pwc-archive/papers-with-abstracts huggingface dataset:
Uncovering ecological state dynamics with hidden Markov models
Date: 2020-07-14 00:00:00

Ecological systems can often be characterised by changes among a finite set
of underlying states pertaining to individuals, populations, communities, or
entire ecosystems through time. Owing to the inherent difficulty of empirical
field studies, ecological state dynamics operating at any level of this
hierarchy can often be unobservable or "hidden". Ecologists must therefore
often contend with incomplete or indirect observations that are somehow related
to these underlying processes. By formally disentangling state and observation
processes based on simple yet powerful mathematical properties that can be used
to describe many ecological phenomena, hidden Markov models (HMMs) can
facilitate inferences about complex system state dynamics that might otherwise
be intractable. However, while HMMs are routinely applied in other disciplines,
they have only recently begun to gain traction within the broader ecological
community. We provide a gentle introduction to HMMs, establish some common
terminology, and review the immense scope of HMMs for applied ecological
research. We also provide a supplemental tutorial on some of the more technical
aspects of HMM implementation and interpretation. By illustrating how
practitioners can use a simple conceptual template to customise HMMs for their
specific systems of interest, revealing methodological links between existing
applications, and highlighting some practical considerations and limitations of
these approaches, our goal is to help establish HMMs as a fundamental
inferential tool for ecologists.
Design of Breathing-states Detector for m-Health Platform using Seismocardiographic Signal
Authors: Tilendra Choudhary, L. N. Sharma, M. K. Bhuyan, Kangkana Bora
Date: 2020-02-24 00:00:00

In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of-breathings, such as breathlessness, normal breathing, and long and labored breathing. For this, it is needed to measure cardiac-induced chest-wall vibrations, reflected in the SCG signal. Orthogonal subspace projection is employed to extract the SCG cycles with the help of a concurrent ECG signal. Subsequently, fifteen statistically significant morphological-features are extracted from each of the SCG cycles. These features can efficiently characterize physiological changes due to varying respiratory rates. Stacked autoencoder (SAE) based architecture is employed for the identification of different respiratory-effort levels. The performance of the proposed method is evaluated and compared with other standard classifiers for 1147 analyzed SCG-beats. The proposed method gives an overall average accuracy of 91.45% in recognizing three different breathing states. The quantitative analysis of the performance results clearly shows the effectiveness of the proposed framework. It may be employed in various healthcare applications, such as pre-screening medical sensors and IoT based remote health-monitoring systems.
Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework
Date: 2020-02-24 00:00:00
Tasks: Distributed Optimization

In this paper, the alternating direction method of multipliers (ADMM) is
investigated for distributed optimization problems in a networked multi-agent
system. In particular, a new adaptive-gain ADMM algorithm is derived in a
closed form and under the standard convex property in order to greatly speed up
convergence of ADMM-based distributed optimization. Using Lyapunov direct
approach, the proposed solution embeds control gains into weighted network
matrix among the agents and uses those weights as adaptive penalty gains in the
augmented Lagrangian. It is shown that the proposed closed loop gain adaptation
scheme significantly improves the convergence time of underlying ADMM
optimization. Convergence analysis is provided and simulation results are
included to demonstrate the effectiveness of the proposed scheme.
Unique Scales Preserve Self-Similar Integrate-and-Fire Functionality of Neuronal Clusters
Date: 2020-02-24 00:00:00

Identifying the brain's neuronal cluster size to be presented as nodes in a
network computation is critical to both neuroscience and artificial
intelligence, as these define the cognitive blocks required for building
intelligent computation. Experiments support many forms and sizes of neural
clustering, while neural mass models (NMM) assume scale-invariant
functionality. Here, we use computational simulations with brain-derived fMRI
network to show that not only brain network stays structurally self-similar
continuously across scales, but also neuron-like signal integration
functionality is preserved at particular scales. As such, we propose a
coarse-graining of network of neurons to ensemble-nodes, with multiple spikes
making up its ensemble-spike, and time re-scaling factor defining its
ensemble-time step. The fractal-like spatiotemporal structure and function that
emerge permit strategic choice in bridging across experimental scales for
computational modeling, while also suggesting regulatory constraints on
developmental and/or evolutionary "growth spurts" in brain size, as per
punctuated equilibrium theories in evolutionary biology.
Adaptive Multi-Receiver Coded Slotted ALOHA for Indoor Optical Wireless Communications
Date: 2020-04-17 00:00:00
Tasks: Decoder, Diversity

