscular stimulation, guides
new stimulation electrode and method design, and more importantly, facilitates
a fundamental understanding of the physical process during electrode-tissue
interaction. In our model, myelin is assumed to be inductive. The voltage
waveform resonance caused by this inductive myelin accounts for the much lower
stimulation threshold to activate motoneurons than muscle fibers, which is
observed with in vivo measurements. These findings confirmed the feasibility of
studying electrode-tissue interaction using a proper distributed-parameter
circuit. Our current application on the neuromuscular system also raises the
possibility that this distributed-parameter circuit model could potentially be
applied to study other neural tissues, including the Peripheral Nervous System
(PNS) and the Central Nervous System (CNS).
Monomer-induced customisation of UV-cured atelocollagen hydrogel networks
Date: 2018-12-13 00:00:00
Tasks: Vocal Bursts Type Prediction

The covalent functionalisation of type I atelocollagen with either
4-vinylbenzyl or methacrylamide residues is presented as a simple synthetic
strategy to achieve customisable, cell-friendly UV-cured hydrogel networks with
widespread clinical applicability. Molecular parameters, i.e. the type of
monomer, degree of atelocollagen functionalisation and UV curing solution, have
been systematically varied and their effect on gelation kinetics, swelling
behaviour, elastic properties and enzymatic degradability investigated.
UV-cured hydrogel networks deriving from atelocollagen precursors
functionalised with equivalent molar content of 4-vinylbenzyl and
methacrylamide adducts proved to display remarkably-different swelling ratio,
storage modulus and collagenase resistance, similarly to the case of UV-cured
hydrogel networks obtained with the same type of methacrylamide adduct, but
varied degree of functionalisation. UV-induced network formation of 4VBC
functionalised atelocollagen molecules yielded hydrogels with increased
stiffness and enzymatic stability, attributed to the molecular rigidity of
resulting aromatised crosslinking segment, whilst no toxic response was
observed with osteosarcoma G292 cells. Although to a lesser extent, the pH of
the UV-curing solution also proved to affect macroscopic hydrogel properties,
likely due to the altered organisation of atelocollagen molecules during
network formation. By leveraging the knowledge gained with classic synthetic
networks, this study highlights how the type of monomer can be conveniently
exploited to realise customisable atelocollagen hydrogels with controlled
structure-property relationships to meet the requirements of unmet clinical
applications.
Fast Nonlinear Fourier Transform Algorithms Using Higher Order Exponential Integrators
Date: 2019-10-16 00:00:00

The nonlinear Fourier transform (NFT) has recently gained significant
attention in fiber optic communications and other engineering fields. Although
several numerical algorithms for computing the NFT have been published, the
design of highly accurate low-complexity algorithms remains a challenge. In
this paper, we present new fast forward NFT algorithms that achieve accuracies
that are orders of magnitudes better than current methods, at comparable run
times and even for moderate sampling intervals. The new algorithms are compared
to existing solutions in multiple, extensive numerical examples.
Dynamic Ecological System Analysis
Date: 2019-12-29 00:00:00

This article develops a new mathematical method for holistic analysis of
nonlinear dynamic compartmental systems through the system decomposition
theory. The method is based on the novel dynamic system and subsystem
partitioning methodologies through which compartmental systems are decomposed
to the utmost level. The dynamic system and subsystem partitioning enable
tracking the evolution of the initial stocks, environmental inputs, and
intercompartmental system flows, as well as the associated storages derived
from these stocks, inputs, and flows individually and separately within the
system. Moreover, the transient and the dynamic direct, indirect, acyclic,
cycling, and transfer (diact) flows and associated storages transmitted along a
given flow path or from one compartment, directly or indirectly, to any other
are analytically characterized, systematically classified, and mathematically
formulated. Further, the article develops a dynamic technique based on the
diact transactions for the quantitative classification of interspecific
interactions and the determination of their strength within food webs. Major
concepts and quantities of the current static network analyses are also
extended to nonlinear dynamic settings and integrated with the proposed dynamic
measures and indices within the proposed unifying mathematical framework.
Therefore, the proposed methodology enables a holistic view and analysis of
ecological systems. We consider that this methodology brings a novel complex
system theory to the service of urgent and challenging environmental problems
of the day and has the potential to lead the way to a more formalistic
ecological science.
Construction and reduction of the Pareto set in asymmetric travelling salesman problem with two criteria
Authors: Zakharov Aleksey O., Kovalenko Yulia V.
Date: 2018-11-26 00:00:00

