pwc-archive/papers-with-abstracts huggingface dataset:
Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map
Date: 2019-06-19 00:00:00
Tasks: Image Enhancement, Image Restoration, Underwater Image Restoration

Underwater images often have severe quality degradation and distortion due to
light absorption and scattering in the water medium. A hazed image formation
model is widely used to restore the image quality. It depends on two optical
parameters: the background light and the transmission map. Underwater images
can also be enhanced by color and contrast correction from the perspective of
image processing. In this paper, we propose an effective underwater image
enhancement method for underwater images in composition of underwater image
restoration and color correction. Firstly, a manually annotated background
lights (MABLs) database is developed. With reference to the relationship
between MABLs and the histogram distributions of various underwater images,
robust statistical models of BLs estimation are provided. Next, the TM of R
channel is roughly estimated based on the new underwater dark channel prior via
the statistic of clear and high resolution underwater images, then a scene
depth map based on the underwater light attenuation prior and an adjusted
reversed saturation map are applied to compensate and modify the coarse TM of R
channel. Next, TMs of G-B channels are estimated based on the difference of
attenuation ratios between R channel and G-B channels. Finally, to improve the
color and contrast of the restored image with a natural appearance, a variation
of white balance is introduced as post-processing. In order to guide the
priority of underwater image enhancement, sufficient evaluations are conducted
to discuss the impacts of the key parameters including BL and TM, and the
importance of the color correction. Comparisons with other state-of-the-art
methods demonstrate that our proposed underwater image enhancement method can
achieve higher accuracy of estimated BLs, less computation time, more superior
performance, and more valuable information retention.
IEEE 802.15.7-Compliant Ultra-low Latency Relaying VLC System for Safety-Critical ITS
Authors: Tassadaq Nawaz, Marco Seminara, Stefano Caputo, Lorenzo Mucchi, Francesco Cataliotti, Jacopo Catani
Date: 2019-06-20 00:00:00

The integration of Visible-Light Communications technology (VLC) in Intelligent Transportation Systems (ITS) is a very promising platform for a cost-effective implementation of revolutionary ITS and cooperative ITS protocols. In this paper, we propose an infrastructure-to-vehicle-to-vehicle (I2V2V) VLC system for ITS, implementing it through a regular LED traffic light serving as a transmitter and a digital Active Decode-and-Relay (ADR) stage for decoding and relaying the received information towards further incoming units. The proposed VLC system targets the challenging and important case of ultra-low latency ADR transmission of short packets, as this is needed for emerging applications of automatic braking, car platooning and other critical automatic and/or assisted driving applications. The experimental validation of the ADR VLC chain, as well as a thorough statistical analysis of errors distribution in the transmission, has been performed for short to medium distances, up to 50 meters. The performances of the designed system are evaluated by measuring the packet error rate (PER) and latency in the whole ADR transmission chain. Our analysis shows that our system attains ultra-low, sub-ms latencies at 99.9\% confidence level for PER as high as $5\times10^{-3}$, yet granting a latency below 10 ms even for distances of 50 m. The demonstrated system prototype is compatible with IEEE 802.15.7 standard.
Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach
Date: 2019-06-20 00:00:00
Tasks: Edge-computing, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A novel non-orthogonal multiple access (NOMA) based cache-aided mobile edge
computing (MEC) framework is proposed. For the purpose of efficiently
allocating communication and computation resources to users' computation tasks
requests, we propose a long-short-term memory (LSTM) network to predict the
task popularity. Based on the predicted task popularity, a long-term reward
maximization problem is formulated that involves a joint optimization of the
task offloading decisions, computation resource allocation, and caching
decisions. To tackle this challenging problem, a single-agent Q-learning
(SAQ-learning) algorithm is invoked to learn a long-term resource allocation
strategy. Furthermore, a Bayesian learning automata (BLA) based multi-agent
Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions.
More specifically, a BLA based action select scheme is proposed for the agents
in MAQ-learning to select the optimal action in every state. We prove that the
BLA based action selection scheme is instantaneously self-correcting and the
selected action is an optimal solution for each state. Extensive simulation
results demonstrate that: 1) The prediction error of the proposed LSTMs based
task popularity prediction decreases with increasing learning rate. 2) The
proposed framework significantly outperforms the benchmarks like all local
computing, all offloading computing, and non-cache computing. 3) The proposed
BLA based MAQ-learning achieves an improved performance compared to
conventional reinforcement learning algorithms.
A Signal Subspace Rotation Method for Localization of Multiple Wideband Sound Sources
Date: 2019-06-20 00:00:00
Tasks: Sound Source Localization

