pwc-archive/papers-with-abstracts huggingface dataset:
Empirical Evaluation of a 28 GHz Antenna Array on a 5G Mobile Phone Using a Body Phantom
Authors: Lauri Vähä-Savo, Christian Cziezerski, Mikko Heino, Katsuyuki Haneda, Clemens Icheln, Ali Hazmi, Ruiyuan Tian
Date: 2020-09-14 00:00:00

Implementation of an antenna array on a 5G mobile phone chassis is crucial in ensuring the radio link quality especially at millimeter-waves. However, we generally lack the ability to design antennas under practical operational conditions involving body effects of a mobile user in a repeatable manner. We developed numerical and physical phantoms of a human body for evaluation of mobile handset antennas at 28 GHz. While the numerical phantom retains a realistic and accurate body shape, our physical phantom has much simpler hexagonal cross-section to represent a body. Gains of the phased antenna array configuration on a mobile phone chassis, called co-located array is numerically and experimentally evaluated. The array is formed by placing two sets of 4-element dual-polarized patch antenna arrays, called two modules, at two locations of a mobile phone chassis. Modules are intended to collect the maximum amount of energy to the single transceiver chain. Spherical coverage of the realized gain by the array shows that the experimental statistics of the realized gains across entire solid angles agree with numerical simulations. We thereby demonstrate that our antenna evaluation method reproduces the reality and our phantom serves repeatable tests of antenna array prototypes at 28 GHz.
3-D macro/microporous-nanofibrous bacterial cellulose scaffolds seeded with BMP-2 preconditioned mesenchymal stem cells exhibit remarkable potential for bone tissue engineering
Authors: Swati Dubey, Rutusmita Mishra, Partha Roy, R. P. Singh
Date: 2020-09-14 00:00:00

Bone repair using BMP-2 is a promising therapeutic approach in clinical practices, however, high dosages required to be effective pose issues of cost and safety. The present study explores the potential of low dose BMP-2 treatment via tissue engineering approach, which amalgamates 3-D macro/microporous-nanofibrous bacterial cellulose (mNBC) scaffolds and low dose BMP-2 primed murine mesenchymal stem cells (C3H10T1/2 cells). Initial studies on cell-scaffold interaction using unprimed C3H10T1/2 cells confirmed that scaffolds provided a propitious environment for cell adhesion, growth, and infiltration, owing to its ECM-mimicking nano-micro-macro architecture. Osteogenic studies were conducted by preconditioning the cells with 50 ng/mL BMP-2 for 15 minutes, followed by culturing on mNBC scaffolds for up to three weeks. The results showed an early onset and significantly enhanced bone matrix secretion and maturation in the scaffolds seeded with BMP-2 primed cells compared to the unprimed ones. Moreover, mNBC scaffolds alone were able to facilitate the mineralization of cells to some extent. These findings suggest that, with the aid of 'osteoinduction' from low dose BMP-2 priming of stem cells and 'osteoconduction' from nano-macro/micro topography of mNBC scaffolds, a cost-effective bone tissue engineering strategy can be designed for quick and excellent in vivo osseointegration.
Robust discrete choice models with t-distributed kernel errors
Authors: Rico Krueger, Michel Bierlaire, Thomas Gasos, Prateek Bansal
Date: 2020-09-14 00:00:00
Tasks: Discrete Choice Models

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, standard discrete choice models produce biased estimates and suffer from compromised predictive accuracy. Robust statistical models are less sensitive to outliers than standard non-robust models. This paper analyses two robust alternatives to the multinomial probit (MNP) model. The two models are robit models whose kernel error distributions are heavy-tailed t-distributions to moderate the influence of outliers. The first model is the multinomial robit (MNR) model, in which a generic degrees of freedom parameter controls the heavy-tailedness of the kernel error distribution. The second model, the generalised multinomial robit (Gen-MNR) model, is more flexible than MNR, as it allows for distinct heavy-tailedness in each dimension of the kernel error distribution. For both models, we derive Gibbs samplers for posterior inference. In a simulation study, we illustrate the excellent finite sample properties of the proposed Bayes estimators and show that MNR and Gen-MNR produce more accurate estimates if the choice data contain outliers through the lens of the non-robust MNP model. In a case study on transport mode choice behaviour, MNR and Gen-MNR outperform MNP by substantial margins in terms of in-sample fit and out-of-sample predictive accuracy. The case study also highlights differences in elasticity estimates across models.
Optimal Resource Allocation for Delay Minimization in NOMA-MEC Networks
Authors: Fang Fang, Yanqing Xu, Zhiguo Ding, Chao Shen, Mugen Peng, George K. Karagiannidis
Date: 2020-09-11 00:00:00
Tasks: Edge-computing

