pwc-archive/papers-with-abstracts huggingface dataset:
A new model for the implementation of positive and negative emotion recognition
Authors: Jennifer Sorinasa, Juan C. Fernandez-Troyano, Mikel Val-Calvo, Jose Manuel Ferrández, Eduardo Fernandez
Date: 2019-05-01 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Classification, Emotion Recognition

The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective BCI (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. 12 seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-base emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Moreover, although results were not conclusive, new insights regarding subject-independent approximation have been discussed.
Stability Analysis of Simple and Online User Association Policies for Millimeter Wave Networks
Date: 2020-03-13 00:00:00
Tasks: Fairness

In a millimeter wave (mmWave) network, user association, the process of
deciding as to which base station (BS) a given user should associate with, is a
crucial process which affects the throughput and delay performance seen by
users in the network and the amount of load at each BS. In the existing
research literature, the stability region of a user association policy, i.e.,
the set of user arrival rates for which the user association policy stabilizes
the network, has not been analytically characterized for any user association
policy for mmWave networks. In this paper, we study the user association
problem in mmWave networks and compare the performance of four user association
policies: Signal to Noise Ratio (SNR) based, Throughput based, Load based and
Mixed. All these policies are simple, easy to implement, distributed and
online. We use a Continuous Time Markov Chain (CTMC) model and Lyapunov
function techniques to analytically characterize the stability region of each
of the above four user association policies. We also evaluate the performance
of the above four user association policies in a large mmWave network, in which
link qualities fluctuate with time and users are mobile, via detailed
simulations. Our results show that the Throughput based policy outperforms the
other three user association policies in terms of stability region as well as
average throughput, average delay and fairness performance.
Interfacing PDM MEMS microphones with PFM spiking systems: Application for Neuromorphic Auditory Sensors
Date: 2020-03-16 00:00:00

In neuromorphic engineering, computation is commonly performed
asynchronously, mimicking the way in which nervous systems process information:
spike by spike. The Neuromorphic Auditory Sensor (NAS) has been implemented
under this principle: applying different spike-based Signal Processing blocks.
Computation in the spike domain requires the conversion of signals from analog
or digital representation to the spike domain, which could present a speed
constraint in many cases. This paper presents a spike-based system to convert
audio information from low-power pulse density modulation (PDM)
MicroElectroMechanical Systems (MEMS) microphones into rate coded spike
frequencies. These spikes represent the input signal of the NAS, avoiding the
analog or digital to spike conversion, and therefore improving the time
response of the NAS. This conversion has been done in VHDL as an interface for
PDM microphones, converting their pulses into temporal distributed spikes
following a pulse frequency modulation (PFM) scheme with an accurate
Inter-Spike-Interval, known as "PDM to spikes interface" (PSI). This was tested
in two scenarios, first as a stand-alone circuit for its characterization, and
then integrated with a NAS for verification. The PSI achieves a Total Harmonic
Distortion (THD) of -39.51dB and a Signal-to-Noise Ratio (SNR) of 59.12dB,
demands less than 1\% of the resources of a Spartan-6 FPGA and has a power
consumption below 5mW.
Optimal Storage Arbitrage under Net Metering using Linear Programming
Date: 2019-08-15 00:00:00
Tasks: Model Predictive Control

We formulate the optimal energy arbitrage problem for a piecewise linear cost
function for energy storage devices using linear programming (LP). The LP
formulation is based on the equivalent minimization of the epigraph. This
formulation considers ramping and capacity constraints, charging and
discharging efficiency losses of the storage, inelastic consumer load and local
renewable generation in presence of net-metering which facilitates selling of
energy to the grid and incentivizes consumers to install renewable generation
and energy storage. We consider the case where the consumer loads, electricity
prices, and renewable generations at different instances are uncertain. These
uncertain quantities are predicted using an Auto-Regressive Moving Average
(ARMA) model and used in a model predictive control (MPC) framework to obtain
the arbitrage decision at each instance. In numerical results we present the
sensitivity analysis of storage performing arbitrage with varying ramping
batteries and different ratio of selling and buying price of electricity.
Set-valued risk statistics with the time value of money
Authors: Fei Sun, Xiaozhi Fan, Weitao Liu
Date: 2019-04-16 00:00:00

The time value of money is a critical factor not only in risk analysis, but also in insurance and financial applications. In this paper, we consider a special class of set-valued risk statistics by introducing the time value of money. In fact, the risk statistics established by this method is closer to financial reality than traditional ones. Moreover, this new risk statistic can be uesd for the quantification of portfolio risk. By further developing the properties related to these risk statistics, we are able to derive representation results for such risk.
Optimizing enzymatic catalysts for rapid turnover of substrates with low enzyme sequestration
Authors: Abhishek Deshpande, Thomas E. Ouldridge
Date: 2019-05-02 00:00:00

