pwc-archive/papers-with-abstracts huggingface dataset:
The Effect of Punishment and Reward on Cooperation in a Prisoners' Dilemma Game
Authors: Alexander Kangas
Date: 2023-09-01 00:00:00
Tasks: Form

This work studies the effect of incentives (in the form of punishment and reward) on the equilibrium fraction of cooperators and defectors in an iterated n-person prisoners' dilemma game. With a finite population of players employing a strategy of nice tit-for-tat or universal defect, an equilibrium fraction of each player-type can be identified from linearized payoff functions. Incentives take the form of targeted and general punishment, and targeted and general reward. The primary contribution of this work is in clearly articulating the design and marginal effect of these incentives on cooperation. Generalizable results indicate that while targeted incentives have the potential to substantially reduce but never entirely eliminate defection, they exhibit diminishing marginal effectiveness. General incentives on the other hand have the potential to eliminate all defection from the population of players. Applications to policy are briefly considered.
Regret-Minimizing Project Choice
Authors: Yingni Guo, Eran Shmaya
Date: 2023-09-01 00:00:00

An agent observes the set of available projects and proposes some, but not necessarily all, of them. A principal chooses one or none from the proposed set. We solve for a mechanism that minimizes the principal's worst-case regret. We compare the single-project environment in which the agent can propose only one project with the multiproject environment in which he can propose many. In both environments, if the agent proposes one project, it is chosen for sure if the principal's payoff is sufficiently high; otherwise, the probability that it is chosen decreases in the agent's payoff. In the multiproject environment, the agent's payoff from proposing multiple projects equals his maximal payoff from proposing each project alone. The multiproject environment outperforms the single-project one by providing better fallback options than rejection and by delivering this payoff to the agent more efficiently.
Message Passing Based Block Sparse Signal Recovery for DOA Estimation Using Large Arrays
Authors: Yiwen Mao, Dawei Gao, Qinghua Guo, Ming Jin
Date: 2023-09-01 00:00:00

This work deals with directional of arrival (DOA) estimation with a large antenna array. We first develop a novel signal model with a sparse system transfer matrix using an inverse discrete Fourier transform (DFT) operation, which leads to the formulation of a structured block sparse signal recovery problem with a sparse sensing matrix. This enables the development of a low complexity message passing based Bayesian algorithm with a factor graph representation. Simulation results demonstrate the superior performance of the proposed method.
A Spatial Sigma-Delta Approach to Mitigation of Power Amplifier Distortions in Massive MIMO Downlink
Authors: Yatao Liu, Mingjie Shao, Wing-Kin Ma
Date: 2023-09-01 00:00:00

In massive multiple-input multiple-output (MIMO) downlink systems, the physical implementation of the base stations (BSs) requires the use of cheap and power-efficient power amplifiers (PAs) to avoid high hardware cost and high power consumption. However, such PAs usually have limited linear amplification ranges. Nonlinear distortions arising from operation beyond the linear amplification ranges can significantly degrade system performance. Existing approaches to handle the nonlinear distortions, such as digital predistortion (DPD), typically require accurate knowledge, or acquisition, of the PA transfer function. In this paper, we present a new concept for mitigation of the PA distortions. Assuming a uniform linear array (ULA) at the BS, the idea is to apply a Sigma-Delta ($\Sigma \Delta$) modulator to spatially shape the PA distortions to the high-angle region. By having the system operating in the low-angle region, the received signals are less affected by the PA distortions. To demonstrate the potential of this spatial $\Sigma \Delta$ approach, we study the application of our approach to the multi-user MIMO-orthogonal frequency division modulation (OFDM) downlink scenario. A symbol-level precoding (SLP) scheme and a zero-forcing (ZF) precoding scheme, with the new design requirement by the spatial $\Sigma \Delta$ approach being taken into account, are developed. Numerical simulations are performed to show the effectiveness of the developed $\Sigma \Delta$ precoding schemes.
Evaluation of onboard sensors for track geometry monitoring against conventional track recording measurements
Authors: Hengcheng Zhang, Zhan Yie Chin, Pietro Borghesani, James Pitt, Michael E. Cholette
Date: 2023-09-01 00:00:00

The main objective of this paper is to assess the estimation of track condition parameters using onboard micro-electro-mechanical-system (MEMS) accelerometers. A prototype of an onboard data acquisition system was designed and installed on a track recording car (TRC) and a measurement campaign was conducted on an extensive portion of the Brisbane Suburban railway network. Comparison of the accelerometer-based results vs TRC recordings have shown that accelerometers installed on the bogie are the best compromise between proximity to the source and insensitivity to impulsive noise. It was found that two vertical bogie accelerometers (left and right) provide a good quantitative estimate of vertical alignment and that strong correlations with TRC measurements exist for lateral MEMS accelerometer measurements (horizontal alignment). These findings suggest that two bogie MEMS accelerometers with vertical and lateral measurement axes are effective in estimating geographical distributions of vertical/horizontal alignment.
Concept for an Automatic Annotation of Automotive Radar Data Using AI-segmented Aerial Camera Images
Authors: Marcel Hoffmann, Sandro Braun, Oliver Sura, Michael Stelzig, Christian Schüßler, Knut Graichen, Martin Vossiek
Date: 2023-09-01 00:00:00
Tasks: Position