Multi-access edge computing (MEC) can enhance the computing capability of mobile devices, while non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two strategies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task delay minimization in multi-user NOMA-MEC networks, where multiple users can offload their tasks simultaneously through the same frequency band. We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts. We aim to minimize the task delay among users by optimizing their tasks partition ratios and offloading transmit power. The delay minimization problem is first formulated, and it is shown that it is a nonconvex one. By carefully investigating its structure, we transform the original problem into an equivalent quasi-convex. In this way, a bisection search iterative algorithm is proposed in order to achieve the minimum task delay. To reduce the complexity of the proposed algorithm and evaluate its optimality, we further derive closed-form expressions for the optimal task partition ratio and offloading power for the case of two-user NOMA-MEC networks. Simulations demonstrate the convergence and optimality of the proposed algorithm and the effectiveness of the closed-form analysis.
Necessary and sufficient condition for hysteresis in the mathematical model of the cell type regulation of Bacillus subtilis
Authors: Sohei Tasaki, Madoka Nakayama, Izumi Takagi, Jun-ichi Wakita, Wataru Shoji
Date: 2020-09-14 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The key to a robust life system is to ensure that each cell population is maintained in an appropriate state. In this work, a mathematical model was used to investigate the control of the switching between the migrating and non-migrating states of the Bacillus subtilis cell population. In this case, the motile cells and matrix producers were the predominant cell types in the migrating cell population and non-migrating state, respectively, and could be suitably controlled according to the environmental conditions and cell density information. A minimal smooth model consisting of four ordinary differential equations was used as the mathematical model to control the B. subtilis cell types. Furthermore, the necessary and sufficient conditions for the hysteresis, which pertains to the change in the pheromone concentration, were clarified. In general, the hysteretic control of the cell state enables stable switching between the migrating and growth states of the B. subtilis cell population, thereby facilitating the biofilm life cycle. The results of corresponding culture experiments were examined, and the obtained corollaries were used to develop a model to input environmental conditions, especially, the external pH. On this basis, the environmental conditions were incorporated in a simulation model for the cell type control. In combination with a mathematical model of the cell population dynamics, a prediction model for colony growth involving multiple cell states, including concentric circular colonies of B. subtilis, could be established.
An Energy-efficient Wireless Neural Recording System with Compressed Sensing and Encryption
Authors: Xilin Liu, Andrew G. Richardson, Jan Van der Spiegel
Date: 2020-09-14 00:00:00
Tasks: compressed sensing, Data Compression

This paper presents a wireless neural recording system featuring energy-efficient data compression and encryption. An ultra-high efficiency is achieved by leveraging compressed sensing (CS) for simultaneous data compression and encryption. CS enables sub-Nyquist sampling of neural signals by taking advantage of its intrinsic sparsity. It simultaneously encrypts the data with the sampling matrix being the cryptographic key. To share the key over an insecure wireless channel, we implement an elliptic-curve cryptography (ECC) based key exchanging protocol. The CS operation is executed in a custom-designed IC fabricated in 180nm CMOS technology. Mixed-signal circuits are designed to optimize the power efficiency of the matrix-vector multiplication (MVM) of the CS operation. The ECC algorithm is implemented in a low-power Cortex-M0 microcontroller (MCU). To be protected from timing and power analysis attacks, the implementation avoids possible data-dependent branches and also employs a randomized ECC initialization. At a compression ratio of 8x, the average correlated coefficient between the reconstructed signals and the uncompressed signals is 0.973, while the ciphertext-only attacks (CoA) achieve no better than 0.054 over 200,000 attacks. The prototype achieves a 35x power saving compared with conventional implementation in low-power MCUs. This work demonstrates a promising solution for future chronic neural recording systems with requirements in high energy efficiency and security.
Vector copulas
Authors: Yanqin Fan, Marc Henry
Date: 2020-09-14 00:00:00

This paper introduces vector copulas associated with multivariate distributions with given multivariate marginals, based on the theory of measure transportation, and establishes a vector version of Sklar's theorem. The latter provides a theoretical justification for the use of vector copulas to characterize nonlinear or rank dependence between a finite number of random vectors (robust to within vector dependence), and to construct multivariate distributions with any given non overlapping multivariate marginals. We construct Elliptical and Kendall families of vector copulas, derive their densities, and present algorithms to generate data from them. The use of vector copulas is illustrated with a stylized analysis of international financial contagion.
Push-forward method for piecewise deterministic biochemical simulations
Date: 2020-09-14 00:00:00

A biochemical network can be simulated by a set of ordinary differential equations (ODE) under well stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the push-forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing.This work is an extended version of the work presented at the conference CMSB2019.
Passive communication with ambient noise
Authors: Josselin Garnier
Date: 2020-09-14 00:00:00
Tasks: valid

