pwc-archive/papers-with-abstracts huggingface dataset:
Management of Social and Economic Development of Municipalities
Authors: Maria A. Shishanina, Anatoly A. Sidorov
Date: 2021-11-26 00:00:00
Tasks: Management

The paper discusses the process of social and economic development of municipalities. A conclusion is made that developing an adequate model of social and economic development using conventional approaches presents a considerable challenge. It is proposed to use semantic modeling to represent the social and economic development of municipalities, and cognitive mapping to identify the set of connections that occur among indicators and that have a direct impact on social and economic development.
Do price reductions attract customers in urban public transport? A synthetic control approach
Authors: Hannes Wallimann, Kevin Blättler, Widar von Arx
Date: 2021-11-25 00:00:00
Tasks: Blocking

In this paper, we assess the demand effects of lower public transport fares in Geneva, an urban area in Switzerland. Considering a unique sample based on transport companies' annual reports, we find that, when reducing the costs of annual season tickets, day tickets and hourly tickets (by up to 29%, 6% and 20%, respectively), demand increases by, on average, over five years, about 10.6%. To the best of our knowledge, we are the first to show how the synthetic control method (Abadie and Gardeazabal, 2003, Abadie, Diamond, and Hainmueller, 2010) can be used to assess such (for policy-makers) important price reduction effects in urban public transport. Furthermore, we propose an aggregate metric that inherits changes in public transport supply (e.g., frequency increases) to assess these demand effects, namely passenger trips per vehicle kilometre. This metric helps us to isolate the impact of price reductions by ensuring that companies' frequency increases do not affect estimators of interest. In addition, we show how to investigate the robustness of results in similar settings. Using a recent statistical method and a different study design, i.e., not blocking off supply changes as an alternate explanation of the effect, leads us to a lower bound of the effect, amounting to an increase of 3.7%. Finally, as far as we know, it is the first causal estimate of price reduction on urban public transport initiated by direct democracy.
Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation
Authors: Georgios Gerasimou
Date: 2021-11-29 00:00:00
Tasks: Combinatorial Optimization, Descriptive, Experimental Design

Recovering and distinguishing between the strict-preference, indifference and/or indecisiveness parts of a decision maker's preferences is a challenging task but also important for testing theory and conducting welfare analysis. This paper contributes towards this goal by reporting on data from a lab experiment on riskless choice that were analyzed with novel theory-guided computational methods. The experiment included both Forced- and Free-Choice treatments. Its primary novelty consisted of allowing all subjects to select multiple alternatives at each menu. Based on a non-parametric goodness-of-fit criterion that we introduce, which generalizes intuitively a widely used pre-existing method to environments of multi-valued choices, each subjects' decision data were tested against three structured general-choice models that feature maximization of stable but potentially weak and/or incomplete preferences. Nearly 60% of all subjects' are well-explained by one of these models, typically with a unique model-optimal preference relation per subject. Importantly, preferences usually (80%) had a non-trivial indifference part and, where applicable, a clearly distinct indecisiveness part. The achieved uncoupling of revealed indifference and indecisiveness is documented empirically for the first time.
Markovian Persuasion
Authors: Ehud Lehrer, Dimitry Shaiderman
Date: 2021-11-29 00:00:00

In the classical Bayesian persuasion model an informed player and an uninformed one engage in a static interaction. The informed player, the sender, knows the state of nature, while the uninformed one, the receiver, does not. The informed player partially shares his private information with the receiver and the latter then, based on her belief about the state, takes an action. This action determines, together with the state of nature, the utility of both players. We consider a dynamic Bayesian persuasion situation where the state of nature evolves according to a Markovian law. In this repeated persuasion model an optimal disclosure strategy of the sender should, at any period, balance between getting high stage payoff and future implications on the receivers' beliefs. We discuss optimal strategies under different discount factors and characterize when the asymptotic value achieves the maximal value possible.
Projected Multi-Agent Consensus Equilibrium for Ptychographic Image Reconstruction
Authors: Qiuchen Zhai, Brendt Wohlberg, Gregery T. Buzzard, Charles A. Bouman
Date: 2021-11-28 00:00:00
Tasks: Image Reconstruction