We analyse the mechanism of enzyme-substrate catalysis from the perspective of minimizing the load on the enzymes through sequestration, whilst maintaining at least a minimum reaction flux. In particular, we ask: which binding free energies of the enzyme-substrate and enzyme-product reaction intermediates minimize the fraction of enzymes sequestered in complexes, while sustaining at a certain minimal flux? Under reasonable biophysical assumptions, we find that the optimal design will saturate the bound on the minimal flux, and reflects a basic trade-off in catalytic operation. If both binding free energies are too high, there is low sequestration, but the effective progress of the reaction is hampered. If both binding free energies are too low, there is high sequestration, and the reaction flux may also be suppressed in extreme cases. The optimal binding free energies are therefore neither too high nor too low, but in fact moderate. Moreover, the optimal difference in substrate and product binding free energies, which contributes to the thermodynamic driving force of the reaction, is in general strongly constrained by the intrinsic free-energy difference between products and reactants. Both the strategies of using a negative binding free-energy difference to drive the catalyst-bound reaction forward, and of using a positive binding free-energy difference to enhance detachment of the product, are limited in their efficacy.
DNA energy constraints shape biological evolutionary trajectories
Date: 2019-11-27 00:00:00

Most living systems rely on double-stranded DNA (dsDNA) to store their
genetic information and perpetuate themselves. This biological information has
been considered the main target of evolution. However, here we show that
symmetries and patterns in the dsDNA sequence can emerge from the physical
peculiarities of the dsDNA molecule itself and the maximum entropy principle
alone, rather than from biological or environmental evolutionary pressure. The
randomness justifies the human codon biases and context-dependent mutation
patterns in human populations. Thus, the DNA "exceptional symmetries", emerged
from the randomness, have to be taken into account when looking for the DNA
encoded information. Our results suggest that the double helix energy
constraints and, more generally, the physical properties of the dsDNA are the
hard drivers of the overall DNA sequence architecture, whereas the biological
selective processes act as soft drivers, which only under extraordinary
circumstances overtake the overall entropy content of the genome.
Toward Millimeter Wave Joint Radar-Communications: A Signal Processing Perspective
Date: 2019-05-18 00:00:00
Tasks: compressed sensing

Synergistic design of communications and radar systems with common spectral
and hardware resources is heralding a new era of efficiently utilizing a
limited radio-frequency spectrum. Such a joint radar-communications (JRC) model
has advantages of low-cost, compact size, less power consumption, spectrum
sharing, improved performance, and safety due to enhanced information sharing.
Today, millimeter-wave (mm-wave) communications have emerged as the preferred
technology for short distance wireless links because they provide transmission
bandwidth that is several gigahertz wide. This band is also promising for
short-range radar applications, which benefit from the high-range resolution
arising from large transmit signal bandwidths. Signal processing techniques are
critical in implementation of mmWave JRC systems. Major challenges are joint
waveform design and performance criteria that would optimally trade-off between
communications and radar functionalities. Novel multiple-input-multiple-output
(MIMO) signal processing techniques are required because mmWave JRC systems
employ large antenna arrays. There are opportunities to exploit recent advances
in cognition, compressed sensing, and machine learning to reduce required
resources and dynamically allocate them with low overheads. This article
provides a signal processing perspective of mmWave JRC systems with an emphasis
on waveform design.
Algorithm for the reconstruction of dynamic objects in CT-scanning using optical flow
Date: 2019-04-17 00:00:00
Tasks: Object, Optical Flow Estimation

Computed Tomography is a powerful imaging technique that allows
non-destructive visualization of the interior of physical objects in different
scientific areas. In traditional reconstruction techniques the object of
interest is mostly considered to be static, which gives artefacts if the object
is moving during the data acquisition. In this paper we present a method that,
given only scan results of multiple successive scans, can estimate the motion
and correct the CT-images for this motion assuming that the motion field is
smooth over the complete domain using optical flow. The proposed method is
validated on simulated scan data. The main contribution is that we show we can
use the optical flow technique from imaging to correct CT-scan images for
motion.
City classification from multiple real-world sound scenes
Date: 2019-07-29 00:00:00
Tasks: Acoustic Scene Classification, Classification, Event Detection, Multi-Task Learning, Scene Classification, Sound Event Detection