In this paper, we design a novel high-throughput random access scheme for an
indoor optical wireless communication (OWC) massive Internet of Things (IoT)
scenario. Due to the large density of both IoT devices and OWC access points
(APs), we base the proposed scheme on multi-receiver coded slotted ALOHA. In
this scenario, collisions at APs are resolved by a centralized interference
cancellation decoder that may exploit both spatial and temporal diversity. By
applying adaptive control of each OWC AP field of view (FOV), the proposed
system is able to dynamically adapt to different IoT device activation rates,
in order to maintain a high total throughput. Using illustrative simulation
results, we demonstrate the design methodology and performance possibilities of
the proposed method.
Dynamic Power Allocation and Virtual Cell Formation for Throughput-Optimal Vehicular Edge Networks in Highway Transportation
Date: 2020-03-13 00:00:00

This paper investigates highly mobile vehicular networks from users'
perspectives in highway transportation. Particularly, a centralized
software-defined architecture is introduced in which centralized resources can
be assigned, programmed, and controlled using the anchor nodes (ANs) of the
edge servers. Unlike the legacy networks, where a typical user is served from
only one access point (AP), in the proposed system model, a vehicle user is
served from multiple APs simultaneously. While this increases the reliability
and the spectral efficiency of the assisted users, it also necessitates an
accurate power allocation in all transmission time slots. As such, a joint user
association and power allocation problem is formulated to achieve enhanced
reliability and weighted user sum rate. However, the formulated problem is a
complex combinatorial problem, remarkably hard to solve. Therefore,
fine-grained machine learning algorithms are used to efficiently optimize joint
user associations and power allocations of the APs in a highly mobile vehicular
network. Furthermore, a distributed single-agent reinforcement learning
algorithm, namely SARL-MARL, is proposed which obtains nearly identical
genie-aided optimal solutions within a nominal number of training episodes than
the baseline solution. Simulation results validate that our solution
outperforms existing schemes and can attain genie-aided optimal performances.
Identification and Validation of the SNV Biomarkers Based on Multi-Dimensional Patterns
Authors: Bo Li, Junying Zhang, Liang Yu
Date: 2020-02-25 00:00:00

Background: Single nucleotide variants (SNVs) are detected as different distributions of DNA samples of distinct types of cancer patients. Even though, it is an exacting task to select the appropriate method to identify cancer to the greatest extent of SNVs. Results: In this paper, we proposed a biomarker concept based on SNV patterns in different feature dimensions. Raw dataset (2761 samples) consisting of twelve different cancers was obtained from TCGA (The Cancer Genome Atlas). After preliminary screening of 562,321 DNA mutation sites in the samples, the mutation sites were extracted and characterized by cancer types in six different SNV feature dimensions. In this study, we found that the extracted features showed similar distribution in the cluster center of the disease type of the samples. After the initial processing of the raw data, the sample was more focused on the subtype distribution of the cancer or the cancer at the SNV level. We used k-nearest neighbors (KNN) to classify the extracted features and Leave-One-Out cross verified them. The accuracy of classifying is stable at around 97% and reached 97.43% at the highest. During the validation phase, we found validated oncogenes in the loci of the features with the highest importance among nine cancers. Conclusions: In summary, the samples showed consistent patterns according to the cancer in which it belongs. It is feasible to classify the cancer of the sample by the distribution of different dimensions of the SNVs and has a high accuracy. And has potential implications for the discovery of cancer-causing genes.
Controllable Sequence-To-Sequence Neural TTS with LPCNET Backend for Real-time Speech Synthesis on CPU
Date: 2020-02-25 00:00:00
Tasks: CPU, Prosody Prediction, Sentence, Speech Synthesis

