pwc-archive/papers-with-abstracts huggingface dataset:
Demonstration of 3D ISAR Security Imaging at 24GHz with a Sparse MIMO Array
Date: 2019-08-19 00:00:00
Tasks: GPU, Object Recognition

A 3D ISAR security imaging experiment at 24GHz is demonstrated with a sparse
MIMO array. The MIMO array is an 8Tx/16Rx linear array to achieve real-aperture
imaging along the vertical dimension. It is time-switching multiplexed with a
low-cost FMCW transceiver working at 22GHz-26GHz. A calibration procedure is
proposed to calibrate the channel imbalance across the MIMO array. The
experiment is conducted on human moving on a cart, where we take advantage of
the linear motion of human to form inverse synthetic aperture along the
horizontal dimension. To track the motion of human, a 3D depth camera is used
as an auxiliary sensor to capture the rough position of target to aid ISAR
imaging. The back projection imaging algorithm is implemented on GPU for
quasi-real-time operation. Finally, experiments are conducted with real human
with concealed objects and a preliminary automatic object recognition algorithm
based on convolutional neural networks are developed and evaluated on real
data.
A nonequilibrium strategy for fast target search on the genome
Authors: F. Cagnetta, D. Michieletto, D. Marenduzzo
Date: 2019-08-19 00:00:00

Vital biological processes such as genome repair require fast and efficient binding of selected proteins to specific target sites on DNA. Here we propose an active target search mechanism based on "chromophoresis", the dynamics of DNA-binding proteins up or down gradients in the density of epigenetic marks, or colours (biochemical tags on the genome). We focus on a set of proteins that deposit marks from which they are repelled---a case which is only encountered away from thermodynamic equilibrium. For suitable ranges of kinetic parameter values, chromophoretic proteins can perform unidirectional motion and are optimally redistributed along the genome. Importantly, they can also locally unravel a region of the genome which is collapsed due to self-interactions and "dive" deep into its core, for a striking enhancement of the efficiency of target search on such an inaccessible substrate. We discuss the potential relevance of chromophoresis for the location of DNA lesions.
Employing Game Theory and TDMA Protocol to Enhance Security and Manage Power Consumption in WSNs-based Cognitive Radio
Date: 2019-08-12 00:00:00

The rapid development of wireless sensor networks (WSNs) is the significant
incentive to contribute in the vulnerable applications such as cognitive radio
(CR). This paper proposes a Stackelberg game approach to enhance the WSN-based
CR security against the spectrum sensing data falsification (SSDF) attack and
conserve the consequent lost power consumption. The attack aims to corrupt the
spectrum decision by imposing interference power to the delivered reports from
the sensor nodes (SNs) to the fusion center (FC) to make a protection level
below a specific threshold. The proposed model utilizes the intelligent
Stackelberg game features along with the matched filter (MF) to maximize the
number of protected reports sent by the SNs to the FC leading to accurate
decision of the spectrum status. Furthermore, the TDMA protocol is utilized to
resolve the complexity of employing MF for the spectrum detection to avoid the
collision between the delivered reports. The proposed model aims to enhance the
number of correctly received reports at the FC, and hence manage the lost
energy of reports retransmission due to the malicious attack effect. Moreover,
the model can conserve the lost power of the failure communication attempts due
to the SSDF attack impact. Simulation results indicate the improved performance
of the proposed protection model along with the MF over the six different
environments against the SSDF attack as compared to two defense schemes,
namely, random and equal weight defense strategies.
Radio Resource Management for V2V Multihop Communication Considering Adjacent Channel Interference
Date: 2019-08-12 00:00:00
Tasks: Fairness, Management, Scheduling

This paper investigate joint scheduling and power control for V2V multicast
allowing multihop communication. The effects of both co-channel interference
and adjacent channel interference are considered. First, we solve the problem
with the objective of maximizing the throughput and connectivity of vehicles in
the network. Then extend the same problem formulation to include the objective
of minimizing the latency and the average age of information (AoI), which is
the age of the latest received message. In order to account for fairness, we
also show the problem formulation to maximize the worst-case throughput and
connectivity. All the problems are formulated as mixed Boolean linear
programming problems, which allows computation of optimal solutions.
Furthermore, we consider the error probability of a link failure in all the
problem formulations and accommodate the probability requirements for
satisfying a certain throughput/connectivity/latency/AoI. In order to support a
large V2V network, a clustering algorithm is proposed whose computational
complexity scale well with the network size. To handle the case of zero channel
information at the scheduler, a multihop distributed scheduling scheme is
proposed.
A Modified Ising Model of Barab\'asi-Albert Network with Gene-type Spins
Date: 2019-08-19 00:00:00

