pwc-archive/papers-with-abstracts huggingface dataset:
With great power (prices) comes great tail pipe emissions? \\ A natural experiment of electricity prices and electric car adoption
Authors: Johannes Mauritzen
Date: 2023-04-04 00:00:00
Tasks: counterfactual

A fundemantal and unanswered question for the widely shared goal of electrifying passenger vehicles is how the price of electricity, which can vary greatly across countries and regions, affects buying behavior. I make use of a natural experiment in Norway in the period 2021-2022 when large price differences between north and south emerged to estimate the effect of electricity prices on the decision to purchase a pure battery-electric vehicle. Simple difference estimates along the border of the price zones as well as a difference-in-difference regression model suggest a significant but economically modest effect of a 2-4\% reduction in the probability of purchasing an electric vehicle in the high price zone. A counterfactual simulation suggests that there would have been about 3000 to 6000 fewer electric vehicles sold in the high-price south compared to a scenario where the south had equally low prices as in the north.
The Economic Effect of Gaining a New Qualification Later in Life
Authors: Finn Lattimore, Daniel M. Steinberg, Anna Zhu
Date: 2023-04-04 00:00:00

Pursuing educational qualifications later in life is an increasingly common phenomenon within OECD countries since technological change and automation continues to drive the evolution of skills needed in many professions. We focus on the causal impacts to economic returns of degrees completed later in life, where motivations and capabilities to acquire additional education may be distinct from education in early years. We find that completing an additional degree leads to more than \$3000 (AUD, 2019) extra income per year compared to those who do not complete additional study. For outcomes, treatment and controls we use the extremely rich and nationally representative longitudinal data from the Household Income and Labour Dynamics Australia survey (HILDA). To take full advantage of the complexity and richness of this data we use a Machine Learning (ML) based methodology for causal effect estimation. We are also able to use ML to discover sources of heterogeneity in the effects of gaining additional qualifications. For example, those younger than 45 years of age when obtaining additional qualifications tend to reap more benefits (as much as \$50 per week more) than others.
How Advance Sales can Reduce Profits: When to Buy, When to Sell, and What Price to Charge
Authors: Amihai Glazer, Refael Hassin, Irit Nowik
Date: 2019-12-05 00:00:00

A consumer who wants to consume a good in a particular period may nevertheless attempt to buy it earlier if he is concerned that in delaying he would find the good already sold. This paper considers a model in which the good may be offered in two periods; the period in which all consumers most value the good (period 2), and an earlier period (period 1). Examining the profit-maximizing strategy of the firm under unbounded demand, we find that even with no cost of making the product available early, the firm does not profit, and usually loses, by making the product available early. Interestingly, the price that maximizes profits induces all arrivals to occur early, or all arrivals to occur late, depending on the parameters. The firm would not set a price which induces consumers to arrive in both periods. In particular, if the firm controls the penalty for arriving early, then it should set a high penalty so that no one arrives early. The Nash equilibrium behavior of consumers, when deciding if and when to arrive is more complicated than one may suppose, and can generate some unexpected behavior. For example, when there is unbounded demand, most potential consumers decide not to arrive at all. Additionally, the arrival rate may decline with the surplus a person gets from buying the good. Surprisingly, we find that an increase in the number of units for sale increases the number of consumers who arrive early. Moreover, we find that the profit-maximizing price increases with the number of units offered for sale. This too is unexpected as an increase in supply often results in price reduction. In our case, an increase in the number of units on sale also increases demand, and the seller may profit by increasing the price. In the single-unit case, we give closed solutions for the equilibrium customer behavior and profit-maximizing firm strategy and conduct sensitivity analysis.
Inequality and Growth: A Two-Player Dynamic Game with Production and Appropriation
Authors: Julio Huato
Date: 2023-04-04 00:00:00

This paper models a two-agent economy with production and appropriation as a noncooperative dynamic game, and determines its closed-form Markovian Nash equilibrium. The analysis highlights the para-metric conditions that tip the economy from a nonaggressive or "co-operative" equilibrium to outright distributional conflict. The model includes parameters that capture the role of appropriation technology and destructiveness. The full dynamic implications of the game are yet to be explored, but the model offers a promising general framework for thinking about different technological and economic conditions as more or less conducive to cooperation or distributional conflict.
TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio
Authors: Anurag Kumar, Ke Tan, Zhaoheng Ni, Pranay Manocha, Xiaohui Zhang, Ethan Henderson, Buye Xu
Date: 2023-04-04 00:00:00