Motivated by applications to wireless communications, this paper addresses the propagation of waves transmitted by ambient noise sources and interacting with metamaterials. We discuss a generalized Helmholtz-Kirchhoff identity that is valid in dispersive media and we characterize the statistical properties of the empirical cross spectral density of the wave field. We can then introduce and analyze an original communication scheme between two passive arrays that uses only ambient noise illumination. The passive transmitter array does not transmit anything but it is a tunable metamaterial surface that can modulate its dispersive properties and encode a message in the modulation. The passive receiver array made of two receivers that are half-a-wavelength apart from each other can decode the message from the empirical cross spectral density of the wave field.
Grid-forming frequency shaping control
Date: 2020-09-14 00:00:00

As power systems transit to a state of high renewable penetration, little or
no presence of synchronous generators makes the prerequisite of well-regulated
frequency for grid-following inverters unrealistic. Thus, there is a trend to
resort to grid-forming inverters which set frequency directly. We propose a
novel grid-forming frequency shaping control that is able to shape the
aggregate system frequency dynamics into a first-order one with the desired
steady-state frequency deviation and Rate of Change of Frequency (RoCoF) after
a sudden power imbalance. The no overshoot property resulting from the
first-order dynamics allows the system frequency to monotonically move towards
its new steady-state without experiencing frequency Nadir, which largely
improves frequency security. We prove that our grid-forming frequency-shaping
control renders the system internally stable under mild assumptions. The
performance of the proposed control is verified via numerical simulations on a
modified Icelandic Power Network test case.
Graph-Adaptive Activation Functions for Graph Neural Networks
Authors: Bianca Iancu, Luana Ruiz, Alejandro Ribeiro, Elvin Isufi
Date: 2020-09-14 00:00:00
Tasks: Recommendation Systems

Activation functions are crucial in graph neural networks (GNNs) as they allow defining a nonlinear family of functions to capture the relationship between the input graph data and their representations. This paper proposes activation functions for GNNs that not only adapt to the graph into the nonlinearity, but are also distributable. To incorporate the feature-topology coupling into all GNN components, nodal features are nonlinearized and combined with a set of trainable parameters in a form akin to graph convolutions. The latter leads to a graph-adaptive trainable nonlinear component of the GNN that can be implemented directly or via kernel transformations, therefore, enriching the class of functions to represent the network data. Whether in the direct or kernel form, we show permutation equivariance is always preserved. We also prove the subclass of graph-adaptive max activation functions are Lipschitz stable to input perturbations. Numerical experiments with distributed source localization, finite-time consensus, distributed regression, and recommender systems corroborate our findings and show improved performance compared with pointwise as well as state-of-the-art localized nonlinearities.
Analysis of Random Access in NB-IoT Networks with Three Coverage Enhancement Groups: A Stochastic Geometry Approach
Authors: Yan Liu, Yansha Deng, Nan Jiang, Maged Elkashlan, Arumugam Nallanathan
Date: 2020-09-14 00:00:00

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values and transmission resources. To characterize the RACH performance of the NB-IoT network with three CE groups, this paper develops a novel traffic-aware spatio-temporal model to analyze the RACH success probability, where both the preamble transmission outage and the collision events of each CE group jointly determine the traffic evolution and the RACH success probability. Based on this analytical model, we derive the analytical expression for the RACH success probability of a randomly chosen IoT device in each CE group over multiple time slots with different RACH schemes, including baseline, back-off (BO), access class barring (ACB), and hybrid ACB and BO schemes (ACB&BO). Our results have shown that the RACH success probabilities of the devices in three CE groups outperform that of a single CE group network but not for all the groups, which is affected by the choice of the categorizing parameters.This mathematical model and analytical framework can be applied to evaluate the performance of multiple group users of other networks with spatial separations.
VRFT with ARX controller model and constrained total least squares
Date: 2020-09-14 00:00:00

The virtual reference feedback tuning (VRFT) is a non-iterative data-driven
(DD) method employed to tune a controller's parameters aiming to achieve a
prescribed closed-loop performance. In its most common formulation, the
parameters of a linearly parametrized controller are estimated by solving a
least squares (LS) problem, which in the presence of noise leads to a biased
estimate of the controller's parameters. To eliminate this bias, an
instrumental variable (IV) variant of the method is usual, at the cost of
increasing significantly the estimate's variance. In the present work, we
propose to apply the constrained total least squares (CTLS) solution to the
VRFT problem. We formulate explicitly the VRFT solution with CTLS for
controllers described by an autoregressive exogenous (ARX) model. The
effectiveness of the proposed solution is illustrated by two case studies in
which it is compared to the usual VRFT solutions and to another, statistically
efficient, design method.
Towards the Use of Re-configurable Intelligent Surfaces in VLC Systems: Beam Steering
Authors: Alain R. Ndjiongue, Telex M. N. Ngatched, Octavia A. Dobre, Harald Haas
Date: 2020-09-15 00:00:00