The central question of systems biology is to understand how individual
components of a biological system such as genes or proteins cooperate in
emerging phenotypes resulting in the evolution of diseases. As living cells are
open systems in quasi-steady state type equilibrium in continuous exchange with
their environment, computational techniques that have been successfully applied
in statistical thermodynamics to describe phase transitions may provide new
insights to emerging behavior of biological systems. Here we will
systematically evaluate the translation of computational techniques from
solid-state physics to network models that closely resemble biological networks
and develop specific translational rules to tackle problems unique to living
systems. Hence we will focus on logic models exhibiting only two states in each
network node. Motivated by the apparent asymmetry between biological states
where an entity exhibits boolean states i.e. is active or inactive, we present
an adaptation of symmetric Ising model towards an asymmetric one fitting to
living systems here referred to as the modified Ising model with gene-type
spins. We analyze phase transitions by Monte Carlo simulations and propose
mean-field solution of modified Ising model of a network type that closely
resembles real-world network, the Barab\'{a}si-Albert model of scale-free
networks. We show that asymmetric Ising models show similarities to symmetric
Ising models with external field and undergoes a discontinuous phase transition
of the first-order and exhibits hysteresis. The simulation setup presented here
can be directly used for any biological network connectivity dataset and is
also applicable for other networks that exhibit similar states of activity.
This is a general statistical method to deal with non-linear large scale models
arising in the context of biological systems and is scalable to any network
size.
Advanced Mathematical Business Strategy Formulation Design
Authors: Song-Kyoo Kim
Date: 2019-08-15 00:00:00
Tasks: Decision Making, Form

This paper deals with the explicit design of strategy formulations to make the best strategic choices from a conventional matrix form of representing strategic choices. The explicit strategy formulation is an analytical model which is targeted to provide a mathematical strategy framework to find the best moment for strategy shifting to prepare rapid market changes. This theoretical model could be adapted into practically any strategic decision making situation when a strategic formulation is described as a matrix form with quantitative measured decision parameters. Analytically tractable results are obtained by using the fluctuation theory and these results are capable to predict the best moments of changing strategies in a matrix form. This research helps strategy decision makers who want to find the optimal moments of shifting present strategies.
Salient Speech Representations Based on Cloned Networks
Date: 2019-08-19 00:00:00
Tasks: Decoder

We define salient features as features that are shared by signals that are
defined as being equivalent by a system designer. The definition allows the
designer to contribute qualitative information. We aim to find salient features
that are useful as conditioning for generative networks. We extract salient
features by jointly training a set of clones of an encoder network. Each
network clone receives as input a different signal from a set of equivalent
signals. The objective function encourages the network clones to map their
input into a set of features that is identical across the clones. It
additionally encourages feature independence and, optionally, reconstruction of
a desired target signal by a decoder. As an application, we train a system that
extracts a time-sequence of feature vectors of speech and uses it as a
conditioning of a WaveNet generative system, facilitating both coding and
enhancement.
Evolutionary dynamics of cancer: from epigenetic regulation to cell population dynamics -- mathematical model framework, applications, and open problems
Authors: Jinzhi Lei
Date: 2019-08-19 00:00:00

Predictive modeling of the evolutionary dynamics of cancer is a challenge issue in computational cancer biology. In this paper, we propose a general mathematical model framework for the evolutionary dynamics of cancer with plasticity and heterogeneity in cancer cells. Cancer is a group of diseases involving abnormal cell growth, during which abnormal regulations in stem cell regeneration are essential for the dynamics of cancer development. In general, the dynamics of stem cell regeneration can be simplified as a $\mathrm{G_0}$ phase cell cycle model, which lead to a delay differentiation equation. When cell heterogeneity and plasticity are considered, we establish a differential-integral equation based on the random transition of epigenetic states of stem cells during cell division. The proposed model highlights cell heterogeneity and plasticity, and connects the heterogeneity with cell-to-cell variance in cellular behaviors, e.g. proliferation, apoptosis, and differentiation/senescence, and can be extended to include gene mutation-induced tumor development. Hybrid computations models are developed based on the mathematical model framework, and are applied to the process of inflammation-induced tumorigenesis and tumor relapse after CAR-T therapy. Finally, we give rise to several mathematical problems related to the proposed differential-integral equation. Answers to these problems are crucial for the understanding of the evolutionary dynamics of cancer.
Information Design for Regulating Traffic Flows under Uncertain Network State
Date: 2019-08-19 00:00:00