Measuring quality and intelligibility of a speech signal is usually a critical step in development of speech processing systems. To enable this, a variety of metrics to measure quality and intelligibility under different assumptions have been developed. Through this paper, we introduce tools and a set of models to estimate such known metrics using deep neural networks. These models are made available in the well-established TorchAudio library, the core audio and speech processing library within the PyTorch deep learning framework. We refer to it as TorchAudio-Squim, TorchAudio-Speech QUality and Intelligibility Measures. More specifically, in the current version of TorchAudio-squim, we establish and release models for estimating PESQ, STOI and SI-SDR among objective metrics and MOS among subjective metrics. We develop a novel approach for objective metric estimation and use a recently developed approach for subjective metric estimation. These models operate in a ``reference-less" manner, that is they do not require the corresponding clean speech as reference for speech assessment. Given the unavailability of clean speech and the effortful process of subjective evaluation in real-world situations, such easy-to-use tools would greatly benefit speech processing research and development.
Beam Alignment with an Intelligent Reflecting Surface for Integrated Sensing and Communication
Authors: Florian Muhr, Lorenzo Zaniboni, Saeid K. Dehkordi, Fernando Pedraza Nieto, Giuseppe Caire
Date: 2023-04-04 00:00:00
Tasks: Integrated sensing and communication, parameter estimation

In a typical communication system, in order to maintain a desired SNR level, initial beam alignment (BA) must be established prior to data transmission. In a setup where a Base Station (BS) Tx sends data via a digitally modulated waveform, we propose a User Equipment (UE) enhanced with a Hybrid Intelligent Reflecting Surface (HIRS) to aid beam alignment. A novel multi-slot estimation scheme is developed that alleviates the restrictions imposed by the Hybrid Digital-Analog (HDA) architecture of the HIRS and the BS. To demonstrate the effectiveness of the proposed BA scheme, we derive the CRLB of the parameter estimation scheme and provide numerical results.
Traveling Wave Method for Event Localization and Characterization of Power Transmission Lines
Authors: Marko Hudomalj, Andrej Trost, Andrej Čampa
Date: 2023-04-04 00:00:00

Traveling wave theory is deployed today to improve the monitoring of transmission lines in electrical power grids. Most traveling wave methods require prior knowledge of the wave propagation of the transmission line, which is a major source of error as the value changes during the operation of the line. To improve the localization of events on transmission lines, we propose a new online localization method that simultaneously determines the frequency-dependent wave propagation characteristic from the traveling wave measurements of the event. Compared to conventional methods, this is achieved with one additional traveling wave measurement, but the method can still be applied in different measurement setups. We have derived the method based on the complex continuous wavelet transform. The accuracy of the method is evaluated in a simulation with a frequency-dependent transmission line model of the IEEE 39-bus system. The method was developed independently of the type of event and evaluated in test setups considering different lengths of the monitored line, line types and event locations. The localization accuracy is compared with existing online methods and analyzed with regard to the characterization capabilities. The method has a high relative localization accuracy in the range of 0.1\,\% under different test conditions.
Risk Sensitive Filtering with Randomly Delayed Measurements
Authors: Ranjeet Kumar Tiwari, Shovan Bhaumik
Date: 2023-04-04 00:00:00

Conventional Bayesian estimation requires an accurate stochastic model of a system. However, this requirement is not always met in many practical cases where the system is not completely known or may differ from the assumed model. For such a system, we consider a scenario where the measurements are transmitted to a remote location using a common communication network and due to which, a delay is introduced while receiving the measurements. The delay that we consider here is random and one step maximum at a given time instant. For such a scenario, this paper develops a robust estimator for a linear Gaussian system by minimizing the risk sensitive error criterion that is defined as an expectation of the accumulated exponential quadratic error. The criteria for the stability of the risk sensitive Kalman filter (RSKF) are derived and the results are used to study the stability of the developed filter. Further, it is assumed that the latency probability related to delay is not known and it is estimated by maximizing the likelihood function. Simulation results suggest that the proposed filter shows acceptable performance under the nominal conditions, and it performs better than the Kalman filter for randomly delayed measurements and the RSKF in presence of both the model uncertainty and random delays.
Modeling and Estimation for Systems with Randomly Delayed Measurements and Packet Dropouts
Authors: Ranjeet Kumar Tiwari, Shovan Bhaumik
Date: 2023-04-04 00:00:00

