pwc-archive/papers-with-abstracts huggingface dataset:
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
Authors: Wankai Tang, Ming Zheng Chen, Xiangyu Chen, Jun Yan Dai, Yu Han, Marco Di Renzo, Yong Zeng, Shi Jin, Qiang Cheng, Tie Jun Cui
Date: 2019-11-13 00:00:00

Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this paper, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
How do scientific disciplines evolve in applied sciences? The properties of scientific fission and ambidextrous scientific drivers
Date: 2019-11-13 00:00:00

One of the fundamental questions in science is how scientific disciplines
evolve and sustain progress in society. No studies to date allows us to explain
the endogenous processes that support the evolution of scientific disciplines
and emergence of new scientific fields in applied sciences of physics. This
study confronts this problem here by investigating the evolution of
experimental physics to explain and generalize some characteristics of the
dynamics of applied sciences. Empirical analysis suggests properties about the
evolution of experimental physics and in general of applied sciences, such as:
a) scientific fission, the evolution of scientific disciplines generates a
process of division into two or more research fields that evolve as autonomous
entities over time; b) ambidextrous drivers of science, the evolution of
science via scientific fission is due to scientific discoveries or new
technologies; c) new driving research fields, the drivers of scientific
disciplines are new research fields rather than old ones; d) science driven by
development of general purpose technologies, the evolution of experimental
physics and applied sciences is due to the convergence of experimental and
theoretical branches of physics associated with the development of computer,
information systems and applied computational science. Results also reveal that
average duration of the upwave of scientific production in scientific fields
supporting experimental physics is about 80 years. Overall, then, this study
begins the process of clarifying and generalizing, as far as possible, some
characteristics of the evolutionary dynamics of scientific disciplines that can
lay a foundation for the development of comprehensive properties explaining the
evolution of science as a whole for supporting fruitful research policy
implications directed to advancement of science and technological progress in
society.
Utilization of e-Nose Sensory Modality as Add-On Feature for Advanced Driver Assistance System
Date: 2019-11-13 00:00:00

The frequent usage of sensory modalities for Advanced Driver Assistance
System (ADAS) and in some In-Vehicles Information System (IVIS) are only for
visuals and hearing applications. Yet, the air quality inside the car is not
usually monitored. Not to mention, the existing breath analyzers are used only
by road traffic police enforcer randomly in examining the alcohol content in
motorist's breath to avoid drunk driving related accidents. In this study, we
proposed the development and utilization of an e-nose sensory module as an
added feature for Controller Area Network (CAN) technology application. An
electronic nose is characterized by its capability to intelligently sensed the
gases present in the surrounding using an array of gas sensors with a pattern
reorganization component. This e-nose comprises of a sensor array of five
commercially available sensors (TG 2602, TG 822, TG 825, TG 813 and TG 880), a
data acquisition interface, and a microprocessor. A software from the CAN
module verified the system functionality. Experimental results indicate that
the proposed system is able to identify the five different types of gases
namely methane, ethanol, propone, isobutane, and hydrogen with high efficiency.
Thus, it can be used as an added safety feature for vehicles.
Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter's State of the Art
Authors: Caitríona M. Ryan, Andrew Parnell, Catherine Mahoney
Date: 2019-11-13 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

The detection of anomalies in real time is paramount to maintain performance and efficiency across a wide range of applications including web services and smart manufacturing. This paper presents a novel algorithm to detect anomalies in streaming time series data via statistical learning. We adapt the generalised extreme studentised deviate test [1] to streaming data by using a sliding window approach. This is made computationally feasible by recursive updates of the Grubbs test statistic [2]. Moreover, a priority queue [3] is employed to reduce memory requirements, where subsets of the required data streaming window are maintained in the algorithm rather than the full list. Our method is statistically principled. It is suitable for streaming data and it outperforms the AnomalyDetection software package, recently released by Twitter Inc. (Twitter) [4] and used by multiple teams at Twitter as their state of the art on a daily basis [5]. The methodology is demonstrated using an example of unlabelled data from the Twitter AnomalyDetection GitHub repository and using a real manufacturing example with labelled anomalies.
A Novel MDP Based Decision Support Framework to Restore Earthquake Damaged Distribution Systems
Date: 2019-11-13 00:00:00

