pwc-archive/papers-with-abstracts huggingface dataset:
Relay-Assisted Cooperative Federated Learning
Authors: Zehong Lin, Hang Liu, Ying-Jun Angela Zhang
Date: 2021-07-20 00:00:00
Tasks: Federated Learning

Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the communication efficiency of FL, over-the-air computation allows a large number of mobile devices to concurrently upload their local models by exploiting the superposition property of wireless multi-access channels. Due to wireless channel fading, the model aggregation error at the edge server is dominated by the weakest channel among all devices, causing severe straggler issues. In this paper, we propose a relay-assisted cooperative FL scheme to effectively address the straggler issue. In particular, we deploy multiple half-duplex relays to cooperatively assist the devices in uploading the local model updates to the edge server. The nature of the over-the-air computation poses system objectives and constraints that are distinct from those in traditional relay communication systems. Moreover, the strong coupling between the design variables renders the optimization of such a system challenging. To tackle the issue, we propose an alternating-optimization-based algorithm to optimize the transceiver and relay operation with low complexity. Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large. The analysis provides critical insights on relay deployment in the implementation of cooperative FL. Extensive numerical results show that our design achieves faster convergence compared with state-of-the-art schemes.
How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?
Authors: Tung T. Vu, Hien Quoc Ngo, Thomas L. Marzetta, Michail Matthaiou
Date: 2021-07-20 00:00:00
Tasks: Federated Learning

Federated learning (FL) has been considered as a promising learning framework for future machine learning systems due to its privacy preservation and communication efficiency. In beyond-5G/6G systems, it is likely to have multiple FL groups with different learning purposes. This scenario leads to a question: How does a wireless network support multiple FL groups? As an answer, we first propose to use a cell-free massive multiple-input multiple-output (MIMO) network to guarantee the stable operation of multiple FL processes by letting the iterations of these FL processes be executed together within a large-scale coherence time. We then develop a novel scheme that asynchronously executes the iterations of FL processes under multicasting downlink and conventional uplink transmission protocols. Finally, we propose a simple/low-complexity resource allocation algorithm which optimally chooses the power and computation resources to minimize the execution time of each iteration of each FL process.
Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach
Authors: Guoliang Li, Shuai Wang, Jie Li, Rui Wang, Fan Liu, Xiaohui Peng, Tony Xiao Han, Chengzhong Xu
Date: 2021-07-20 00:00:00
Tasks: Integrated sensing and communication, ISAC

Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition. This is because of the large experimental datasets and the black-box nature of deep neural networks. This paper presents SDP3, a Simulation-Driven Performance Predictor and oPtimizer, which consists of SDP3 data simulator, SDP3 performance predictor and SDP3 performance optimizer. Specifically, the SDP3 data simulator generates vivid wireless sensing datasets in a virtual environment, the SDP3 performance predictor predicts the sensing performance based on the function regression method, and the SDP3 performance optimizer investigates the sensing and communication performance tradeoff analytically. It is shown that the simulated sensing dataset matches the experimental dataset very well in the motion recognition accuracy. By leveraging SDP3, it is found that the achievable region of recognition accuracy and communication throughput consists of a communication saturation zone, a sensing saturation zone, and a communication-sensing adversarial zone, of which the desired balanced performance for ISAC systems lies in the third one.
Predicting the 2020 US Presidential Election with Twitter
Authors: Michael Caballero
Date: 2021-07-19 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

One major sub-domain in the subject of polling public opinion with social media data is electoral prediction. Electoral prediction utilizing social media data potentially would significantly affect campaign strategies, complementing traditional polling methods and providing cheaper polling in real-time. First, this paper explores past successful methods from research for analysis and prediction of the 2020 US Presidential Election using Twitter data. Then, this research proposes a new method for electoral prediction which combines sentiment, from NLP on the text of tweets, and structural data with aggregate polling, a time series analysis, and a special focus on Twitter users critical to the election. Though this method performed worse than its baseline of polling predictions, it is inconclusive whether this is an accurate method for predicting elections due to scarcity of data. More research and more data are needed to accurately measure this method's overall effectiveness.
Two-stage Planning for Electricity-Gas Coupled Integrated Energy System with CCUS Considering Carbon Tax and Price Uncertainty
Authors: Ang Xuan, Xinwei Shen, Qinglai Guo, Hongbin Sun
Date: 2021-07-19 00:00:00