A networked system often uses a shared communication network to transmit the measurements to a remotely located estimation center. Due to the limited bandwidth of the channel, a delay may appear while receiving the measurements. This delay can be arbitrary step random, and packets are sometimes dropped during transmission as it exceeds a certain permissible number. In this paper, such measurements are modeled with the Poisson distribution, which allows the user to determine the maximum delay the system might suffer. When the measurement delay exceeds the permissible number, the packet dropout happens. Based on the proposed model, we solve the problem by assuming that the prior and posterior densities of states are Gaussian and derive the expression of the estimated state and the error covariance. Later, relaxing the Gaussian assumption for densities, we propose a solution with the help of the sequential Monte Carlo (SMC) approach. The proposed SMC method divides the set of particles into several groups, where each group supports the possibility that the received measurement is delayed by a certain number of steps. The strength of an individual group is determined by the probability of a measurement being delayed with the same number of steps that the group represents. This approach estimates the states and also assesses the amount of delay from the received measurements. Finally, the developed estimators are implemented on two nonlinear estimation problems, and the simulation results are compared. The proposed SMC approach shows better results compared to the designed Gaussian delay filters and existing particle filters with delay.
Predictive Resource Allocation for URLLC using Empirical Mode Decomposition
Authors: Chandu Jayawardhana, Thushan Sivalingam, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho
Date: 2023-04-04 00:00:00
Tasks: Management, Prediction

Effective resource allocation is a crucial requirement to achieve the stringent performance targets of ultra-reliable low-latency communication (URLLC) services. Predicting future interference and utilizing it to design efficient interference management algorithms is one way to allocate resources for URLLC services effectively. This paper proposes an empirical mode decomposition (EMD) based hybrid prediction method to predict the interference and allocate resources for downlink based on the prediction results. EMD is used to decompose the past interference values faced by the user equipment. Long short-term memory and auto-regressive integrated moving average methods are used to predict the decomposed components. The final predicted interference value is reconstructed using individual predicted values of decomposed components. It is found that such a decomposition-based prediction method reduces the root mean squared error of the prediction by $20 - 25\%$. The proposed resource allocation algorithm utilizing the EMD-based interference prediction was found to meet near-optimal allocation of resources and correspondingly results in $2-3$ orders of magnitude lower outage compared to state-of-the-art baseline prediction algorithm-based resource allocation.
Antenna Array Structures for Enhanced Cluster Index Modulation
Authors: Mahmoud Raeisi, Asil Koc, Ibrahim Yildirim, Ertugrul Basar, Tho Le-Ngoc
Date: 2023-04-04 00:00:00

This paper investigates the effect of various antenna array structures, i.e., uniform linear array (ULA), uniform rectangular array (URA), uniform circular array (UCA), and concentric circular array (CCA), on cluster index modulation (CIM) enabled massive multiple-input multiple-output (mMIMO) millimeter-wave (mmWave) communications systems. As the CIM technique indexes spatial clusters to convey additional information bits, the different radiation characteristics caused by different array structures can significantly affect system performance. By analyzing the effects of array characteristics such as radiation pattern, array directivity, half-power beam width (HPBW), and radiation side lobes on bit error rate (BER) performance, we reveal that URA achieves better error performance than its counterparts in a CIM-enabled mmWave system. We demonstrate that narrower beams alone cannot guarantee better BER performance in a CIM-based system. Instead, other radiation characteristics, especially radiation side lobes, can significantly influence system performance by entailing extra interference in the non-intended directions. Illustrative results show that URA owes its superiority to its lower side lobes. We also propose an algorithm to implement fixed phase shifters (FPS) as a hardware-efficient (HE) analog network structure (beamformer/combiner) to reduce cost and energy consumption in mmWave systems and investigate the effect of a non-ideal analog network on the BER performance for different array structures. It is demonstrated that HE systems with a few FPSs can achieve similar BER performance compared to the optimum (OP) analog network structure.
High-rate Reliable Communication using Multi-hop and Mesh THz/FSO Networks
Authors: Praveen Kumar Singya, Behrooz Makki, Antonio D'Errico, Mohamed-Slim Alouini
Date: 2023-04-04 00:00:00

In this work, we consider multi-hop and mesh hybrid teraHertz/free-space optics (THz/FSO)-based backhaul networks for high data-rate communications. The results are presented for the cases with both out-band integrated access and backhaul (IAB) and non-IAB based communication setups. We consider different deployments of the THz and FSO networks and consider both switching and combining methods between the hybrid FSO/THz links. We study the impact of atmospheric turbulence, atmospheric attenuation, and the pointing error on the FSO communication. The THz communication suffers from small scale fading, path-loss, and the misalignment error. Finally, we evaluate the effects of atmospheric attenuation/path-loss, pointing/misalignment error, small-scale fading, atmospheric turbulence, number of antennas, number of user equipments, number of hops, and the threshold data-rates on the performance of considered systems. As we show, with different network deployments and switching/combining methods, the hybrid implementation of the THz/FSO links improves the network reliability significantly.
OTFDM: A Novel 2D Modulation Waveform Modeling Dot-product Doubly-selective Channel
Authors: Yihua Ma, Zhifeng Yuan, Yu Xin, Jiang Hua, Guanghui Yu, Jin Xu, Liujun Hu
Date: 2023-04-04 00:00:00

