pwc-archive/papers-with-abstracts huggingface dataset:
Neural-net-induced Gaussian process regression for function approximation and PDE solution
Authors: Pang Guofei, Yang Liu, Karniadakis George Em
Date: 2018-06-22 00:00:00
Tasks: Gaussian Processes, regression, Uncertainty Quantification

Neural-net-induced Gaussian process (NNGP) regression inherits both the high
expressivity of deep neural networks (deep NNs) as well as the uncertainty
quantification property of Gaussian processes (GPs). We generalize the current
NNGP to first include a larger number of hyperparameters and subsequently train
the model by maximum likelihood estimation. Unlike previous works on NNGP that
targeted classification, here we apply the generalized NNGP to function
approximation and to solving partial differential equations (PDEs).
Specifically, we develop an analytical iteration formula to compute the
covariance function of GP induced by deep NN with an error-function
nonlinearity. We compare the performance of the generalized NNGP for function
approximations and PDE solutions with those of GPs and fully-connected NNs. We
observe that for smooth functions the generalized NNGP can yield the same order
of accuracy with GP, while both NNGP and GP outperform deep NN. For non-smooth
functions, the generalized NNGP is superior to GP and comparable or superior to
deep NN.
Robust Algorithms for Localizing Moving Nodes in Wireless Sensor Networks
Date: 2018-07-20 00:00:00

The vivid success of the emerging wireless sensor technology (WSN) gave rise
to the notion of localization in the communications field. Indeed, the interest
in localization grew further with the proliferation of the wireless sensor
network applications including medicine, military as well as transport. By
utilizing a subset of sensor terminals, gathered data in a WSN can be both
identified and correlated which helps in managing the nodes distributed
throughout the network. In most scenarios presented in the literature, the
nodes to be localized are often considered static. However, as we are heading
towards the 5th generation mobile communication, the aspect of mobility should
be regarded. Thus, the novelty of this research relies in its ability to merge
the robotics as well as WSN fields creating a state of art for the localization
of moving nodes. The challenging aspect relies in the capability of merging
these two platforms in a way where the limitations of each is minimized as much
as possible. A hybrid technique which combines both the Particle Filter (PF)
method and the Time Difference of Arrival Technique (TDOA) is presented.
Simulation results indicate that the proposed approach outperforms other
techniques in terms of accuracy and robustness.
In Vivo WBAN Communication: Design and Implementation
Date: 2018-07-20 00:00:00

The emerging in vivo communication and networking system is a prospective
component in advancing healthcare delivery and empowering the development of
new applications and services. In vivo communications is based on networked
cyber-physical systems of embedded devices to allow rapid, correct and cost-
effective responses under various conditions. This chapter presents the
existing research which investigates the state of art of the in vivo
communication. It focuses on characterizing and modeling the in vivo wireless
channel and contrasting it with the other familiar channels. MIMO in vivo is
also of cencern in this chapter since it significantly enhances the performance
gain and data rates. Furthermore, this chapter addresses in vivo
nano-communication which is presented for medical applications to provide fast
and accurate disease diagnosis and treatment. Such communication paradigm is
capable of operating inside the human body in real time and will be of great
benefit for medical monitoring and medical implant communications.
Consequently, propagation at the Terahertz (THz) frequency must be well
understood as it is considered the most promising band for electromagnetic
nano-communication models.
A 28/37/39GHz Multiband Linear Doherty Power Amplifier in Silicon for 5G Applications
Date: 2019-05-12 00:00:00