In this article, we propose two-stage planning models for Electricity-Gas Coupled Integrated Energy System (EGC-IES), in which traditional thermal power plants (TTPPs) are considered to be retrofitted into carbon capture power plants (CCPPs), with power to gas (PtG) coupling CCPPs to gas system. The sizing and siting of carbon capture, utilisation and storage (CCUS)/PtG facilities, as well as the operation cost of TTPPs/CCPPs/gas sources/PtG, are all considered in the proposed model, including penalty on carbon emissions and revenue of CCUS. With changing policy on climate change and carbon emission regulation, the uncertainties of carbon price and carbon tax are also analysed and considered in the proposed planning model. The stochastic planning, and robust planning methods are introduced to verify mutually through economic and carbon indices. The proposed methods' effectiveness in reducing carbon emissions, increasing profit of CCUS from EGC-IES are demonstrated through various cases and discussions.
Explicit Calibration of mmWave Phased Arrays with Phase Dependent Errors
Authors: Joyson Sebastian, Pranav Dayal, Kee-Bong Song, Walid AliAhmad
Date: 2021-07-16 00:00:00

We consider an error model for phased array with gain errors and phase errors, with errors dependent on the phase applied and the antenna index. Under this model, we propose an algorithm for measuring the errors by selectively turning on the antennas at specific phases and measuring the transmitted power. In our algorithm, the antennas are turned on individually and then pairwise for the measurements, and rotation of the phased array is not required. We give numerical results to measure the accuracy of the algorithm as a function of the signal-to-noise ratio in the measurement setup. We also compare the performance of our algorithm with the traditional rotating electric vector (REV) method and observe the superiority of our algorithm. Simulations also demonstrate an improvement in the coverage on comparing the cumulative distribution function (CDF) of equivalent isotropically radiated power (EIRP) before and after calibration.
Channel Performance Estimations with Extended Channel Probing
Authors: Kaida Kaeval, Helmut Griesser, Klaus Grobe, Joerg-Peter Elbers, Marko Tikas, Gert Jervan
Date: 2021-07-13 00:00:00

We test the concept of extended channel probing in an Optical Spectrum as a Service scenario in coherent optimized flex-grid long-haul and 10Gbit/s OOK optimized 100-GHz fixed-grid dispersion-managed legacy DWDM networks. An estimation accuracy better than +/- 0.1dB in GSNR implementation margin is obtained for both networks by using flexible coherent transceivers on lightpaths up to 822km on regional haul legacy network and up to 5738km on a long-haul network. We also explain, how to detect the operation regime of the channel by analyzing the received GSNR results from constant power spectral density (PSD) probing mode and constant signal power probing mode
An Information Theory Approach to Physical Domain Discovery
Authors: Daniel Shea, Stephen Casey
Date: 2021-07-13 00:00:00

The project of physics discovery is often equivalent to finding the most concise description of a physical system. The description with optimum predictive capability for a dataset generated by a physical system is one that minimizes both predictive error on the dataset and the complexity of the description. The discovery of the governing physics of a system can therefore be viewed as a mathematical optimization problem. We outline here a method to optimize the description of arbitrarily complex physical systems by minimizing the entropy of the description of the system. The Recursive Domain Partitioning (RDP) procedure finds the optimum partitioning of each physical domain into subdomains, and the optimum predictive function within each subdomain. Penalty functions are introduced to limit the complexity of the predictive function within each domain. Examples are shown in 1D and 2D. In 1D, the technique effectively discovers the elastic and plastic regions within a stress-strain curve generated by simulations of amorphous carbon material, while in 2D the technique discovers the free-flow region and the inertially-obstructed flow region in the simulation of fluid flow across a plate.
Cluster Consensus on Matrix-weighted Switching Networks
Authors: Lulu Pan, Haibin Shao, Mehran Mesbahi, Dewei Li, Yugeng Xi
Date: 2021-07-20 00:00:00

This paper examines the cluster consensus problem of multi-agent systems on matrix-weighted switching networks. Necessary and/or sufficient conditions under which cluster consensus can be achieved are obtained and quantitative characterization of the steady-state of the cluster consensus are provided as well. Specifically, if the underlying network switches amongst finite number of networks, a necessary condition for cluster consensus of multi-agent system on switching matrix-weighted networks is firstly presented, it is shown that the steady-state of the system lies in the intersection of the null space of matrix-valued Laplacians corresponding to all switching networks. Second, if the underlying network switches amongst infinite number of networks, the matrix-weighted integral network is employed to provide sufficient conditions for cluster consensus and the quantitative characterization of the corresponding steady-state of the multi-agent system, using null space analysis of matrix-valued Laplacian related of integral network associated with the switching networks. In particular, conditions for the bipartite consensus under the matrix-weighted switching networks are examined. Simulation results are finally provided to demonstrate the theoretical analysis.
A biophysical network model reveals the link between deficient inhibitory cognitive control and major neurotransmitter and neural connectivity hypotheses in schizophrenia
Authors: Konstantinos Spiliotis, Giannis Kahramanoglou, Jens Starke, Nikolaos Smyrnis, Constantinos Siettos
Date: 2021-07-20 00:00:00
Tasks: Functional Connectivity