Ptychography is a computational imaging technique using multiple, overlapping, coherently illuminated snapshots to achieve nanometer resolution by solving a nonlinear phase-field recovery problem. Ptychography is vital for imaging of manufactured nanomaterials, but existing algorithms have computational shortcomings that limit large-scale application. In this paper, we present the Projected Multi-Agent Consensus Equilibrium (PMACE) approach for solving the ptychography inversion problem. This approach extends earlier work on MACE, which formulates an inversion problem as an equilibrium among multiple agents, each acting independently to update a full reconstruction. In PMACE, each agent acts on a portion (projection) corresponding to one of the snapshots, and these updates to projections are then combined to give an update to the full reconstruction. The resulting algorithm is easily parallelized, with convergence properties inherited from convergence results associated with MACE. We apply our method on simulated data and demonstrate that it outperforms competing algorithms in both reconstruction quality and convergence speed.
Unsupervised cross domain learning with applications to 7 layer segmentation of OCTs
Authors: Yue Wu, Abraham Olvera Barrios, Ryan Yanagihara, Irene Leung, Marian Blazes, Adnan Tufail, Aaron Lee
Date: 2021-11-23 00:00:00
Tasks: Domain Adaptation

Unsupervised cross domain adaptation for OCT 7 layer segmentation and other medical applications where labeled training data is only available in a source domain and unavailable in the target domain. Our proposed method helps generalize of deep learning to many areas in the medical field where labeled training data are expensive and time consuming to acquire or where target domains are too novel to have had labelling.
Harmonic Retrieval with $L_1$-Tucker Tensor Decomposition
Authors: Zhenting Luan, Zhenyu Ming, Yuchi Wu, Wei Han, Xiang Chen, Bo Bai, Liping Zhang
Date: 2021-11-29 00:00:00
Tasks: Retrieval, Tensor Decomposition

Harmonic retrieval (HR) has a wide range of applications in the scenes where signals are modelled as a summation of sinusoids. Past works have developed a number of approaches to recover the original signals. Most of them rely on classical singular value decomposition, which are vulnerable to unexpected outliers. In this paper, we present new decomposition algorithms of third-order complex-valued tensors with $L_1$-principle component analysis ($L_1$-PCA) of complex data and apply them to a novel random access HR model in presence of outliers. We also develop a novel subcarrier recovery method for the proposed model. Simulations are designed to compare our proposed method with some existing tensor-based algorithms for HR. The results demonstrate the outlier-insensitivity of the proposed method.
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Authors: Jinxiang Song, Christian Häger, Jochen Schröder, Alexandre Graell i Amat, Henk Wymeersch
Date: 2021-11-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose an AE-based transceiver for a WDM system impaired by hardware imperfections. We design our AE following the architecture of conventional communication systems. This enables to initialize the AE-based transceiver to have similar performance to its conventional counterpart prior to training and improves the training convergence rate. We first train the AE in a single-channel system, and show that it achieves performance improvements by putting energy outside the desired bandwidth, and therefore cannot be used for a WDM system. We then train the AE in a WDM setup. Simulation results show that the proposed AE significantly outperforms the conventional approach. More specifically, it increases the spectral efficiency of the considered system by reducing the guard band by 37\% and 50\% for a root-raised-cosine filter-based matched filter with 10\% and 1\% roll-off, respectively. An ablation study indicates that the performance gain can be ascribed to the optimization of the symbol mapper, the pulse-shaping filter, and the symbol demapper. Finally, we use reinforcement learning to learn the pulse-shaping filter under the assumption that the channel model is unknown. Simulation results show that the reinforcement-learning-based algorithm achieves similar performance to the standard supervised end-to-end learning approach assuming perfect channel knowledge.
Radio Frequency Fingerprint Identification for Security in Low-Cost IoT Devices
Authors: Guanxiong Shen, Junqing Zhang, Alan Marshall, Mikko Valkama, Joseph Cavallaro
Date: 2021-11-28 00:00:00
Tasks: Data Augmentation