This paper presents the first multiband mm-wave linear Doherty PA in silicon
for broadband 5G applications. We introduce a new transformer-based on-chip
Doherty power combiner, which can reduce the impedance transformation ratio in
power back-off (PBO) and thus improve the bandwidth and power-combining
efficiency. We also devise a "driver-PA co-design" method, which creates
power-dependent uneven feeding in the Doherty PA and enhances the Doherty
operation without any hardware overhead or bandwidth compromise. For the proof
of concept, we implement a 28/37/39-GHz PA fully integrated in a standard
130-nm SiGe BiCMOS process, which occupies 1.8mm2. The PA achieves a 52% -3-dB
small-signal S21 bandwidth and a 40% -1-dB large-signal saturated output power
(Psat) bandwidth. At 28/37/39GHz, the PA achieves +16.8/+17.1/+17-dBm Psat,
+15.2/+15.5/+15.4-dBm P1dB, and superior 1.72/1.92/1.62 times efficiency
enhancement over class-B operation at 5.9/6/6.7-dB PBO. Moreover, the PA
demonstrates multi-Gb/s data rates with excellent efficiency and linearity for
64QAM in all the three 5G bands. This PA advances the state of the art for
Doherty, wideband, and 5G silicon PAs in mm-wave bands. It supports drop-in
upgrade for current PAs in existing mm-wave systems and opens doors to compact
system solutions for future multiband 5G massive MIMO and phased-array
platforms.
Availability and Reliability of Wireless Links in 5G Systems: A Space-Time Approach
Date: 2018-06-29 00:00:00

Wireless links are characterized by fluctuating quality leading to variable
packet error rates which are orders of magnitude higher than the ones of wired
links. Therefore, it is of paramount importance to investigate the limitations
of using 5G wireless links for internet of things (IoT) applications. 5G
wireless links in IoT need to assure determinism of process flows via realtime
communication anytime and anywhere, which is an utmost requirement for multiple
verticals like automotive, industrial automation and aerospace. Based on a
space-time approach, in this work, we provide novel definitions of wireless
link availability and reliability, assuming a number of access points (APs) and
end points (EPs) deployed over a fixed area. Our objective is to analyze the
availability of a service in both domains. In the space domain, we characterize
spatially available areas consisting of all locations that meet a performance
requirement with confidence. In the time domain, we propose a channel
allocation scheme accounting for the spatial availability of a cell. To
emphasize the incurred space-time performance trade-offs, numerical results are
presented, also highlighting the effect of different system parameters on the
achievable link availability and reliability.
On the Potential of Multi-Mode Antennas for Direction-of-Arrival Estimation
Date: 2019-01-25 00:00:00
Tasks: Direction of Arrival Estimation

In this paper, we show that a multi-mode antenna (MMA) is an interesting
alternative to a conventional phased antenna array for direction-of-arrival
(DoA) estimation. By MMA we mean a single physical radiator with multiple
ports, which excite different characteristic modes. In contrast to phased
arrays, a closed-form mathematical model of the antenna response, like a
steering vector, is not straightforward to define for MMAs. Instead one has to
rely on calibration measurement or electromagnetic field (EMF) simulation data,
which is discrete. To perform DoA estimation, array interpolation technique
(AIT) and wavefield modeling (WM) are suggested as methods with inherent
interpolation capabilities, fully taking antenna nonidealities like mutual
coupling into account. We present a non-coherent DoA estimator for low-cost
receivers and show how coherent DoA estimation and joint DoA and polarization
estimation can be performed with MMAs. Utilizing these methods, we assess the
DoA estimation performance of an MMA prototype in simulations for both 2D and
3D cases. The results show that WM outperforms AIT for high SNR. Coherent
estimation is superior to non-coherent, especially in 3D, because non-coherent
suffers from estimation ambiguities. In conclusion, DoA estimation with a
single MMA is feasible and accurate.
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data
Authors: Singh Jaspreet, Anand Avishek
Date: 2018-06-29 00:00:00
Tasks: Decision Making, Learning-To-Rank

Predictive models are omnipresent in automated and assisted decision making
scenarios. But for the most part they are used as black boxes which output a
prediction without understanding partially or even completely how different
features influence the model prediction avoiding algorithmic transparency.
Rankings are ordering over items encoding implicit comparisons typically
learned using a family of features using learning-to-rank models. In this paper
we focus on how best we can understand the decisions made by a ranker in a
post-hoc model agnostic manner. We operate on the notion of interpretability
based on explainability of rankings over an interpretable feature space.
Furthermore we train a tree based model (inherently interpretable) using labels
from the ranker, called secondary training data to provide explanations.
Consequently, we attempt to study how well does a subset of features,
potentially interpretable, explain the full model under different training
sizes and algorithms. We do experiments on the learning to rank datasets with
30k queries and report results that serve show in certain settings we can learn
a faithful interpretable ranker.
Herding behavior in cryptocurrency markets
Date: 2018-11-30 00:00:00