In this paper, the problem of extending narrowband multichannel sound source
localization algorithms to the wideband case is addressed. The DOA estimation
of narrowband algorithms is based on the estimate of inter-channel phase
differences (IPD) between microphones of the sound sources. A new method for
wideband sound source DOA estimation based on signal subspace rotation is
present. The proposed algorithm normalizes the narrowband signal statistics by
rotating the estimated signal subspace to the wideband counterpart in the
eigenvector domain. Then the wideband DOA estimate can be obtained by
estimating the normalized IPD from these wideband signal statistics. In
addition to requiring less computational complexity compared to repeating the
narrowband algorithms for all relevant frequencies of wideband signals, the
proposed method also does not require any additional prior knowledge. The
experimental results demonstrate the efficacy and the robustness of the
proposed method.
Macroscopic theorem of the portfolio optimization problem with a risk-free asset
Date: 2019-06-20 00:00:00
Tasks: Portfolio Optimization

The investment risk minimization problem with budget and return constraints
has been the subject of research using replica analysis but there are
shortcomings in the extant literature. With respect to Tobin's separation
theorem and the capital asset pricing model, it is necessary to investigate the
implications of a risk-free asset and examine its influence on the optimal
portfolio. Accordingly, in this work, we explore the investment risk
minimization problem in the presence of a risk-free asset with budget and
return constraints. Moreover, we discuss opportunity loss, the Pythagorean
theorem of the Sharpe ratio, and Tobin's separation theorem.
Cell Shape and Durotaxis Follow from Mechanical Cell-Substrate Reciprocity and Focal Adhesion Dynamics: A Unifying Mathematical Model
Authors: Elisabeth G. Rens, Roeland M. H. Merks
Date: 2019-06-21 00:00:00

Many animal cells change their shape depending on the stiffness of the substrate on which they are cultured: they assume small, rounded shapes in soft ECMs, they elongate within stiffer ECMs, and flatten out on hard substrates. Cells tend to prefer stiffer parts of the substrate, a phenomenon known as durotaxis. Such mechanosensitive responses to ECM mechanics are key to understanding the regulation of biological tissues by mechanical cues, as it occurs, e.g., during angiogenesis and the alignment of cells in muscles and tendons. Although it is well established that the mechanical cell-ECM interactions are mediated by focal adhesions, the mechanosensitive molecular complexes linking the cytoskeleton to the substrate, it is poorly understood how the stiffness-dependent kinetics of the focal adhesions eventually produce the observed interdependence of substrate stiffness and cell shape and cell behavior. Here we show that the mechanosensitive behavior of single-focal adhesions, cell contractility and substrate adhesivity together suffice to explain the observed stiffness-dependent behavior of cells. We introduce a multiscale computational model that is based upon the following assumptions: (1) cells apply forces onto the substrate through FAs; (2) the FAs grow and stabilize due to these forces; (3) within a given time-interval, the force that the FAs experience is lower on soft substrates than on stiffer substrates due to the time it takes to reach mechanical equilibrium; and (4) smaller FAs are pulled from the substrate more easily than larger FAs. Our model combines the cellular Potts model for the cells with a finite-element model for the substrate, and describes each FA using differential equations. Together these assumptions provide a unifying model for cell spreading, cell elongation and durotaxis in response to substrate mechanics.
Mirage: 2D Source Localization Using Microphone Pair Augmentation with Echoes
Date: 2019-06-21 00:00:00
Tasks: Sound Source Localization