The emergence of visible light communication (VLC) technology as a solution to solve radio frequency impediments, such as spectrum shortage, is continuously appealing. In addition to its large and unlicensed bandwidth, VLC provides a high level of security in a closed room with zero radio frequency interference. However, loss of the VLC signal is experienced when the receiver rotates or moves. This challenge requires a special solution for integration into portable devices. On the other hand, re-configurable intelligent surface (RIS) is a technology exploited in radio frequency to solve dead zones and loss of signal. RIS elements are characterized by tunable physico-chemical characteristics including physical depth and refractive index. In this paper, we exploit these RIS attributes to steer the incident light beam and offer the VLC receiver a large range of rotation angle while improving its signal detection capabilities. We show that instead of using convex, parabolic, or spherical lenses, adopting a meta-lens with artificial muscles or a thin-film crystal-liquid with embedded Titanium dioxide nano-disk, a VLC receiver can detect light rays at $90^o$ incidence angle with high precision and considerable improvement in the detected light intensity, even with a miniaturized single photodetector.
Sense-Store-Send: Trajectory Optimization for a Buffer-aided Internet of UAVs
Authors: Yujie Jin, Hongliang Zhang, Shuhang Zhang, Zhu Han, Lingyang Song
Date: 2020-09-15 00:00:00

In this letter, we study a buffer-aided Internet of unmanned aerial vehicles (UAVs) in which a UAV performs data sensing, stores the data, and sends it to the base station (BS) in cellular networks. To minimize the overall completion time for all the sensing tasks, we formulate a joint trajectory, sensing location, and sensing time optimization problem. To solve this NP-hard problem efficiently, we propose an iterative trajectory, sensing location and sensing time optimization (ITLTO) algorithm, and discuss the trade-off between sensing time and flying time. Simulation results show that the proposed algorithm can effectively reduce the completion time for the sensing tasks.
Compositional Models for Power Systems
Date: 2020-09-15 00:00:00
Tasks: Management

The problem of integrating multiple overlapping models and data is pervasive
in engineering, though often implicit. We consider this issue of model
management in the context of the electrical power grid as it transitions
towards a modern 'Smart Grid.' We present a methodology for specifying,
managing, and reasoning within multiple models of distributed energy resources
(DERs), entities which produce, consume, or store power, using categorical
databases and symmetric monoidal categories. Considering the problem of
distributing power on the grid in the presence of DERs, we show how to connect
a generic problem specification with implementation-specific numerical solvers
using the paradigm of categorical databases.
Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines
Authors: Oussama Ghdiri, Wael Jaafar, Safwan Alfattani, Jihene Ben Abderrazak, Halim Yanikomeroglu
Date: 2020-09-15 00:00:00
Tasks: Trajectory Planning

In this paper, we focus on energy-efficient UAV-based IoT data collection in sensor networks in which the sensed data have different time deadlines. In the investigated setting, the sensors are clustered and managed by cluster heads (CHs), and multiple UAVs are used to collect data from the CHs. The formulated problem is solved through a two-step approach. In the first step, an efficient method is proposed to determine the minimal number of CHs and their best locations. Subsequently, the minimal number of UAVs and their trajectories are obtained by solving the associated capacitated vehicle routing problem. Results show the efficiency of our proposed CHs placement method compared to baseline approaches, where bringing the CHs closer to the dockstation allows significant energy savings. Moreover, among different UAV trajectory planning algorithms, Tabu search achieves the best energy consumption. Finally, the impact of the battery capacity and time deadline are investigated in terms of consumed energy, number of visited CHs, and number of deployed UAVs.
Recent scaling properties of Bitcoin price returns
Authors: Tetsuya Takaishi
Date: 2020-09-15 00:00:00
Tasks: Time Series, Time Series Analysis

While relevant stylized facts are observed for Bitcoin markets, we find a distinct property for the scaling behavior of the cumulative return distribution. For various assets, the tail index $\mu$ of the cumulative return distribution exhibits $\mu \approx 3$, which is referred to as "the inverse cubic law." On the other hand, that of the Bitcoin return is claimed to be $\mu \approx 2$, which is known as "the inverse square law." We investigate the scaling properties using recent Bitcoin data and find that the tail index changes to $\mu \approx 3$, which is consistent with the inverse cubic law. This suggests that some properties of the Bitcoin market could vary over time. We also investigate the autocorrelation of absolute returns and find that it is described by a power-law with two scaling exponents. By analyzing the absolute returns standardized by the realized volatility, we verify that the Bitcoin return time series is consistent with normal random variables with time-varying volatility.