There are no solid arguments to sustain that digital currencies are the
future of online payments or the disruptive technology that some of its former
participants declared when used to face critiques. This paper aims to solve the
cryptocurrency puzzle from a behavioral finance perspective by finding the
parallelism between biases present in financial markets that could be applied
to cryptomarkets. Moreover, it is suggested that cryptocurrencies' prices are
driven by herding, hence this study test herding behavior under asymmetric and
symmetric conditions and the existence of different herding regimes by
employing the Markov-Switching approach.
Develop machine learning based predictive models for engineering protein solubility
Date: 2018-07-20 00:00:00
Tasks: BIG-bench Machine Learning

Protein activity is a significant characteristic for recombinant proteins
which can be used as biocatalysts. High activity of proteins reduces the cost
of biocatalysts. A model that can predict protein activity from amino acid
sequence is highly desired, as it aids experimental improvement of proteins.
However, only limited data for protein activity are currently available, which
prevents the development of such models. Since protein activity and solubility
are correlated for some proteins, the publicly available solubility dataset may
be adopted to develop models that can predict protein solubility from sequence.
The models could serve as a tool to indirectly predict protein activity from
sequence. In literature, predicting protein solubility from sequence has been
intensively explored, but the predicted solubility represented in binary values
from all the developed models was not suitable for guiding experimental designs
to improve protein solubility. Here we propose new machine learning models for
improving protein solubility in vivo. We first implemented a novel approach
that predicted protein solubility in continuous numerical values instead of
binary ones. After combing it with various machine learning algorithms, we
achieved a prediction accuracy of 76.28 percent when Support Vector Machine
algorithm was used. Continuous values of solubility are more meaningful in
protein engineering, as they enable researchers to choose proteins with higher
predicted solubility for experimental validation, while binary values fail to
distinguish proteins with the same value. There are only two possible values so
many proteins have the same one.
Socio-economic constraints to maximum human lifespan
Date: 2018-06-29 00:00:00
Tasks: Decision Making

The analysis of the demographic transition of the past century and a half,
using both empirical data and mathematical models, has rendered a wealth of
well-established facts, including the dramatic increases in life expectancy.
Despite these insights, such analyses have also occasionally triggered debates
which spill over many disciplines, from genetics, to biology, or demography.
Perhaps the hottest discussion is happening around the question of maximum
human lifespan, which --besides its fascinating historical and philosophical
interest-- poses urgent pragmatic warnings on a number of issues in public and
private decision-making. In this paper, we add to the controversy some results
which, based on purely statistical grounds, suggest that the maximum human
lifespan is not fixed, or has not reached yet a plateau. Quite the contrary,
analysis on reliable data for over 150 years in more than 20 industrialized
countries point at a sustained increase in the maximum age at death.
Furthermore, were this trend to continue, a limitless lifespan could be
achieved by 2102. Finally, we quantify the dependence of increases in the
maximum lifespan on socio-economic factors. Our analysis indicates that in some
countries the observed rising patterns can only be sustained by progressively
larger increases in GDP, setting the problem of longevity in a context of
diminishing returns.
A Probabilistic Modeling Approach to One-Shot Gesture Recognition
Date: 2020-02-17 00:00:00
Tasks: Gesture Recognition