Radio frequency fingerprint identification (RFFI) can uniquely classify wireless devices by analyzing the received signal distortions caused by the intrinsic hardware impairments. The state-of-the-art deep learning techniques such as convolutional neural network (CNN) have been adopted to classify IoT devices with high accuracy. However, deep learning-based RFFI requires input data of a fixed size. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the low SNR RFFI is rarely investigated. In this paper, the state-of-the-art transformer model is used as the classifier, which can process signals of variable length. Data augmentation is adopted to improve low SNR RFFI performance. A multi-packet inference approach is further proposed to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low SNR RFFI performance by up to 50% and multi-packet inference can further increase it by over 20%.
Generalized Fully Coherent Closed-form Receiver Design for Joint Radar and Communication System
Authors: Muhammad Zubair, Sajid Ahmed, Mohamed-Slim Alouini
Date: 2021-11-28 00:00:00
Tasks: Form

In conventional radar, the transmission of the same waveform is repeated after a predefined interval of time called pulse-repetition-interval (PRI). This technique helps to estimate the range and Doppler shift of targets and suppress clutter. In dual-function radar communication (DFRC), different waveforms are transmitted after each PRI. Thus, each waveform yields different range-side-lobe (RSL) levels at the receiver's output. As a consequence, Doppler shift estimation and clutter suppression become challenging tasks. A state-of-the-art (SOTA) method claims that if the number of waveforms is more than two, it is impossible to achieve fully coherent RSL levels with both waveforms. Therefore, this algorithm uses iterative methods to achieve as much as possible coherency and minimize the RSL levels. In contrast to that SOTA method, we proposed two novel closed-form receivers for the DFRC that yield a fully coherent response for several waveforms and suppress the RSL levels. Experimental results demonstrate that the proposed receivers achieve full coherency and the RSL levels are significantly lower than the conventional method.
Optimization of RIS Configurations for Multiple-RIS-Aided mmWave Positioning Systems based on CRLB Analysis
Authors: Yu Liu, Sheng Hong, Cunhua Pan, Yinlu Wang, Yijin Pan, Ming Chen
Date: 2021-11-28 00:00:00

Reconfigurable intelligent surface (RIS) is a promising technology for future millimeter-wave (mmWave) communication systems. However, its potential benefits of adopting RIS for high-precision positioning in mmWave systems are still less understood. In this paper, we study a multiple-RIS-aided mmWave positioning system and derive the Cram$\rm{\acute{e}}$r-Rao error bound. Based on the derived bound, we optimize the phase shift of the RISs by the particle swarm optimization (PSO) algorithm. Numerical results have demonstrated the advantages of using multiple RISs in enhancing the positioning accuracy in mmWave systems.
Resource Allocation in Laser-based Optical Wireless Cellular Networks
Authors: Ahmad Adnan Qidan, Maximo Morales-Cespedes, Taisir El-Gorashi, Jaafar M. H. Elmirghani
Date: 2021-11-27 00:00:00

Optical wireless communication provides data transmission at high speeds which can satisfy the increasing demands for connecting a massive number of devices to the Internet. In this paper, vertical-cavity surface-emitting(VCSEL) lasers are used as transmitters due to their high modulation speed and energy efficiency. However, a high number of VCSEL lasers is required to ensure coverage where each laser source illuminates a confined area. Therefore, multiple users are classified into different sets according to their connectivity. Given this point, a transmission scheme that uses blind interference alignment (BIA) is implemented to manage the interference in the laser-based network. In addition, an optimization problem is formulated to maximize the utility sum rate taking into consideration the classification of the users. To solve this problem, a decentralized algorithm is proposed where the main problem is divided into sub-problems, each can be solved independently avoiding complexity. The results demonstrate the optimality of the decentralized algorithm where a sub-optimal solution is provided. Finally, it is shown that BIA can provide high performance in laser-based networks compared with zero forcing (ZF) transmit precoding scheme.
Artificial Neural Network for Resource Allocation in Laser-based Optical wireless Networks
Authors: Ahmad Adnan Qidan, Taisir El-Gorashi1, Jaafar M. H. Elmirghani
Date: 2021-11-27 00:00:00
Tasks: Dataset Generation