It is commonly observed that acoustic echoes hurt performance of sound source
localization (SSL) methods. We introduce the concept of microphone array
augmentation with echoes (MIRAGE) and show how estimation of early-echo
characteristics can in fact benefit SSL. We propose a learning-based scheme for
echo estimation combined with a physics-based scheme for echo aggregation. In a
simple scenario involving 2 microphones close to a reflective surface and one
source, we show using simulated data that the proposed approach performs
similarly to a correlation-based method in azimuth estimation while retrieving
elevation as well from 2 microphones only, an impossible task in anechoic
settings.
BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability
Authors: Joshua Zoen Git Hiew, Xin Huang, Hao Mou, Duan Li, Qi Wu, Yabo Xu
Date: 2019-06-21 00:00:00
Tasks: Sentiment Analysis

Traditional sentiment construction in finance relies heavily on the dictionary-based approach, with a few exceptions using simple machine learning techniques such as Naive Bayes classifier. While the current literature has not yet invoked the rapid advancement in the natural language processing, we construct in this research a textual-based sentiment index using a well-known pre-trained model BERT developed by Google, especially for three actively trading individual stocks in Hong Kong market with at the same time the hot discussion on Weibo.com. On the one hand, we demonstrate a significant enhancement of applying BERT in financial sentiment analysis when compared with the existing models. On the other hand, by combining with the other two commonly-used methods when it comes to building the sentiment index in the financial literature, i.e., the option-implied and the market-implied approaches, we propose a more general and comprehensive framework for the financial sentiment analysis, and further provide convincing outcomes for the predictability of individual stock return by combining LSTM (with a feature of a nonlinear mapping). It is significantly distinct with the dominating econometric methods in sentiment influence analysis which are all of a nature of linear regression.
Capacity Enhancement of Cooperative NOMA over Rician Fading Channels with Orbital Angular Momentum
Date: 2019-06-21 00:00:00

This letter proposes the usage of orbital angular momentum (OAM) for
cooperative non-orthogonal multiple access (CNOMA) to enhance sum capacity (SC)
for the future cellular communication system. The proposed CNOMA-OAM scheme is
analyzed and compared with other schemes, i.e., conventional CNOMA,
conventional orthogonal multiple access (OMA) with OAM. The impact of the power
allocation factor for OAM beam over SC is also analyzed. The analytical result
is justified by simulation results which demonstrate that the proposed
CNOMA-OAM provides higher SC compared to other schemes.
Control of noise in gene expression by transcriptional reinitiation
Authors: Rajesh Karmakar
Date: 2019-06-21 00:00:00

Gene expression is a random or noisy process. The process consists of several random events among which the reinitiation of transcription by RNAP is an important one. The RNAP molecules can bind the gene only after the promoter gets activated by transcription factors. Several transcription factors bind the promoter to put the gene in the active state. The gene turns into inactive state as the bound transcription factors leave the promoter. During the active period of the gene, many RNAP molecules transcribe the gene to synthesize the mRNAs. The binding event of RNAP to the active state of the gene is a probabilistic process and therefore, introduces noise or fluctuations in the mRNA and protein levels. In this paper, we analytically calculate the Fano factor in mRNA and protein levels and also the probability distribution of mRNA numbers exactly with the binding event of RNAPs in gene transcription process. The analytically calculated expression of Fano factor of proteins shows excellent agreement with an experimental result. Then we show that the Fano factor in mRNA levels can be sub-Poissonian due to the reinitiation of transcription by RNAP and the mean mRNA level can be increased without increasing the Fano factor. Our study show that the Fano factor can also be reduced keeping mRNA levels fixed. We find that the reinitiation of transcription can behave as a fine-tuned control process to regulate the mRNA/protein level in the cell.
Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs
Date: 2019-06-19 00:00:00