State-of-the-art sequence-to-sequence acoustic networks, that convert a
phonetic sequence to a sequence of spectral features with no explicit prosody
prediction, generate speech with close to natural quality, when cascaded with
neural vocoders, such as Wavenet. However, the combined system is typically too
heavy for real-time speech synthesis on a CPU. In this work we present a
sequence-to-sequence acoustic network combined with lightweight LPCNet neural
vocoder, designed for real-time speech synthesis on a CPU. In addition, the
system allows sentence-level pace and expressivity control at inference time.
We demonstrate that the proposed system can synthesize high quality 22 kHz
speech in real-time on a general-purpose CPU. In terms of MOS score degradation
relative to PCM, the system attained as low as 6.1-6.5% for quality and 6.3-
7.0% for expressiveness, reaching equivalent or better quality when compared to
a similar system with a Wavenet vocoder backend.
RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling Based Video Coding
Date: 2020-02-25 00:00:00
Tasks: Super-Resolution

Integrating deep learning techniques into the video coding framework gains
significant improvement compared to the standard compression techniques,
especially, applying super-resolution (up-sampling) in down-sampling based
video coding as post-processing. However, different from the conventional
super-resolution works, the output can be distorted by the various compression
artifacts in the decoded frames. The straightforward solution is to integrate
the artifacts removing techniques before super-resolution. But some helpful
features may be removed together with the artifacts, which will degrade the
performance of super-resolution. To address this problem, we proposed a
restoration-reconstruction deep neural network (RR-DnCNN) using the
degradation-aware techniques. Moreover, to prevent the loss of essential
features in the very deep network from restoration to super-resolution, we
leverage up-sampling skip connections to compensate for the lost information
from restoration layers. It is called restoration-reconstruction u-shaped deep
neural network (RR-DnCNN v2.0). As a result, our RR-DnCNN v2.0 can achieve
17.02% BD-rate reduction on UHD resolution compared to the standard H.265/HEVC.
The source code is available at https://minhmanho.github.io/rrdncnn/.
Optimal Switching of Controlled Rectifiers
Date: 2020-02-25 00:00:00

This paper discusses a linear programming approach for designing switching
signals for controlled rectifiers to achieve a low input current & output
voltage total harmonic distortions. The focus here is on fully controlled
rectifiers made with four-quadrant MOSFET based switches. This topology, unlike
thyristor based rectifiers, can be turned ON or OFF anytime. Yet another
assumption made here is that the current drawn by the load is constant. The
basic idea for designing the waveform is to first time discretize its one
period. This discretization, along with Parsevals identity lead to a linear
programming formulation for minimizing a weighted sum of total harmonic
distortions of the input current and the output voltages. The LPs so obtained
can be solved efficiently using standard solvers to obtain the switching
instants. The method can be used for both single phase and three-phase
rectifiers. Simulations are provided for corroboration.
Molecular recognition between cadherins studied by a coarse-grained model interacting with a coevolutionary potential
Date: 2020-02-25 00:00:00
Tasks: valid

Studying the conformations involved in the dimerization of cadherins is
highly relevant to understand the development of tissue and its failure, which
is associated with tumors and metastases. Experimental techniques, like X-ray
crystallography, can usually report only the most stable conformations, missing
minority states that could nonetheless be important for the recognition
mechanism. Computer simulations could be a valid complement to the experimental
approach. However, standard all-atom protein models in explicit solvent are
computationally too demanding to search thoroughly the conformational space of
multiple chains composed of several hundreds of amino acids. To reach this
goal, we resorted to a coarse-grained model in implicit solvent. The standard
problem with this kind of models is to find a realistic potential to describe
their interactions. We used coevolutionary information from cadherin
alignments, corrected by a statistical potential, to build an interaction
potential which is agnostic of the experimental conformations of the protein.
Using this model, we explored the conformational space of multi-chain systems
and validated the results comparing with experimental data. We identified
dimeric conformations that are sequence-specific and that can be useful to
rationalize the mechanism of recognition between cadherins.
Model-based Joint Bit Allocation between Geometry and Color for Video-based 3D Point Cloud Compression
Date: 2020-03-27 00:00:00