Traffic navigation services have gained widespread adoption in recent years.
The route recommendations generated by these services often leads to severe
congestion on urban streets, raising concerns from neighboring residents and
city authorities. This paper is motivated by the question: How can a
transportation authority design an information structure to induce a preferred
equilibrium traffic flow pattern in uncertain network state conditions? We
approach this question from a Bayesian persuasion viewpoint. We consider a
basic routing game with two parallel routes and an uncertain state that affects
the travel cost on one of the routes. The authority sends a noisy signal of the
state to a given fraction of travelers. The information structure (i.e.,
distribution of signals in each state) chosen by the authority creates a
heterogeneous information environment for the routing game. The solution
concept governing the travelers' route choices is Bayesian Wardrop Equilibrium.
We design an information structure to minimize the average traffic spillover --
the amount of equilibrium route flow exceeding a certain threshold -- on one of
the routes. We provide an analytical characterization of the optimal
information structure for any fraction of travelers receiving the signal. We
find that it can achieve the minimum spillover so long as the fraction of
travelers receiving the signal is larger than a threshold (smaller than 1).
Evaluation of Simplified Methodology for Obtaining mmWave MIMO Channels from Ray-Tracing Simulations
Date: 2019-08-20 00:00:00

The use of higher frequencies and MIMO is important in many 5G use cases.
However, the available channel models for millimeter waves (mmWaves) currently
demand investigation and the number of measurements is still limited. Using
simulators is a current practice in mmWave MIMO research and ray tracing is
considered one of the most accurate techniques. Due to the relatively long time
of ray tracing simulations, it is common practice to adopt a simplified
simulation methodology in which omnidirectional antennas are simulated and,
later, the results are used together with a geometrical model to consider that
antenna arrays were used. This allows flexibility and decreases the overall
time spent with simulations. This paper investigates the corresponding
assumptions and how accurate are the results of the simplified methodology when
compared to effectively using antenna arrays in the ray tracing simulation. The
preliminary results indicate that the distance between transmitter and receiver
needs to be sufficiently large.
The emergence of critical stocks in market crash
Date: 2019-08-20 00:00:00

In complex systems like financial market, risk tolerance of individuals is
crucial for system resilience.The single-security price limit, designed as risk
tolerance to protect investors by avoiding sharp price fluctuation, is blamed
for feeding market panic in times of crash.The relationship between the
critical market confidence which stabilizes the whole system and the price
limit is therefore an important aspect of system resilience. Using a simplified
dynamic model on networks of investors and stocks, an unexpected linear
association between price limit and critical market confidence is theoretically
derived and empirically verified in this paper. Our results highlight the
importance of relatively `small' but critical stocks that drive the system to
collapse by passing the failure from periphery to core. These small stocks,
largely originating from homogeneous investment strategies across the market,
has unintentionally suppressed system resilience with the exclusive increment
of individual risk tolerance. Imposing random investment requirements to
mitigate herding behavior can thus improve the market resilience.
Improvements to Warm-Started Optimized Trajectory Planning for ASVs
Date: 2019-08-20 00:00:00
Tasks: Trajectory Planning

We present improvements to a recently developed method for trajectory
planning for autonomous surface vehicles (ASVs) in terms of run time. The
original method combines two types of planners: An A* implementation that
quickly finds the global shortest piecewise linear path on a uniformly
discretized map, and an optimal control-based trajectory planner which takes
into account ASV dynamics. Firstly, we propose an improvement to the
discretization of the map by switching to a Voronoi diagram rather than the
uniform discretization, which offers a far more sparse search tree for the A*
implementation. Secondly, modifications to the path refinement are made, as
suggested in a paper by Bhattacharya and Gavrilova. The changes result in a
reduction to the run time of the first part of the method of 85% for an example
scenario while maintaining the same level of optimality.
Multi-Objective Optimization for Drone Delivery
Date: 2019-07-24 00:00:00
Tasks: Scheduling, Stochastic Optimization