We consider the bicriteria asymmetric travelling salesman problem (bi-ATSP).
Optimal solution to a multicriteria problem is usually supposed to be the
Pareto set, which is rather wide in real-world problems. For the first time we
apply to the bi-ATSP the axiomatic approach of the Pareto set reduction
proposed by V. Noghin. We identify series of 'quanta of information' that
guarantee the reduction of the Pareto set for particular cases of the bi-ATSP.
An approximation of the Pareto set to the bi-ATSP is constructed by a new
multi-objective genetic algorithm. The experimental evaluation carried out in
this paper shows the degree of reduction of the Pareto set approximation for
various 'quanta of information' and various structures of the bi-ATSP instances
generated randomly or from TSPLIB problems.
Impact of the reference choice on scalp EEG connectivity estimation
Date: 2019-01-24 00:00:00
Tasks: Connectivity Estimation, EEG, Electroencephalogram (EEG), Functional Connectivity

Several scalp EEG functional connectivity studies, mostly clinical, seem to
overlook the reference electrode impact. The subsequent interpretation of brain
connectivity is thus often biased by the choice a non-neutral reference. This
study aims at systematically investigating these effects. As EEG reference, we
examined: the vertex electrode (Cz), the digitally linked mastoids (DLM), the
average reference (AVE), and the Reference Electrode Standardization Technique
(REST). As a connectivity metric, we used the imaginary part of coherency. We
tested simulated and real data (eyes open resting state), by evaluating the
influence of electrode density, effect of head model accuracy in the REST
transformation, and impact on the characterization of the topology of
functional networks from graph analysis. Simulations demonstrated that REST
significantly reduced the distortion of connectivity patterns when compared to
AVE, Cz and DLM references. Moreover, the availability of high-density EEG
systems and an accurate knowledge of the head model are crucial elements to
improve REST performance. For real data, a systematic change of the spatial
pattern of functional connectivity depending on the chosen reference was also
observed. The distortion of connectivity patterns was larger for the Cz
reference, and progressively decreases when using the DLM, the AVE, the REST.
Strikingly, we also showed that network attributes derived from graph analysis,
i.e., node degree and local efficiency, are significantly influenced by the EEG
reference choice. Overall, this study highlights that significant differences
arise in scalp EEG functional connectivity and graph network properties, in
dependence of the chosen reference. We hope our study will convey the message
that caution should be taken when interpreting and comparing results obtained
from different laboratories when using different reference schemes.
PT Symmetry, Non-Gaussian Path Integrals, and the Quantum Black-Scholes Equation
Date: 2019-01-18 00:00:00

The Accardi-Boukas quantum Black-Scholes framework, provides a means by which
one can apply the Hudson-Parthasarathy quantum stochastic calculus to problems
in finance. Solutions to these equations can be modelled using nonlocal
diffusion processes, via a Kramers-Moyal expansion, and this provides useful
tools to understand their behaviour. In this paper we develop further links
between quantum stochastic processes, and nonlocal diffusions, by inverting the
question, and showing how certain nonlocal diffusions can be written as quantum
stochastic processes. We then go on to show how one can use path integral
formalism, and PT symmetric quantum mechanics, to build a non-Gaussian kernel
function for the Accardi-Boukas quantum Black-Scholes. Behaviours observed in
the real market are a natural model output, rather than something that must be
deliberately included.
SUGAMAN: Describing Floor Plans for Visually Impaired by Annotation Learning and Proximity based Grammar
Authors: Goyal Shreya, Bhavsar Satya, Patel Shreya, Chattopadhyay Chiranjoy, Bhatnagar Gaurav
Date: 2018-11-14 00:00:00
Tasks: Navigate, Retrieval

In this paper, we propose SUGAMAN (Supervised and Unified framework using
Grammar and Annotation Model for Access and Navigation). SUGAMAN is a Hindi
word meaning "easy passage from one place to another". SUGAMAN synthesizes
textual description from a given floor plan image for the visually impaired. A
visually impaired person can navigate in an indoor environment using the
textual description generated by SUGAMAN. With the help of a text reader
software, the target user can understand the rooms within the building and
arrangement of furniture to navigate. SUGAMAN is the first framework for
describing a floor plan and giving direction for obstacle-free movement within
a building. We learn $5$ classes of room categories from $1355$ room image
samples under a supervised learning paradigm. These learned annotations are fed
into a description synthesis framework to yield a holistic description of a
floor plan image. We demonstrate the performance of various supervised
classifiers on room learning. We also provide a comparative analysis of system
generated and human written descriptions. SUGAMAN gives state of the art
performance on challenging, real-world floor plan images. This work can be
applied to areas like understanding floor plans of historical monuments,
stability analysis of buildings, and retrieval.
Automatic lesion boundary detection in dermoscopy
Authors: Kechyn Glib
Date: 2018-11-23 00:00:00
Tasks: Boundary Detection, Segmentation

This manuscript addresses the problem of the automatic lesion boundary
detection in dermoscopy, using deep neural networks. An approach is based on
the adaptation of the U-net convolutional neural network with skip connections
for lesion boundary segmentation task. I hope this paper could serve, to some
extent, as an experiment of using deep convolutional networks in biomedical
segmentation task and as a guideline of the boundary detection benchmark,
inspiring further attempts and researches.
Integral Geometric Dual Distributions of Multilinear Models
Authors: Brandt Sami Sebastian
Date: 2018-11-22 00:00:00