Rate distortion optimization plays a very important role in image/video
coding. But for 3D point cloud, this problem has not been investigated. In this
paper, the rate and distortion characteristics of 3D point cloud are
investigated in detail, and a typical and challenging rate distortion
optimization problem is solved for 3D point cloud. Specifically, since the
quality of the reconstructed 3D point cloud depends on both the geometry and
color distortions, we first propose analytical rate and distortion models for
the geometry and color information in video-based 3D point cloud compression
platform, and then solve the joint bit allocation problem for geometry and
color based on the derived models. To maximize the reconstructed quality of 3D
point cloud, the bit allocation problem is formulated as a constrained
optimization problem and solved by an interior point method. Experimental
results show that the rate-distortion performance of the proposed solution is
close to that obtained with exhaustive search but at only 0.68% of its time
complexity. Moreover, the proposed rate and distortion models can also be used
for the other rate-distortion optimization problems (such as prediction mode
decision) and rate control technologies for 3D point cloud coding in the
future.
Error Analysis of Programmable Metasurfaces for Beam Steering
Date: 2020-02-25 00:00:00

Recent years have seen the emergence of programmable metasurfaces, where the
user can modify the EM response of the device via software. Adding
reconfigurability to the already powerful EM capabilities of metasurfaces opens
the door to novel cyber-physical systems with exciting applications in domains
such as holography, cloaking, or wireless communications. This paradigm shift,
however, comes with a non-trivial increase of the complexity of the
metasurfaces that will pose new reliability challenges stemming from the need
to integrate tuning, control, and communication resources to implement the
programmability. While metasurfaces will become prone to failures, little is
known about their tolerance to errors. To bridge this gap, this paper examines
the reliability problem in programmable metamaterials by proposing an error
model and a general methodology for error analysis. To derive the error model,
the causes and potential impact of faults are identified and discussed
qualitatively. The methodology is presented and exemplified for beam steering,
which constitutes a relevant case for programmable metasurfaces. Results show
that performance degradation depends on the type of error and its spatial
distribution and that, in beam steering, error rates over 20% can still be
considered acceptable.
A Quantized Representation of Intertemporal Choice in the Brain
Date: 2020-09-15 00:00:00

Value [4][5] is typically modeled using a continuous representation (i.e., a
Real number). A discrete representation of value has recently been postulated
[6]. A quantized representation of probability in the brain was also posited
and supported by experimental data [7]. Value and probability are inter-related
via Prospect Theory [4][5]. In this paper, we hypothesize that intertemporal
choices may also be quantized. For example, people may treat (or discount) 16
days indifferently to 17 days. To test this, we analyzed an intertemporal task
by using 2 novel models: quantized hyperbolic discounting, and quantized
exponential discounting. Our work here is a re-examination of the behavioral
data previously collected for an fMRI study [8]. Both quantized hyperbolic and
quantized exponential models were compared using AIC and BIC tests. We found
that 13/20 participants were best fit to the quantized exponential model, while
the remaining 7/20 were best fit to the quantized hyperbolic model. Overall,
15/20 participants were best fit to models with a 5-bit precision (i.e., 2^5 =
32 steps). In conclusion, regardless of hyperbolic or exponential, quantized
versions of these models are better fit to the experimental data than their
continuous forms. We finally outline some potential applications of our
findings.
Lensless hyperspectral imaging by Fourier transform spectroscopy for broadband visible light: phase retrieval technique
Date: 2020-03-16 00:00:00
Tasks: Retrieval, Transparent objects