The majority of sound scene analysis work focuses on one of two clearly
defined tasks: acoustic scene classification or sound event detection. Whilst
this separation of tasks is useful for problem definition, they inherently
ignore some subtleties of the real-world, in particular how humans vary in how
they describe a scene. Some will describe the weather and features within it,
others will use a holistic descriptor like `park', and others still will use
unique identifiers such as cities or names. In this paper, we undertake the
task of automatic city classification to ask whether we can recognize a city
from a set of sound scenes? In this problem each city has recordings from
multiple scenes. We test a series of methods for this novel task and show that
a simple convolutional neural network (CNN) can achieve accuracy of 50%. This
is less than the acoustic scene classification task baseline in the DCASE 2018
ASC challenge on the same data. A simple adaptation to the class labels of
pairing city labels with grouped scenes, accuracy increases to 52%, closer to
the simpler scene classification task. Finally we also formulate the problem in
a multi-task learning framework and achieve an accuracy of 56%, outperforming
the aforementioned approaches.
Fractal Time Series Analysis of Social Network Activities
Date: 2019-04-10 00:00:00
Tasks: Time Series, Time Series Analysis

In the work, a comparative correlation and fractal analysis of time series of
Bitcoin crypto currency rate and community activities in social networks
associated with Bitcoin was conducted. A significant correlation between the
Bitcoin rate and the community activities was detected. Time series fractal
analysis indicated the presence of self-similar and multifractal properties.
The results of researches showed that the series having a strong correlation
dependence have a similar multifractal structure.
Optimal interplay between synaptic strengths and network structure enhances activity fluctuations and information propagation in hierarchical modular networks
Date: 2020-04-10 00:00:00

In network models of spiking neurons, the joint impact of network structure
and synaptic parameters on activity propagation is still an open problem. Here
we use an information-theoretical approach to investigate activity propagation
in spiking networks with hierarchical modular topology. We observe that
optimized pairwise information propagation emerges due to the increase of
either (i) the global synaptic strength parameter or (ii) the number of modules
in the network, while the network size remains constant. At the population
level, information propagation of activity among adjacent modules is enhanced
as the number of modules increases until a maximum value is reached and then
decreases, showing that there is an optimal interplay between synaptic strength
and modularity for population information flow. This is in contrast to
information propagation evaluated among pairs of neurons, which attains maximum
value at the maximum values of these two parameter ranges. By examining the
network behavior under increase of synaptic strength and number of modules we
find that these increases are associated with two different effects: (i)
increase of autocorrelations among individual neurons, and (ii) increase of
cross-correlations among pairs of neurons. The second effect is associated with
better information propagation in the network. Our results suggest roles that
link topological features and synaptic strength levels to the transmission of
information in cortical networks.v
Dynamics of particle uptake at cell membranes
Authors: Felix Frey, Falko Ziebert, Ulrich S. Schwarz
Date: 2019-05-03 00:00:00

Receptor-mediated endocytosis requires that the energy of adhesion overcomes the deformation energy of the plasma membrane. The resulting driving force is balanced by dissipative forces, leading to deterministic dynamical equations. While the shape of the free membrane does not play an important role for tensed and loose membranes, in the intermediate regime it leads to an important energy barrier. Here we show that this barrier is similar to but different from an effective line tension and suggest a simple analytical approximation for it. We then explore the rich dynamics of uptake for particles of different shapes and present the corresponding dynamical state diagrams. We also extend our model to include stochastic fluctuations, which facilitate uptake and lead to corresponding changes in the phase diagrams.
Public goods in networks with constraints on sharing
Authors: Stefanie Gerke, Gregory Gutin, Sung-Ha Hwang, Philip Neary
Date: 2019-05-05 00:00:00

This paper considers incentives to provide goods that are partially shareable along social links. We introduce a model in which each individual in a social network not only decides how much of a shareable good to provide, but also decides which subset of neighbours to nominate as co-beneficiaries. An outcome of the model specifies an endogenously generated subnetwork of the original network and a public goods game occurring over the realised subnetwork. We prove the existence of specialised pure strategy Nash equilibria: those in which some individuals contribute while the remaining individuals free ride. We then consider how the set of efficient specialised equilibria vary as the constraints on sharing are relaxed and we show that, paradoxically, an increase in shareability may decrease efficiency.
A Binomial Asset Pricing Model in a Categorical Setting
Date: 2019-12-16 00:00:00