Electric power network expanded rapidly in recent decades due of the
excessive need of electricity in every aspect of life, including critical
infrastructures such as medical services, and transportation and communication
systems. Natural disasters are one of the major reasons of electricity outage.
It is extremely important to restore electrical energy in the shortest time
possible after a disaster. This paper proposes a decision support method for
electric system operators to restore electricity to the critical loads in a
distribution system after an earthquake. The proposed method employs Markov
Decision Process to find the optimal restoration scheme based on the
Probability of Failure of critical structures determined by using the Peak
Ground Acceleration values recorded by observatories and earthquake research
centers during earthquakes.
Implicit Higher-Order Moment Matching Technique for Model Reduction of Quadratic-bilinear Systems
Date: 2019-11-13 00:00:00
Tasks: Form

We propose a projection based multi-moment matching method for model order
reduction of quadratic-bilinear systems. The goal is to construct a reduced
system that ensures higher-order moment matching for the multivariate transfer
functions appearing in the input-output representation of the nonlinear system.
An existing technique achieves this for the first two multivariate transfer
functions, in what is called the symmetric form of the multivariate transfer
functions. We extend this framework to an equivalent and simplified form, the
regular form, which allows us to show moment matching for the first three
multivariate transfer functions. Numerical results for three benchmark examples
of quadratic-bilinear systems show that the proposed framework exhibits better
performance with reduced computational cost in comparison to existing
techniques.
Mean-Field Transmission Power Control in Dense Networks
Date: 2020-08-20 00:00:00

We consider uplink power control in wireless communication when a large
number of users compete over the channel resources. The CDMA protocol, as a
supporting technology of 3G networks accommodating signal from different
sources over the code domain, represents the orthogonal multiple access (OMA)
techniques. With the development of 5G wireless networks, non-orthogonal
multiple access (NOMA) is introduced to improve the efficiency of channel
allocation. Our goal is to investigate whether the power-domain NOMA protocol
can introduce performance improvement when the users interact with each other
in a non-cooperative manner. It is compared with the CDMA protocol, where the
fierce competition among users jeopardizes the efficiency of channel usage. In
this work, we conduct analysis with an aggregative game model, and show the
existence and uniqueness of an equilibrium strategy. Next, we adopt the social
welfare of the population as the performance metric, which is the average
utility achieved by the user population. It is shown that under the
corresponding equilibrium strategies, NOMA outperforms CDMA by higher
efficiency of channel access for uplink communications.
Turning genome-wide association study findings into opportunities for drug repositioning
Date: 2019-11-11 00:00:00
Tasks: Drug Discovery

Drug development is a very costly and lengthy process, while repositioned or
repurposed drugs could be brought into clinical practice within a shorter
time-frame and at a much reduced cost. The past decade has observed a massive
growth in the amount of data from genome-wide association studies (GWAS). The
rich information contained in GWAS data has great potential to guide drug
discovery or repositioning. Here we provide an overview of different
computational approaches which employ GWAS data to guide drug repositioning.
These methods include selection of top candidate genes from GWAS as drug
targets, deducing drug candidates based on drug-drug and disease-disease
similarity, searching for reversed expression profiles between drugs and
diseases, pathway-based methods as well as repositioning based on analysis of
biological networks. Each method is illustrated with examples, and their
respective strengths and limitations are discussed. Finally we discussed
several areas for future research.
Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL
Date: 2019-11-13 00:00:00

Model-driven engineering (MDE) provides tools and methods for the
manipulation of formal models. In this letter, we leverage MDE for the
transformation of production system models into flat files that are understood
by general purpose planning tools and that enable the computation of plans,
i.e., sequences of production steps that are required to reach certain
production goals. These plans are then merged back into the production system
model, thus enriching the formalized production system knowledge.
Data Economy for Prosumers in a Smart Grid Ecosystem
Date: 2019-10-30 00:00:00
Tasks: energy management, Management

Smart grids technologies are enablers of new business models for domestic
consumers with local flexibility (generation, loads, storage) and where access
to data is a key requirement in the value stream. However, legislation on
personal data privacy and protection imposes the need to develop local models
for flexibility modeling and forecasting and exchange models instead of
personal data. This paper describes the functional architecture of an home
energy management system (HEMS) and its optimization functions. A set of
data-driven models, embedded in the HEMS, are discussed for improving renewable
energy forecasting skill and modeling multi-period flexibility of distributed
energy resources.
Enhanced Voice Post Processing Using Voice Decoder Guidance Indicators
Date: 2019-11-13 00:00:00
Tasks: Decoder