Several algorithms in prior literature have been proposed which guarantee
consensus of normally behaving agents in a network that may contain
adversarially behaving agents. These algorithms guarantee that the consensus
value lies within the convex hull of initial normal agents' states, with the
exact consensus value possibly being unknown. In leader-follower consensus
problems however, the objective is for normally behaving agents to track a
reference state that may take on values outside of this convex hull. In this
paper we present methods for agents in time-varying graphs with discrete-time
dynamics to resiliently track a reference state propagated by a set of leaders
despite a bounded subset of the leaders and followers behaving adversarially.
Our results are demonstrated through simulations.
Applications of Backscatter Communications for Healthcare Networks
Date: 2019-06-21 00:00:00

Backscatter communication is expected to help in revitalizing the domain of
healthcare through its myriad applications. From on-body sensors to in-body
implants and miniature embeddable devices, there are many potential use cases
that can leverage the miniature and low-powered nature of backscatter devices.
However, the existing literature lacks a comprehensive study that provides a
distilled review of the latest studies on backscatter communications from the
healthcare perspective. Thus, with the objective to promote the utility of
backscatter communication in healthcare, this paper aims to identify specific
applications of backscatter systems. A detailed taxonomy of recent studies and
gap analysis for future research directions are provided in this work. Finally,
we conduct measurements at 590 MHz in different propagation environments with
the in-house designed backscatter device. The link budget results show the
promise of backscatter devices to communicate over large distances for indoor
environments which demonstrates its potential in the healthcare system.
Intrinsic noise, Delta-Notch signalling and delayed reactions promote sustained, coherent, synchronised oscillations in the presomitic mesoderm
Authors: Joseph W. Baron, Tobias Galla
Date: 2019-06-21 00:00:00

Using a stochastic individual-based modelling approach, we examine the role that Delta-Notch signalling plays in the regulation of a robust and reliable somite segmentation clock. We find that not only can Delta-Notch signalling synchronise noisy cycles of gene expression in adjacent cells in the presomitic mesoderm (as is known), but it can also amplify and increase the coherence of these cycles. We examine some of the shortcomings of deterministic approaches to modelling these cycles and demonstrate how intrinsic noise can play an active role in promoting sustained oscillations, giving rise to noise-induced quasi-cycles. Finally, we explore how translational/transcriptional delays can result in the cycles in neighbouring cells oscillating in anti-phase and we study how this effect relates to the propagation of noise-induced stochastic waves.
Enhancing the Physical Layer Security of Dual-functional Radar Communication Systems
Date: 2019-09-10 00:00:00

Dual-functional radar communication (DFRC) system has recently attracted
significant academic attentions as an enabling solution for realizing
radar-communication spectrum sharing. During the DFRC transmission, however,
the critical information could be leaked to the targets, which might be
potential eavesdroppers. Therefore, the physical layer security has to be taken
into consideration. In this paper, fractional programming (FP) problems are
formulated to minimize the signal-to-interference-plus-noise ratio (SINR) at
targets under the constraints for the SINR of legitimate users. By doing so,
the secrecy rate of communication can be guaranteed. We first assume that
communication CSI and the angle of the target are precisely known. After that,
problem is extended to the cases with uncertainty in the target's location,
which indicates that the target might appear in a certain angular interval.
Finally, numerical results have been provided to validate the effectiveness of
the proposed method showing that it is viable to guarantee both radar and
secrecy communication performances by using the techniques we propose.
Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia
Date: 2019-06-21 00:00:00

Dynamical brain state transitions are critical for flexible working memory
but the network mechanisms are incompletely understood. Here, we show that
working memory entails brainwide switching between activity states. The
stability of states relates to dopamine D1 receptor gene expression while state
transitions are influenced by D2 receptor expression and pharmacological
modulation. Schizophrenia patients show altered network control properties,
including a more diverse energy landscape and decreased stability of working
memory representations.
Underpinnings of User-Channel Allocation in Non-Orthogonal Multiple Access for 5G
Date: 2019-06-21 00:00:00