Recently, an unmanned aerial vehicle (UAV), as known as drone, has become an
alternative means of package delivery. Although the drone delivery scheduling
has been studied in recent years, most existing models are formulated as a
single objective optimization problem. However, in practice, the drone delivery
scheduling has multiple objectives that the shipper has to achieve. Moreover,
drone delivery typically faces with unexpected events, e.g., breakdown or
unable to takeoff, that can significantly affect the scheduling problem.
Therefore, in this paper, we propose a multi-objective and three-stage
stochastic optimization model for the drone delivery scheduling, called
multi-objective optimization for drone delivery (MODD) system. To handle the
the multi-objective optimization in the MODD system, we apply
$\varepsilon$-constraint method. The performance evaluation is performed by
using a real dataset from Singapore delivery services.
A lognormal type stochastic volatility model with quadratic drift
Authors: Peter Carr, Sander Willems
Date: 2019-08-20 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper presents a novel one-factor stochastic volatility model where the instantaneous volatility of the asset log-return is a diffusion with a quadratic drift and a linear dispersion function. The instantaneous volatility mean reverts around a constant level, with a speed of mean reversion that is affine in the instantaneous volatility level. The steady-state distribution of the instantaneous volatility belongs to the class of Generalized Inverse Gaussian distributions. We show that the quadratic term in the drift is crucial to avoid moment explosions and to preserve the martingale property of the stock price process. Using a conveniently chosen change of measure, we relate the model to the class of polynomial diffusions. This remarkable relation allows us to develop a highly accurate option price approximation technique based on orthogonal polynomial expansions.
Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning
Authors: Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
Date: 2019-08-15 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning (RL)

For current and future Internet of Things (IoT) networks, mobile edge-cloud computation offloading (MECCO) has been regarded as a promising means to support delay-sensitive IoT applications. However, offloading mobile tasks to the cloud is vulnerable to security issues due to malicious mobile devices (MDs). How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem. In this paper, we investigate simultaneously the security and computation offloading problems in a multi-user MECCO system with blockchain. First, to improve the offloading security, we propose a trustworthy access control using blockchain, which can protect cloud resources against illegal offloading behaviours. Then, to tackle the computation management of authorized MDs, we formulate a computation offloading problem by jointly optimizing the offloading decisions, the allocation of computing resource and radio bandwidth, and smart contract usage. This optimization problem aims to minimize the long-term system costs of latency, energy consumption and smart contract fee among all MDs. To solve the proposed offloading problem, we develop an advanced deep reinforcement learning algorithm using a double-dueling Q-network. Evaluation results from real experiments and numerical simulations demonstrate the significant advantages of our scheme over existing approaches.
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning
Authors: Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
Date: 2019-08-15 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, reinforcement-learning, Reinforcement Learning (RL)

Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the mining process in mobile blockchain requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading and user privacy preservation as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading costs and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states and wireless channel qualities between MUs and MEC server. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.
LIDAR Data for Deep Learning-Based mmWave Beam-Selection
Date: 2020-01-13 00:00:00
Tasks: Autonomous Driving, Deep Learning, Position

Millimeter wave communication systems can leverage information from sensors
to reduce the overhead associated with link configuration. LIDAR (light
detection and ranging) is one sensor widely used in autonomous driving for high
resolution mapping and positioning. This paper shows how LIDAR data can be used
for line-of-sight detection and to reduce the overhead in millimeter wave
beam-selection. In the proposed distributed architecture, the base station
broadcasts its position. The connected vehicle leverages its LIDAR data to
suggest a set of beams selected via a deep convolutional neural network.
Co-simulation of communications and LIDAR in a vehicle-to-infrastructure (V2I)
scenario confirm that LIDAR can help configuring mmWave V2I links.
C-RAN Virtualization with OpenAirInterface
Date: 2019-08-20 00:00:00

C-RAN virtualization is a research topic with great interest since it allows
to share baseband processing resources.Therefore, in this work, we report the
implementation of a virtualized LTE testbed environment of C-RAN by integrating
the OpenAirInterface (OAI) with Docker. Using the test bed,we conducted a
workload study to understand the computation resource demand of C-RAN software.
Virtualization in containers has proven to be effective in creating a
functional 4G network which achieves realistic results to facilitate research.