Voice enhancement and voice coding are imperative and important functions in
a voice-communication system. However, both functions are commonly treated
independently, even though both utilize similar features of the underlying
signals. Our proposal is to leverage information from one function to the
benefit of the other. Specifically, our proposed changes are focused on changes
to the voice enhancement at the downlink side and utilizing information of the
voice decoding. Preliminary results show that such an approach results in
improved quality. Additionally, suggestions are provided on future extensions
of the proposed concept.
A Study of Interference Distributions in Millimeter Wave Cellular Networks
Date: 2019-11-11 00:00:00

We study the distribution of the interference power in a millimeter wave
(mmWave) cellular network. Such interference is random and highly dependent on
the employed transmission technique, as well as the varying channel conditions
and the varying association between users and base stations. Traditional
networks at lower frequencies usually employ omnidirectional transmission which
creates an (almost) equal amount of interference in any direction. MmWave
networks, however, must employ directional beamforming transmission in order to
compensate for the high path loss in mmWave frequency bands. These directional
transmissions drastically change the network interference structure. We examine
the interference power distributions in an mmWave network employing beamforming
transmission under different user association schemes, and contrast with those
under omnidirectional transmission. Numerical results using an analytical
mmWave channel model and a measurement-based channel generator, NYUSIM, show
that beamforming not only reduces the amount of strong interference and hence
significantly enhances network throughput, but also user association can
considerably alter network interference and throughput structures.
Deep Learning Decoding of Mental State in Non-invasive Brain Computer Interface
Date: 2019-11-11 00:00:00
Tasks: Brain Computer Interface, EEG, Eeg Decoding, Electroencephalogram (EEG), Prediction

Brain computer interface (BCI) has been popular as a key approach to monitor
our brains recent year. Mental states monitoring is one of the most important
BCI applications and becomes increasingly accessible. However, the mental state
prediction accuracy and generality through encephalogram (EEG) are not good
enough for everyday use. Here in this paper we present a deep learning-based
EEG decoding method to read mental states. We propose a novel 1D convolutional
neural network with different filter lengths to capture different frequency
bands information. To improve the prediction accuracy, we also used a
resnet-like structure to train a relatively deep convolutional neural network
to promote feature extraction. Compared with traditional ways of predictions
such as KNN and SVM, we achieved a significantly better result with an accuracy
of 96.40%. Also, in contrast with some already published open source deep
neural network structures, our methods achieved the state of art prediction
accuracy on a mental state recognition dataset. Our results demonstrate using
only 1D convolution could extract the features of EEG and the possibility of
mental state prediction using portable EEG devices.
Pricing Multi-Interval Dispatch under Uncertainty Part I: Dispatch-Following Incentives
Authors: Ye Guo, Cong Chen, Lang Tong
Date: 2019-11-13 00:00:00

Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives of profit-maximizing generators and shows that, under mild conditions, no uniform-pricing scheme for the rolling-window economic dispatch provides dispatch-following incentives that avoid discriminative out-of-the-market uplifts. A nonuniform pricing mechanism, referred to as the temporal locational marginal pricing (TLMP), is proposed. As an extension of the standard locational marginal pricing (LMP), TLMP takes into account both generation and ramping-induced opportunity costs. It eliminates the need for the out-of-the-market uplifts and guarantees full dispatch-following incentives regardless of the accuracy of the demand forecasts used in the dispatch. It is also shown that, under TLMP, a price-taking market participant has incentives to bid truthfully with its marginal cost of generation. Part II of the paper extends the theoretical results developed in Part I to more general network settings. It investigates a broader set of performance measures, including the incentives of the truthful revelation of ramping limits, revenue adequacy of the operator, consumer payments, generator profits, and price volatility under the rolling-window dispatch model with demand forecast errors.
Econophysics deserves a revamping
Authors: Paolo Magrassi
Date: 2019-11-08 00:00:00

The paper argues that attracting more economists and adopting a more-precise definition of dynamic complexity might help econophysics acquire more attention in the economics community and bring new lymph to economic research. It may be necessary to concentrate less on the applications than on the basics of economic complexity, beginning with expansion and deepening of the study of small systems with few interacting components, while until thus far complexity has been assumed to be a prerogative of complicated systems only. It is possible that without a thorough analysis at that level, the understanding of systems that are at the same time complex and complicated will continue to elude economics and econophysics research altogether. To that purpose, the paper initiates and frames a definition of dynamic complexity grounded on the concept of non-linear dynamical system.
Impulse Response Invariant Discretization of Complex Fractional Order Integrator
Date: 2019-11-18 00:00:00