We propose an integral geometric approach for computing dual distributions
for the parameter distributions of multilinear models. The dual distributions
can be computed from, for example, the parameter distributions of conics,
multiple view tensors, homographies, or as simple entities as points, lines,
and planes. The dual distributions have analytical forms that follow from the
asymptotic normality property of the maximum likelihood estimator and an
application of integral transforms, fundamentally the generalised Radon
transforms, on the probability density of the parameters. The approach allows
us, for instance, to look at the uncertainty distributions in feature
distributions, which are essentially tied to the distribution of training data,
and helps us to derive conditional distributions for interesting variables and
characterise confidence intervals of the estimates.
Relation Networks for Optic Disc and Fovea Localization in Retinal Images
Authors: Babu Sudharshan Chandra, Maiya Shishira R, Elango Sivasankar
Date: 2018-11-23 00:00:00
Tasks: Relation

Diabetic Retinopathy is the leading cause of blindness in the world. At least
90\% of new cases can be reduced with proper treatment and monitoring of the
eyes. However, scanning the entire population of patients is a difficult
endeavor. Computer-aided diagnosis tools in retinal image analysis can make the
process scalable and efficient. In this work, we focus on the problem of
localizing the centers of the Optic disc and Fovea, a task crucial to the
analysis of retinal scans. Current systems recognize the Optic disc and Fovea
individually, without exploiting their relations during learning. We propose a
novel approach to localizing the centers of the Optic disc and Fovea by
simultaneously processing them and modeling their relative geometry and
appearance. We show that our approach improves localization and recognition by
incorporating object-object relations efficiently, and achieves highly
competitive results.
AI Matrix - Synthetic Benchmarks for DNN
Authors: Wei Wei, Xu Lingjie, Jin Lingling, Zhang Wei, Zhang Tianjun
Date: 2018-11-27 00:00:00
Tasks: Benchmarking, CPU, GPU

Deep neural network (DNN) architectures, such as convolutional neural
networks (CNN), involve heavy computation and require hardware, such as CPU,
GPU, and AI accelerators, to provide the massive computing power. With the many
varieties of AI hardware prevailing on the market, it is often hard to decide
which one is the best to use. Thus, benchmarking AI hardware effectively
becomes important and is of great help to select and optimize AI hardware.
Unfortunately, there are few AI benchmarks available in both academia and
industry. Examples are BenchNN[1], DeepBench[2], and Dawn Bench[3], which are
usually a collection of typical real DNN applications. While these benchmarks
provide performance comparison across different AI hardware, they suffer from a
number of drawbacks. First, they cannot adapt to the emerging changes of DNN
algorithms and are fixed once selected. Second, they contain tens to hundreds
of applications and take very long time to finish running. Third, they are
mainly selected from open sources, which are restricted by copyright and are
not representable to proprietary applications. In this work, a synthetic
benchmarks framework is firstly proposed to address the above drawbacks of AI
benchmarks. Instead of pre-selecting a set of open-sourced benchmarks and
running all of them, the synthetic approach generates only a one or few
benchmarks that best represent a broad range of applications using profiled
workload characteristics data of these applications. Thus, it can adapt to
emerging changes of new DNN algorithms by re-profiling new applications and
updating itself, greatly reduce benchmark count and running time, and strongly
represent DNN applications of interests. The generated benchmarks are called AI
Matrix, serving as a performance benchmarks matching the statistical workload
characteristics of a combination of applications of interests.
Incorporating Deep Features in the Analysis of Tissue Microarray Images
Authors: Yan Donghui, Randolph Timothy W., Zou Jian, Gong Peng
Date: 2018-11-26 00:00:00
Tasks: Clustering

Tissue microarray (TMA) images have been used increasingly often in cancer
studies and the validation of biomarkers. TACOMA---a cutting-edge automatic
scoring algorithm for TMA images---is comparable to pathologists in terms of
accuracy and repeatability. Here we consider how this algorithm may be further
improved. Inspired by the recent success of deep learning, we propose to
incorporate representations learnable through computation. We explore
representations of a group nature through unsupervised learning, e.g.,
hierarchical clustering and recursive space partition. Information carried by
clustering or spatial partitioning may be more concrete than the labels when
the data are heterogeneous, or could help when the labels are noisy. The use of
such information could be viewed as regularization in model fitting. It is
motivated by major challenges in TMA image scoring---heterogeneity and label
noise, and the cluster assumption in semi-supervised learning. Using this
information on TMA images of breast cancer, we have reduced the error rate of
TACOMA by about 6%. Further simulations on synthetic data provide insights on
when such representations would likely help. Although we focus on TMAs,
learnable representations of this type are expected to be applicable in other
settings.