Recently, a two-dimension (2D) modulation waveform of orthogonal time-frequency-space (OTFS) has been a popular 6G candidate to replace existing orthogonal frequency division multiplexing (OFDM). The extensive OTFS researches help to make both the advantages and limitations of OTFS more and more clear. The limitations are not easy to overcome as they come from OTFS on-grid 2D convolution channel model. Instead of solving OTFS inborn challenges, this paper proposes a novel 2D modulation waveform named orthogonal time-frequency division multiplexing (OTFDM). OTFDM uses a 2D dot-product channel model to cope with doubly-selectivity. Compared with OTFS, OTFDM supports grid-free channel delay and Doppler and gains a simple and efficient 2D equalization. The concise dot-division equalization can be easily combined with MIMO. The simulation result shows that OTFDM is able to bear high mobility and greatly outperforms OFDM in doubly-selective channel.
Equilibrium with Heterogeneous Information Flows
Authors: Scott Robertson
Date: 2023-04-03 00:00:00

We study a continuous time economy where throughout time, insiders receive private signals regarding the risky assets' terminal payoff. We prove existence of a partial communication equilibrium where, at each private signal time, the public receives a signal of the same form as the associated insider, but of lower quality. This causes a jump in both the public information flow and equilibrium asset price. The resultant markets, while complete between each jump time, are incomplete over each jump. After establishing equilibrium for a finite number of private signal times, we consider the limit as the private signals become more and more frequent. Under appropriate scaling we prove convergence of the public filtration to the natural filtration generated by both the fundamental factor process $X$ and a continuous time process $J$ taking the form $J_t = X_1 + Y_t$ where $X_1$ is the terminal payoff and $Y$ an independent Gaussian process. This coincides with the filtration considered in 'Additional Utility of Insiders with Imperfect Dynamical Information' (Corcuera, et al. Finance & Stochastics 2004). However, while therein the filtration was exogenously assumed to be that of an insider who observes a private signal flow, here it arises endogenously as the public filtration when there are a large number of insiders receiving signals throughout time.
naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python
Authors: Gavin Mischler, Vinay Raghavan, Menoua Keshishian, Nima Mesgarani
Date: 2023-04-04 00:00:00

Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, and analysis tools which operate on that data structure. The package removes many of the complications associated with this domain, such as varying trial durations and multi-modal stimuli, and provides a general-purpose analysis framework that interfaces easily with existing toolboxes used in the field.
Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques
Authors: Krish Desai
Date: 2023-03-29 00:00:00
Tasks: EEG, Ensemble Learning

Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons. PD patients report experiencing tremor, rigidity, visual impairment, bradykinesia, and several cognitive deficits. Although Electroencephalography (EEG) indicates abnormalities in PD patients, one major challenge is the lack of a consistent, accurate, and systemic biomarker for PD in order to closely monitor the disease with therapeutic treatments and medication. In this study, we collected Electroencephalographic data from 15 PD patients and 16 Healthy Controls (HC). We first preprocessed every EEG signal using several techniques and extracted relevant features using many feature extraction algorithms. Afterwards, we applied several machine learning algorithms to classify PD versus HC. We found the most significant metrics to be achieved by the Random Forest ensemble learning approach, with an accuracy, precision, recall, F1 score, and AUC of 97.5%, 100%, 95%, 0.967, and 0.975, respectively. The results of this study show promise for exposing PD abnormalities using EEG during clinical diagnosis, and automating this process using signal processing techniques and ML algorithms to evaluate the difference between healthy individuals and PD patients.
Super-performance: sampling, planning, and ecological information
Authors: Bradly Alicea
Date: 2023-03-22 00:00:00

The connection between active perception and the limits of performance provide a path to understanding naturalistic behavior. We can take a comparative cognitive modeling perspective to understand the limits of this performance and the existence of superperformance. We will discuss two categories that are hypothesized to originate in terms of coevolutionary relationships and evolutionary trade offs: supersamplers and superplanners. Supersamplers take snapshots of their sensory world at a very high sampling rate. Examples include flies (vision) and frogs (audition) with ecological specializations. Superplanners internally store information to evaluate and act upon multiple features of spatiotemporal environments. Slow lorises and turtles provide examples of superplanning capabilities. The Gibsonian Information (GI) paradigm is used to evaluate sensory sampling and planning with respect to direct perception and its role in capturing environmental information content. By contrast, superplanners utilize internal models of the environment to compensate for normal rates of sensory sampling, and this relationship often exists as a sampling/planning tradeoff. Supersamplers and superplanners can exist in adversarial relationships, or longer-term as coevolutionary relationships. Moreover, the tradeoff between sampling and planning capacity can break down, providing relativistic regimes. We can apply the principles of superperformance to human augmentation technologies.