Gesture recognition enables a natural extension of the way we currently
interact with devices. Commercially available gesture recognition systems are
usually pre-trained and offer no option for customization by the user. In order
to improve the user experience, it is desirable to allow end users to define
their own gestures. This scenario requires learning from just a few training
examples if we want to impose only a light training load on the user. To this
end, we propose a gesture classifier based on a hierarchical probabilistic
modeling approach. In this framework, high-level features that are shared among
different gestures can be extracted from a large labeled data set, yielding a
prior distribution for gestures. When learning new types of gestures, the
learned shared prior reduces the number of required training examples for
individual gestures. We implemented the proposed gesture classifier for a Myo
sensor bracelet and show favorable results for the tested system on a database
of 17 different gesture types. Furthermore, we propose and implement two
methods to incorporate the gesture classifier in a real-time gesture
recognition system.
Footwear Size Recommendation System
Authors: Singh Shreya, Abdulla G Mohammed, Borar Sumit, Arora Sagar
Date: 2018-06-28 00:00:00

While shopping for fashion products, customers usually prefer to try-out
products to examine fit, material, overall look and feel. Due to lack of try
out options during online shopping, it becomes pivotal to provide customers
with as much of this information as possible to enhance their shopping
experience. Also it becomes essential to provide same experience for new
customers. Our work here focuses on providing a production ready size
recommendation system for shoes and address the challenge of providing
recommendation for users with no previous purchases on the platform. In our
work, we present a probabilistic approach based on user co-purchase data
facilitated by generating a brand-brand relationship graph. Specifically we
address two challenges that are commonly faced while implementing such
solution. 1. Sparse signals for less popular or new products in the system 2.
Extending the solution for new users. Further we compare and contrast this
approach with our previous work and show significant improvement both in
recommendation precision and coverage.
High-Performance Parallel Implementation of Genetic Algorithm on FPGA
Authors: Torquato Matheus F., Fernandes Marcelo A. C.
Date: 2018-06-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Genetic Algorithms (GAs) are used to solve search and optimization problems
in which an optimal solution can be found using an iterative process with
probabilistic and non-deterministic transitions. However, depending on the
problem's nature, the time required to find a solution can be high in
sequential machines due to the computational complexity of genetic algorithms.
This work proposes a parallel implementation of a genetic algorithm on
field-programmable gate array (FPGA). Optimization of the system's processing
time is the main goal of this project. Results associated with the processing
time and area occupancy (on FPGA) for various population sizes are analyzed.
Studies concerning the accuracy of the GA response for the optimization of two
variables functions were also evaluated for the hardware implementation.
However, the high-performance implementation proposes in this paper is able to
work with more variable from some adjustments on hardware architecture.
Mineralogical Characteristics of Harmattan Dust Across Jos North Central and Potiskum North Earthern Cities of Nigeria
Authors: O. A. Falaiye, F. O Aweda
Date: 2018-06-19 00:00:00

The trace metals and mineralogical composition of harmattan dust carried out on the samples collected at Jos ((9 55'N, 8 55'E)) and Potiskum ((11 43'N, 11 02'E) as revealed by PIXE and AAS machine using clean Petri Dishes and Plastic bowls of 10 cm in diameter aimed on the characteristics of the mineralogical and elemental composition of the harmattan dust carried out in Nigeria. Thirteen trace elements, Na, K, Ca, Mg, Fe, Cd, Zn, Mn, Cu, Si, Al, Ti, and Zr were determined and their concentrations were evaluated in different proportion. Minerals such as Quartz [SiO2], Corundum [Al2O3], Hematite [Fe2O3], Lime [CaO], Periclase [MgO], Rutile [TiO2], Zincite [MnO], Bunsenite [NiO], Cuprite [Cu2O], Zincite [ZnO], Baddeleyite [ZrO2], Litharge [PbO], Monazite [P2O5], Montrodydite [HgO] and Petzite [Au2O3] were also determined in different concentrations. The particle weight of the sample for the residential and commercial areas were calculated to be Jos (18.95g/m2, 19.25g/m2), Potiskum (24.24 g/m2, 2515g/m2) respectively. The results shows that the harmattan dust that blows across the two stations in Nigeria comprise of high elements and more minerals.
Introduction to the Special Issue on Approaches to Control Biological and Biologically Inspired Networks
Date: 2018-06-28 00:00:00