A novel phase retrieval algorithm for broadband hyperspectral phase imaging
from noisy intensity observations is proposed. It utilizes advantages of the
Fourier Transform spectroscopy in the self-referencing optical setup and
provides, additionally beyond spectral intensity distribution, reconstruction
of the investigated object's phase. The noise amplification Fellgett's
disadvantage is relaxed by the application of sparse wavefront noise filtering
embedded in the proposed algorithm. The algorithm reliability is proved by
simulation tests and results of physical experiments on transparent objects
which demonstrate precise phase imaging and object depth (profile)
reconstructions.
IRS-aided Large-Scale MIMO Systems with Passive Constant Envelope Precoding
Date: 2020-02-26 00:00:00
Tasks: Stochastic Optimization

In this paper, an intelligent reflecting surface (IRS)-aided large-scale MIMO
system is investigated in which constant envelope precoding (CEP) is utilized
at each base station (BS). It is both cost-effective and energy-efficient to
implement CEP in a large-scale antenna array. We aim to optimize the discrete
phase shifts at both the BS and the IRS to minimize the sum power of multi-user
interference (MUI) in the system via our proposed three algorithms. For the
sake of simplicity, a simple single-cell scenario is considered, where the
optimization of the BS and IRS phase shifts is solved by a low-complexity
trellis-based algorithm. Then, this algorithm is extended to a multi-cell
scenario, where the precoding operation in each BS is performed individually.
With the aid of stochastic optimization method, a low-overhead trellis-based
solution is proposed which has better performance than the first one. Finally,
we solve the optimization problem via the semi-definite relaxation (SDR)
scheme, to serve as a performance benchmark for the proposed algorithms.
Meanwhile, interference and complexity analysis is provided for the proposed
algorithms. Numerical results demonstrate that while the performance of the
trellis-based algorithms is negligibly lower than that of the continuous-phase
SDR-based solution, the computational complexity and the implementation cost of
the former is much lower than the latter, which is appealing for practical
applications.
On the propensity of Asn-Gly-containing heptapeptides to form $\beta$-turn structures : comparison between ab initio quantum mechanical calculations and Molecular Dynamics simulations
Date: 2020-02-27 00:00:00

Both molecular mechanical and quantum mechanical calculations play an
important role in describing the behavior and structure of molecules. In this
work, we compare for the same peptide systems the results obtained from folding
molecular dynamics simulations with previously reported results from quantum
mechanical calculations. More specifically, three molecular dynamics
simulations of 5 $\mu$s each in explicit water solvent were carried out for
three Asn-Gly-containing heptapeptides, in order to study their folding and
dynamics. Previous data, based on quantum mechanical calculations and the DFT
methods have shown that these peptides adopt $\beta$-turn structures in aqueous
solution, with type I' $\beta$-turn being the most preferred motif. The results
from our analyses indicate that for the given system the two methods diverge in
their predictions. The possibility of a force field-dependent deficiency is
examined as a possible source of the observed discrepancy.
Analysing Multibeam, Cooperative, Ground Based Radar in a Bistatic Configuration
Authors: Pepijn B. Cox, Wim L. van Rossum
Date: 2020-02-24 00:00:00

Recent advances in digital beam forming for phased arrays in combination with digital signal processing should enable the development of multibeam radar in a bistatic configuration. In the bistatic setting, the pulse travelling outward from the transmitter should be followed or "chased" by the receiver. During transmission, depending on the location of the transmitter, receiver, and pulse, the number of digital beams and their location at the transmitter vary. In this paper, we analyse the geometrically depending number of digital beams and the beam switching rate of the receiver needed for pulse chasing. In addition, we derive the pulse repetition frequency (PRF) for the bistatic configuration based on the desired detection range. It is shown that the PRF in the bistatic case can be increased compared to its monostatic counterpart when the distance between the transmitter and the receiver is increased. Our results are applied on the scenario of an air traffic control radar to show the feasibility of a multibeam, ground based bistatic surveillance radar. It will be demonstrated that the maximum PRF can almost be doubled and an adaptive sensing and tracking paradigm can lead to a maximum of 64 simultaneous receiver beams for the bistatic surveillance and tracking setting.