Optical wireless communication offers unprecedented communication speeds that can support the massive use of the Internet on a daily basis. In indoor environments, optical wireless networks are usually multi-user multiple-input multiple-output (MU-MIMO) systems, where a high number of optical access points (APs) is required to ensure coverage. In this work, a laser-based optical wireless network is considered for serving multiple users. Moreover, blind inference alignment (BIA) is implemented to achieve a high degree of freedom (DoF) without the need for channel state information (CSI) at transmitters, which is difficult to provide in such wireless networks. Then, an objective function is defined to allocate the resources of the network taking into consideration the requirements of users and the available resources. This optimization problem can be solved through exhaustive search or distributed algorithms. However, a practical algorithm that provides immediate solutions in real time scenarios is required. In this context, an artificial neural network (ANN) model is derived in order to obtain a sub-optimal solution with low computational time. The implementation of the ANN model involves three important steps, dataset generation, offline training, and real time application. The results show that the trained ANN model provides a significant solution close to the optimal one.
Analogue Radio over Fiber aided Multi-service Communications for High Speed Trains
Authors: Yichuan Li, Salman Ghafoor, Mohammed El-Hajjar
Date: 2021-11-27 00:00:00

High speed trains (HST) have gradually become an essential means of transportation, where given our digital world, it is expected that passengers will be connected all the time. More specifically, the on-board passengers require fast mobile connections, which cannot be provided by the currently implemented cellular networks. Hence, in this article, we propose an analogue radio over fiber (A-RoF) aided multi-service network architecture for high-speed trains, in order to enhance the quality of service as well as reduce the cost of the radio access network (RAN). The proposed design can simultaneously support sub- 6GHz as well as milimeter wave (mmWave) communications using the same architecture. Explicitly, we design a photonics aided beamforming technique in order to eliminate the bulky high-speed electronic phase-shifters and the hostile broadband mmWave mixers while providing a low-cost RAN solution. Finally, a beamforming range of 180 is demonstrated with a high resolution using our proposed system.
Analogue Radio Over Fiber for Next-Generation RAN: Challenges and Opportunities
Authors: Yichuan Li, Qijie Xie, Mohammed El-Hajjar, Lajos Hanzo
Date: 2021-11-27 00:00:00

The radio access network (RAN) connects the users to the core networks, where typically digitised radio over fiber (D-RoF) links are employed. The data rate of the RAN is limited by the hardware constraints of the D-RoF-based backhaul and fronthaul. In order to break this bottleneck, the potential of the analogue radio over fiber (A-RoF) based RAN techniques are critically appraised for employment in the next-generation systems, where increased-rate massive multiple-input-multiple-output (massive-MIMO) and millimeter wave (mmWave) techniques will be implemented. We demonstrate that huge bandwidth and power-consumption cost benefits may accrue upon using A-RoF for next-generation RANs. We provide an overview of the recent A-RoF research and a performance comparison of A-RoF and D-RoF, concluding with further insights on the future potential of A-RoF.
Resource Allocation for IRS-Enabled Secure Multiuser Multi-Carrier Downlink URLLC Systems
Authors: Mohammad Naseri Tehrani, Shahrokh Farahmand
Date: 2021-11-27 00:00:00