Adachi and Ryu introduced a category Prob of probability spaces whose objects
are all probability spaces and whose arrows correspond to measurable functions
satisfying an absolutely continuous requirement in [Adachi and Ryu, 2019]. In
this paper, we develop a binomial asset pricing model based on Prob. We
introduce generalized filtrations with which we can represent situations such
as some agents forget information at some specific time. We investigate the
valuations of financial claims along this type of non-standard filtrations.
Cooperative Evaluation of the Cause of Urban Traffic Congestion via Connected Vehicles
Date: 2019-04-26 00:00:00
Tasks: Decision Making

We developed a distributed data mining system to elaborate a decision concerning the cause of urban traffic congestion via emerging connected vehicle (CV) technology. We observe this complex phenomena through the interactions between vehicles exchanging messages via Vehicle to Vehicle (V2V) communication. Results are based on real-time simulation generated scenarios extended from the real-world traffic Travel and Activity PAtterns Simulation (TAPAS) Cologne scenario. We evaluate a Voting Procedure (VP) useful for obtaining deeper insights using cooperation between vehicles, Belief Functions (BF) aim at improving representation of information and a Data Association Technique (DAT) aiming at data mining and extracting the association rules from the messages exchanged. Methods are tested and compared using a microscopic urban mobility simulator, SUMO and a network simulator, ns-2, for the simulation of communication between CVs. Compared to the Back-Propagation algorithm (BP) extensively used in the past literature, our performance evaluation shows that the proposed methods enhance the estimation of the cause of congestion by 48\% for the proposed VP, 58\% for the BF, 71\% for the DAT and 70\% for \textbeta-DAT. The methods also enhance detection time from 7.09\% to 10.3\%, and \textbeta-DAT outperforms BP by approximately 1.25\% less false alarms triggered by the network, which can be significant in the context of real-time decision making. We show that a market penetration rate between 63\% and 75\% is enough to ensure satisfactory performance.
Lasso under Multi-way Clustering: Estimation and Post-selection Inference
Date: 2019-08-21 00:00:00
Tasks: Clustering, regression

This paper studies high-dimensional regression models with lasso when data is
sampled under multi-way clustering. First, we establish convergence rates for
the lasso and post-lasso estimators. Second, we propose a novel inference
method based on a post-double-selection procedure and show its asymptotic
validity. Our procedure can be easily implemented with existing statistical
packages. Simulation results demonstrate that the proposed procedure works well
in finite sample. We illustrate the proposed method with a couple of empirical
applications to development and growth economics.
A Review on understanding Brain, and Memory Retention and Recall Processes using EEG and fMRI techniques
Date: 2019-04-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Hippocampus

Human memory -- the learning of new information involves changes at the
synaptic level between neurons dedicated for storage of in-formation.
Generally, memory is classified as Long-Term Memory and Short-Term Memory. The
various types of the memory and their disorder are widely studied using
neuroimaging techniques like Electroencephalography (EEG) and functional
Magnetic Resonance Imaging (fMRI). Brain is effectively occupied with the
capabilities of learning, retention and recall. The brain regions (pre-frontal
cortex, associated hippocampus cortices and their interactions with other
lobes) involved in memory recall tasks focuses on understanding the memory
retention and recall processes. However, due to highly complicated and dynamic
mechanisms of brain, the specific regions where information may reside are not
completely explored. In this research paper, recent memory literature using EEG
and fMRI studies is reviewed to understand the memory retention and recall
processes as well as the various brain regions associated with these processes.
A number of stimuli which are reported in previous studies are evaluated and
discussed. Furthermore, the challenges which are being faced by researchers in
EEG and fMRI methodologies are also presented. Recommendations for the future
research related to memory retention and recall are also discussed at the end.
On the reorderability of node-filtered order complexes
Date: 2019-07-24 00:00:00

Growing graphs describe a multitude of developing processes from maturing
brains to expanding vocabularies to burgeoning public transit systems. Each of
these growing processes likely adheres to proliferation rules that establish an
effective order of node and connection emergence. When followed, such
proliferation rules allow the system to properly develop along a predetermined
trajectory. But rules are rarely followed. Here we ask what topological changes
in the growing graph trajectories might occur after the specific but basic
perturbation of permuting the node emergence order. Specifically we harness
applied topological methods to determine which of six growing graph models
exhibit topology that is robust to randomizing node order, termed global
reorderability, and robust to temporally-local node swaps, termed local
reorderability. We find that the six graph models fall upon a spectrum of both
local and global reorderability, and furthermore we provide theoretical
connections between robustness to node pair ordering and robustness to
arbitrary node orderings. Finally we discuss real-world applications of
reorderability analyses and suggest possibilities for designing reorderable
networks.