Non-orthogonal multiple access (NOMA) is a part of 5th generation (5G)
communication systems. This article presents the underpinnings and underlying
structures of the problem of NOMA user-channel allocation. Unlike the
heuristics for NOMA user-channel allocation, the presented results are
guaranteed to converge to a solution. In addition, the solutions are stable.
Generally, the results apply to any NOMA system. Unlike the orthogonal
frequency division multiple access (OFDMA) resource allocation problem, the
core matching is not the solution to NOMA resource allocation. The conditions
under which the fix-point NOMA resource allocation is guaranteed to be stable
from the viewpoint of both the base station and the NOMA users are described.
In addition, relationships of NOMA user-channel resource allocation to game
models and subgame perfect Nash equilibria are elucidated.
Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods
Date: 2019-06-21 00:00:00

With vast databases at their disposal, private tech companies can compete
with public statistical agencies to provide population statistics. However,
private companies face different incentives to provide high-quality statistics
and to protect the privacy of the people whose data are used. When both privacy
protection and statistical accuracy are public goods, private providers tend to
produce at least one suboptimally, but it is not clear which. We model a firm
that publishes statistics under a guarantee of differential privacy. We prove
that provision by the private firm results in inefficiently low data quality in
this framework.
Quantifying the Total Effect of Edge Interventions in Discrete Multistate Networks
Authors: David Murrugarra, Elena Dimitrova
Date: 2019-06-22 00:00:00

Developing efficient computational methods to assess the impact of external interventions on the dynamics of a network model is an important problem in systems biology. This paper focuses on quantifying the global changes that result from the application of an intervention to produce a desired effect, which we define as the total effect of the intervention. The type of mathematical models that we will consider are discrete dynamical systems which include the widely used Boolean networks and their generalizations. The potential interventions can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. We use a class of regulatory rules called nested canalizing functions that frequently appear in published models and were inspired by the concept of canalization in evolutionary biology. In this paper, we provide a polynomial normal form based on the canalizing properties of regulatory functions. Using this polynomial normal form, we give a set of formulas for counting the maximum number of transitions that will change in the state space upon an edge deletion in the wiring diagram. These formulas rely on the canalizing structure of the target function since the number of changed transitions depends on the canalizing layer that includes the input to be deleted. We also present computations on random networks to compare the exact number of changes with the upper bounds provided by our formulas. Finally, we provide statistics on the sharpness of these upper bounds in random networks.
Trajectory Generation for UAVs in Unknown Environments with Extreme Wind Disturbances
Date: 2019-06-22 00:00:00
Tasks: Collision Avoidance, Navigate

The widespread use of unmanned aerial vehicles (UAVs) by the military,
commercial companies, and academia continues to push research for autonomous
vehicle navigation, particularly in varying environmental conditions and
beyond-line-of-sight (BLOS) applications. This article addresses trajectory
generation for UAVs operating in extreme environments where the wind
disturbances may exceed the vehicle's closed-loop stability bounds. To do this,
a controller is developed that has two modes of operation: (1) normal mode, and
(2) drift mode. In the normal mode the vehicle's thrust and sensor limitations
are not exceeded by environmental conditions, whereas in the drift mode they
are. In the drift mode, a drift frame that moves with the prevailing wind is
established in which the vehicle maintains control authority to generate and
track trajectories. The vehicle maintains control authority by relaxing the
inertial frame trajectory tracking requirement and re-planning the trajectory
in the drift frame. Guarantees are established to ensure tracking of the
trajectory, collision avoidance, and respecting the vehicle thrust and sensor
limitations. Simulation results demonstrate the algorithm properties through
two scenarios. First, the performance of two quadrotors is compared where one
utilizes the drift mode and the other does not. Second, multiple vehicles
navigate through two narrow openings between protected and windy environments
to demonstrate on-board updates to navigation parameters based on environmental
conditions.