When the order of an integrator is a complex number, the integrator is called
a complex fractional order integrator (CFOI). The impulse response invariant
discretization (IRID) method is proposed to approximately discretize the CFOI.
The definition of the CFOI is introduced firstly, and the real and imaginary
parts of the CFOI in frequency-domain responses are derived. The code of IRID
for the CFOI based on the MATLAB language is explained. The comparisons of the
impulse responses and frequency-domain responses between the CFOI and the
approximate discrete/continuous transfer functions are presented to illustrate
the effectiveness and correctness of the proposed discretization method. This
paper offers a reliable method to implement the CFOI.
Pleiotropy enables specific and accurate signaling in the presence of ligand cross talk
Authors: Duncan Kirby, Jeremy Rothschild, Matthew Smart, Anton Zilman
Date: 2019-11-14 00:00:00
Tasks: Specificity

Living cells sense their environment through the binding of extra-cellular molecular ligands to cell surface receptors. Puzzlingly, vast numbers of signaling pathways exhibit a high degree of cross talk between different signals whereby different ligands act through the same receptor or shared components downstream. It remains unclear how a cell can accurately process information from the environment in such cross-wired pathways. We show that a feature which commonly accompanies cross talk - signaling pleiotropy (the ability of a receptor to produce multiple outputs) - offers a solution to the cross talk problem. In a minimal model we show that a single pleiotropic receptor can simultaneously identify and accurately sense the concentrations of arbitrary unknown ligands present individually or in a mixture. We calculate the fundamental limits of the signaling specificity and accuracy of such signaling schemes. The model serves as an elementary "building block" towards understanding more complex cross-wired receptor-ligand signaling networks.
An Accelerated Nonlinear Contrast Source Inversion Scheme For Sparse Electromagnetic Imaging
Authors: Ali I. Sandhu, Abdulla Desmal, Hakan Bagci
Date: 2019-11-14 00:00:00

An efficient nonlinear contrast source inversion scheme for electromagnetic imaging of sparse two-dimensional investigation domains is proposed. To avoid generating a sequence of linear sparse optimization problems, the non-linearity is directly tackled using the nonlinear Landweber (NLW) iterations. A self-adaptive projected accelerated steepest descent (A-PASD) algorithm is incorporated to enhance the efficiency of the NLW iterations. The algorithm enforces the sparsity constraint by projecting the result of each steepest descent iteration into the L1-norm ball and selects the largest-possible iteration step without sacrificing from convergence. Numerical results, which demonstrate the proposed schemes accuracy, efficiency, and applicability, are presented.
The Controller Generated from Noise Can Be Lyapunuov Stable: A Controller Stabilized Method
Date: 2019-11-14 00:00:00

Aiming at the difficulty of stability analysis in practical application of
existing control methods, a controller strategy based on lyapunov stability
theory is proposed to realize stable control for any control method. In order
to illustrate the effectiveness of this control scheme, this paper lists two
different lyapunov functions to test the control effect of different system
models in tracking the target function. The simulation results show that the
proposed method has excellent control effect on different types of systems and
is advanced.
Task-dependence in scene perception: Head unrestrained viewing using mobile eye-tracking
Date: 2019-12-13 00:00:00

Real-world scene perception is typically studied in the laboratory using
static picture viewing with restrained head position. Consequently, the
transfer of results obtained in this paradigm to real-word scenarios has been
questioned. The advancement of mobile eye-trackers and the progress in image
processing, however, permit a more natural experimental setup that, at the same
time, maintains the high experimental control from the standard laboratory
setting. We investigated eye movements while participants were standing in
front of a projector screen and explored images under four specific task
instructions. Eye movements were recorded with a mobile eye-tracking device and
raw gaze data was transformed from head-centered into image-centered
coordinates. We observed differences between tasks in temporal and spatial
eye-movement parameters and found that the bias to fixate images near the
center differed between tasks. Our results demonstrate that current mobile
eye-tracking technology and a highly controlled design support the study of
fine-scaled task dependencies in an experimental setting that permits more
natural viewing behavior than the static picture viewing paradigm.