We address a biophysical network dynamical model to study how the modulation of dopamine (DA) activity and related N-methyl-d-aspartate (NMDA) glutamate receptor activity as well as the emerging Pre-Frontal Cortex (PFC) functional connectivity network (FCN) affect inhibitory cognitive function in schizophrenia in an antisaccade task. The values of the model parameters and the topology of the PFC-FCN were estimated by minimizing the differences between simulations and the observed distributions of reaction times (RT) during the performance of the antisaccade task in 30 patients with schizophrenia and 30 healthy controls. We show that the proposed model approximates remarkably well the predicted prefrontal cortical DA hypo-activity and the related NMDA receptor hypo-function as well as the FCN dysconnection pattern that are considered as the major etio-pathological hypotheses to explain cognitive deficits in schizophrenia.
Adaptive Multilevel Monte Carlo for Probabilities
Authors: Abdul-Lateef Haji-Ali, Jonathan Spence, Aretha Teckentrup
Date: 2021-07-19 00:00:00

We consider the numerical approximation of $\mathbb{P}[G\in \Omega]$ where the $d$-dimensional random variable $G$ cannot be sampled directly, but there is a hierarchy of increasingly accurate approximations $\{G_\ell\}_{\ell\in\mathbb{N}}$ which can be sampled. The cost of standard Monte Carlo estimation scales poorly with accuracy in this setup since it compounds the approximation and sampling cost. A direct application of Multilevel Monte Carlo improves this cost scaling slightly, but returns sub-optimal computational complexities since estimation of the probability involves a discontinuous functional of $G_\ell$. We propose a general adaptive framework which is able to return the MLMC complexities seen for smooth or Lipschitz functionals of $G_\ell$. Our assumptions and numerical analysis are kept general allowing the methods to be used for a wide class of problems. We present numerical experiments on nested simulation for risk estimation, where $G = \mathbb{E}[X|Y]$ is approximated by an inner Monte Carlo estimate. Further experiments are given for digital option pricing, involving an approximation of a $d$-dimensional SDE.
Time-adaptive high-order compact finite difference schemes for option pricing in a family of stochastic volatility models
Authors: Bertram Düring, Christof Heuer
Date: 2021-07-19 00:00:00

We propose a time-adaptive, high-order compact finite difference scheme for option pricing in a family of stochastic volatility models. We employ a semi-discrete high-order compact finite difference method for the spatial discretisation, and combine this with an adaptive time discretisation, extending ideas from [LSRHF02] to fourth-order multistep methods in time.
Improving Reverberant Speech Separation with Multi-stage Training and Curriculum Learning
Authors: Rohith Aralikatti, Anton Ratnarajah, Zhenyu Tang, Dinesh Manocha
Date: 2021-07-19 00:00:00
Tasks: Speech Separation

We present a novel approach that improves the performance of reverberant speech separation. Our approach is based on an accurate geometric acoustic simulator (GAS) which generates realistic room impulse responses (RIRs) by modeling both specular and diffuse reflections. We also propose three training methods - pre-training, multi-stage training and curriculum learning that significantly improve separation quality in the presence of reverberation. We also demonstrate that mixing the synthetic RIRs with a small number of real RIRs during training enhances separation performance. We evaluate our approach on reverberant mixtures generated from real, recorded data (in several different room configurations) from the VOiCES dataset. Our novel approach (curriculum learning+pre-training+multi-stage training) results in a significant relative improvement over prior techniques based on image source method (ISM).
Collaborative rover-copter path planning and exploration with temporal logic specifications based on Bayesian update under uncertain environments
Authors: Kazumune Hashimoto, Natsuko Tsumagari, Toshimitsu Ushio
Date: 2021-07-20 00:00:00

This paper investigates a collaborative rover-copter path planning and exploration with temporal logic specifications under uncertain environments. The objective of the rover is to complete a mission expressed by a syntactically co-safe linear temporal logic (scLTL) formula, while the objective of the copter is to actively explore the environment and reduce its uncertainties, aiming at assisting the rover and enhancing the efficiency of the mission completion. To formalize our approach, we first capture the environmental uncertainties by environmental beliefs of the atomic propositions, under an assumption that it is unknown which properties (or, atomic propositions) are satisfied in each area of the environment. The environmental beliefs of the atomic propositions are updated according to the Bayes rule based on the Bernoulli-type sensor measurements provided by both the rover and the copter. Then, the optimal policy for the rover is synthesized by maximizing a belief of the satisfaction of the scLTL formula through an implementation of an automata-based model checking. An exploration policy for the copter is then synthesized by employing the notion of an entropy that is evaluated based on the environmental beliefs of the atomic propositions, and a path that the rover intends to follow according to the optimal policy. As such, the copter can actively explore regions whose uncertainties are high and that are relevant to the mission completion. Finally, some numerical examples illustrate the effectiveness of the proposed approach.
Application of Terminal Region Enlargement Approach for Discrete Time Quasi Infinite Horizon NMPC
Authors: Chinmay Rajhans, Sowmya Gupta
Date: 2021-07-20 00:00:00
Tasks: Model Predictive Control