The emerging field at the intersection of quantitative biology, network
modeling, and control theory has enjoyed significant progress in recent years.
This Special Issue brings together a selection of papers on complementary
approaches to observe, identify, and control biological and biologically
inspired networks. These approaches advance the state of the art in the field
by addressing challenges common to many such networks, including high
dimensionality, strong nonlinearity, uncertainty, and limited opportunities for
observation and intervention. Because these challenges are not unique to
biological systems, it is expected that many of the results presented in these
contributions will also find applications in other domains, including physical,
social, and technological networks.
Nonnegative linear elimination for chemical reaction networks
Date: 2018-06-29 00:00:00

We consider linear elimination of variables in steady state equations of a
chemical reaction network. Particular subsets of variables corresponding to
sets of so-called reactant-noninteracting species, are introduced. The steady
state equations for the variables in such a set, taken together with potential
linear conservation laws in the variables, define a linear system of equations.
We give conditions that guarantee that the solution to this system is
nonnegative, provided it is unique. The results are framed in terms of spanning
forests of a particular multidigraph derived from the reaction network and
thereby conditions for uniqueness and nonnegativity of a solution are derived
by means of the multidigraph. Though our motivation comes from applications in
systems biology, the results have general applicability in applied sciences.
Over-the-Air Time Synchronization for URLLC: Requirements, Challenges and Possible Enablers
Date: 2018-06-29 00:00:00

Ultra-reliable and low-latency communications (URLLC) is an emerging feature
in 5G and beyond wireless systems, which is introduced to support stringent
latency and reliability requirements of mission-critical industrial
applications. In many potential applications, multiple sensors/actuators
collaborate and require isochronous operation with strict and bounded jitter,
e.g., \SI{1}{\micro\second}. To this end, network time synchronization becomes
crucial for real-time and isochronous communication between a controller and
the sensors/actuators. In this paper, we look at different applications in
factory automation and smart grids to reveal the requirements of device-level
time synchronization and the challenges in extending the high-granularity
timing information to the devices. Also, we identify the potential over-the-air
synchronization mechanisms in 5G radio interface, and discuss the needed
enhancements to meet the jitter constraints of time-sensitive URLLC
applications.
Spontaneous symmetry breaking and pattern formation of organoids
Date: 2018-08-28 00:00:00

Recent 3D organ reconstitution studies show that a group of stem cells can
establish a body axis and acquire different fates in a spatially organized
manner. How such symmetry breaking happens in the absence of external spatial
cues, and how developmental circuits are built to permit this is largely
unknown. Here, we review spontaneous symmetry breaking phenomena in organoids,
and hypothesize underlying patterning mechanisms that involve interacting
diffusible species. Recent theoretical advances offer new directions beyond the
prototypical Turing model. Experiments guided by theory will allow us to
predict and control organoid self-organization.
On the scale-free nature of RNA secondary structure networks
Date: 2018-06-30 00:00:00
Tasks: Protein Folding

A network is scale-free if its connectivity density function is proportional
to a power-law distribution. Scale-free networks may provide an explanation for
the robustness observed in certain physical and biological phenomena, since the
presence of a few highly connected hub nodes and a large number of small-
degree nodes may provide alternate paths between any two nodes on average --
such robustness has been suggested in studies of metabolic networks, gene
interaction networks and protein folding. A theoretical justification for why
biological networks are often found to be scale-free may lie in the well-known
fact that expanding networks in which new nodes are preferentially attached to
highly connected nodes tend to be scale-free. In this paper, we provide the
first efficient algorithm to compute the connectivity density function for the
ensemble of all secondary structures of a user-specified length, and show both
by computational and theoretical arguments that preferential attachment holds
when expanding the network from length n to length n + 1 structures. Since
existent power-law fitting software, such as powerlaw, cannot be used to
determine a power-law fit for our exponentially large RNA connectivity data, we
also implement efficient code to compute the maximum likelihood estimate for
the power-law scaling factor and associated Kolmogorov-Smirnov p-value.
Statistical goodness-of-fit tests indicate that one must reject the hypothesis
that RNA connectivity data follows a power-law distribution. Nevertheless, the
power-law fit is visually a good approximation for the tail of connectivity
data, and provides a rationale for investigation of preferential attachment in
the context of macromolecular folding.