Secure ultra-reliable low-latency communication (URLLC) has been recently investigated with the fundamental limits of finite block length (FBL) regime in mind. Analysis has revealed that when eavesdroppers outnumber BS antennas or enjoy a more favorable channel condition compared to the legitimate users, base station (BS) transmit power should increase exorbitantly to meet quality of service (QoS) constraints. Channel-induced impairments such as shadowing and/or blockage pose a similar challenge. These practical considerations can drastically limit secure URLLC performance in FBL regime. Deployment of an intelligent reflecting surface (IRS) can endow such systems with much-needed resiliency and robustness to satisfy stringent latency, availability, and reliability requirements. We address this problem and propose a joint design of IRS platform and secure URLLC network. We minimize the total BS transmit power by simultaneously designing the beamformers and artificial noise at the BS and phase-shifts at the IRS, while guaranteeing the required number of securely transmitted bits with the desired packet error probability, information leakage, and maximum affordable delay. The proposed optimization problem is non-convex and we apply block coordinate descent and successive convex approximation to iteratively solve a series of convex sub-problems instead. The proposed algorithm converges to a sub-optimal solution in a few iterations and attains substantial power saving and robustness compared to baseline schemes.
Towards Terabit LiFi Networking
Authors: Ahmad Adnan Qidan, Taisir El-Gorashi1, Jaafar M. H. Elmirghani
Date: 2021-11-27 00:00:00

Light Fidelity (Li-Fi) is a networked version of optical wireless communication (OWC), which is a strong candidate to fulfill the unprecedented increase in user-traffic expected in the near future. In OWC, a high number of optical access points (APs) is usually deployed on the ceiling of an indoor environment to serve multiple users with different demands. Despite the high data rates of OWC networks, due to the use of the optical band for data transmission, they cannot replace current radio frequency (RF) wireless networks where OWC has several issues including the small converge area of an optical AP, the lack of uplink transmission and high blockage probabilities. However, OWC has the potential to support the requirements in the next generation (6G) of wireless communications. In this context, heterogeneous optical/RF networks can be considered to overcome the limitations of OWC and RF systems, while providing a high quality of service in terms of achievable data rates and coverage. In this work, infrared lasers, vertical-cavity surface-emitting(VCSEL) lasers, are used as the key elements of optical APs for serving multiple users. Then, transmission schemes such as zero forcing (ZF) and blind interference alignment (BIA) are introduced to manage multi-user interference and maximize the sum rate of users. Moreover, a WiFi system is considered to provide uplink transmission and serve users that experience a low signal to noise ratio (SNR) from the optical system. To use the resources of the heterogeneous optical/RF network efficiently, we derive a utility-based objective function that aims to maximize the overall sum rate of the network.
The PAT model of population dynamics
Authors: Z. C. Feng, Y. Charles Li
Date: 2021-11-27 00:00:00
Tasks: model

We introduce a population-age-time (PAT) model which describes the temporal evolution of the population distribution in age. The surprising result is that the qualitative nature of the population distribution dynamics is robust with respect to the birth rate and death rate distributions in age, and initial conditions. When the number of children born per woman is 2, the population distribution approaches an asymptotically steady state of a kink shape; thus the total population approaches a constant. When the number of children born per woman is greater than 2, the total population increases without bound; and when the number of children born per woman is less than 2, the total population decreases to zero.
Replicating Monotonic Payoffs Without Oracles
Authors: Guillermo Angeris, Alex Evans, Tarun Chitra
Date: 2021-11-26 00:00:00

In this paper, we show that any monotonic payoff can be replicated using only liquidity provider shares in constant function market makers (CFMMs), without the need for additional collateral or oracles. Such payoffs include cash-or-nothing calls and capped calls, among many others, and we give an explicit method for finding a trading function matching these payoffs. For example, this method provides an easy way to show that the trading function for maintaining a portfolio where 50% of the portfolio is allocated in one asset and 50% in the other is exactly the constant product market maker (e.g., Uniswap) from first principles. We additionally provide a simple formula for the total earnings of an arbitrageur who is arbitraging against these CFMMs.
Model Risk in Credit Portfolio Models
Authors: Christian Meyer
Date: 2021-11-23 00:00:00
Tasks: All, model

Model risk in credit portfolio models is a serious issue for banks but has so far not been tackled comprehensively. We will demonstrate how to deal with uncertainty in all model parameters in an all-embracing, yet easy-to-implement way.