Ensuring nominal asymptotic stability of the Non-linear Model Predictive Control (NMPC) controller is not trivial. Stabilizing ingredients such as terminal penalty term and terminal region are crucial in establishing the asymptotic stability. Approaches available in the literature provide limited degrees of freedom for the characterization of the terminal region for the discrete time Quasi Infinite Horizon NMPC (QIH-NMPC) formulation. Current work presents alternate approaches namely arbitrary controller based approach and LQR based approach, which provide large degrees of freedom for enlarging the terminal region. Both the approaches are scalable to system of any state and input dimension. Approach from the literature provides a scalar whereas proposed approaches provide a linear controller and two additive matrices as tuning parameters for shaping of the terminal region. Proposed approaches involve solving modified Lyapunov equations to compute terminal penalty term, followed by explicit characterization of the terminal region. Efficacy of the proposed approaches is demonstrated using benchmark two state system. Terminal region obtained using the arbitrary controller based approach and LQR based approach are approximately 10.4723 and 9.5055 times larger by area measure when compared to the largest terminal region obtained using the approach from the literature.
Enhanced Transmission and Distribution NetworkCoordination to Host More Electric Vehicles and PV
Authors: Abbas Rabiee, Andrew Keane, Alireza Soroudi
Date: 2021-07-19 00:00:00

Distributed energy resources (DERs) installed at electric distribution networks create different opportunities and challenges for the distribution system operator (DSO). By increasing the penetration level of DERs, the impacts of these technologies are also sensed by the transmission system operator (TSO). The focus of this paper is on investigating and managing the impacts of the Electric Vehicle (EV) charging and solar Photo-Voltaic (PV) generation considering TSO-DSO interaction constraints. These constraints include the driving point impedance that reflects the impact of the DER-rich distribution system on the upstream HV network and voltage profile control through the on-load tap changing (OLTC) transformer's operation. The aim is to optimally utilize the existing assets and flexibilities to have more EVs and PVs at the distribution level without violating constraints at both transmission and distribution levels. The proposed model could readily be used by policymakers to design and implement the proper time-of-use tariffs for improved handling of the increasing EVs and PVs penetrations in emerging distribution networks. The proposed method is implemented on the IEEE 69-bus standard MV distribution network to demonstrate the applicability.
Supply Chain Digital Twin Framework Design: An Approach of Supply Chain Operations Reference Model and System of Systems
Authors: Jie Zhang, Alexandra Brintrup, Anisoara Calinescu, Edward Kosasih, Angira Sharma
Date: 2021-07-19 00:00:00

Digital twin technology has been regarded as a beneficial approach in supply chain development. Different from traditional digital twin (temporal dynamic), supply chain digital twin is a spatio-temporal dynamic system. This paper explains what is 'twined' in supply chain digital twin and how to 'twin' them to handle the spatio-temporal dynamic issue. A supply chain digital twin framework is developed based on the theories of system of systems and supply chain operations reference model. This framework is universal and can be applied in various types of supply chain systems. We firstly decompose the supply chain system into unified standard blocks preparing for the adoption of digital twin. Next, the idea of supply chain operations reference model is adopted to digitise basic supply chain activities within each block and explain how to use existing information system. Then, individual sub-digital twin is established for each member in supply chain system. After that, we apply the concept of system of systems to integrate and coordinate sub-digital twin into supply chain digital twin from the views of supply chain business integration and information system integration. At last, one simple supply chain system is applied to illustrate the application of the proposed model.
Probability density evolution filter
Authors: Yazhou Xu
Date: 2021-07-15 00:00:00

Based on probability density evolution method (PDEM) and Bayes law, a new filter strategy is proposed, in which the prior probability of system state of interest is predicted by solving the general density evolution equation (GDEE), the posterior probability of system state is then updated in terms of Bayes formula. Furthermore, a Chebyshev polynomial-based collocation method is employed to obtain numerical solutions of the prior probability. An illustrative example is finally presented to validate the probability density evolution filter (PDEF) in comparison to particle filter (PF) and UKF. Overall, PDEF exhibits accuracy close to PF without any resampling algorithm.