This paper presents an approach to automatically annotate automotive radar data with AI-segmented aerial camera images. For this, the images of an unmanned aerial vehicle (UAV) above a radar vehicle are panoptically segmented and mapped in the ground plane onto the radar images. The detected instances and segments in the camera image can then be applied directly as labels for the radar data. Owing to the advantageous bird's eye position, the UAV camera does not suffer from optical occlusion and is capable of creating annotations within the complete field of view of the radar. The effectiveness and scalability are demonstrated in measurements, where 589 pedestrians in the radar data were automatically labeled within 2 minutes.
Ground Truth Generation Algorithm for Medium-Frequency R-Mode Skywave Detection
Authors: Suhui Jeong, Pyo-Woong Son
Date: 2023-09-01 00:00:00

With the advancement of transportation vehicles, the importance and utility of navigation systems providing positioning, navigation, and timing (PNT) information have been increasing. Global navigation satellite systems (GNSS) are widely used navigation systems, but they are vulnerable to radio frequency interference (RFI), resulting in disruptions of satellite navigation signals. Recognizing this limitation, extensive research is being conducted on alternative navigation systems. In the maritime industry, ongoing research focuses on a groundbased integrated navigation system called R-Mode. R-Mode utilizes medium frequency (MF) differential GNSS (DGNSS) and very high-frequency data exchange system (VDES) signals as ranging signals for positioning and incorporates the existing ground-based navigation system known as enhanced long-range navigation (eLoran). However, MF R-Mode, which uses MF DGNSS signals for positioning, exhibits significant performance differences between daytime and nighttime due to skywave interference caused by signals reflecting off the ionosphere. In this study, we propose a skywave ground truth generation algorithm that is crucial for studying mitigation methods for MF R-Mode skywave interference. Furthermore, we demonstrate the proposed algorithm using field-test data.
Empirical Modeling of Variance in Medium Frequency R-Mode Time-of-Arrival Measurements
Authors: Jaewon Yu, Pyo-Woong Son
Date: 2023-09-01 00:00:00

The R-Mode system, an advanced terrestrial integrated navigation system, is designed to address the vulnerabilities of global navigation satellite systems (GNSS) and explore the potential of a complementary navigation system. This study aims to enhance the accuracy of performance simulation for the medium frequency (MF) R-Mode system by modeling the variance of time-of-arrival (TOA) measurements based on actual data. Drawing inspiration from the method used to calculate the standard deviation of time-of-reception (TOR) measurements in Loran, we adapted and applied this approach to the MF R-Mode system. Data were collected from transmitters in Palmi and Chungju, South Korea, and the parameters for modeling the variance of TOA were estimated.
Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net
Authors: Hoang Van Truong, Nguyen Chi Hieu, Pham Ngoc Giao, Nguyen Xuan Phong
Date: 2021-06-29 00:00:00
Proceeding: Journal of ICT Research and Applications 2021 6
Tasks: Anomaly Detection

Anomaly detection in the sound from machines is an important task in machine monitoring. An autoencoder architecture based on the reconstruction error using a log-Mel spectrogram feature is a conventional approach for this domain. However, because of the non-stationary nature of some sounds from the target machine, such a conventional approach does not perform well in those circumstances. In this paper, we propose a novel approach regarding the choice of used features and a new auto-encoder architecture. We created the Mixed Feature, which is a mixture of different sound representations, and a new deep learning method called Fully-Connected U-Net, a form of autoencoder architecture. With experiments on the same dataset as the baseline system, using the same architecture for all types of machines, the experimental results showed that our methods outperformed the baseline system in terms of the AUC and pAUC evaluation metrics. The optimized model achieved 83.38% AUC and 64.51% pAUC on average overall machine types on the developed dataset and outperformed the published baseline by 13.43% AUC and 8.13% pAUC.
Approximate Bayesian computation for Markovian binary trees in phylogenetics
Authors: Mingqi He, Sophie Hautphenne, Yao-ban Chan
Date: 2023-09-01 00:00:00

Phylogenetic trees describe the relationships between species in the evolutionary process, and provide information about the rates of diversification. To understand the mechanisms behind macroevolution, we consider a class of multitype branching processes called Markovian binary trees (MBTs). MBTs allow for trait-based variation in diversification rates, and provide a flexible and realistic probabilistic model for phylogenetic trees. We develop an approximate Bayesian computation (ABC) scheme to infer the rates of MBT parameters by exploiting the information in the shapes of phylogenetic trees. We evaluate the accuracy of this inference method using simulation studies, and find that our method is able to detect variation in the diversification rates, with accuracy comparable to, and generally better than, likelihood-based methods. In an application to a real-life phylogeny of squamata, we reinforce conclusions drawn from earlier studies, in particular supporting the existence of ovi-/viviparity transitions in both directions. Our method demonstrates the potential for more complex models of evolution to be employed in phylogenetic inference, in conjunction with likelihood-free schemes.
Closed-loop experiments and brain machine interfaces with multiphoton microscopy
Authors: Riichiro Hira
Date: 2023-08-31 00:00:00

In the field of neuroscience, the importance of constructing closed-loop experimental systems has increased in conjunction with technological advances in measuring and controlling neural activity in live animals. This paper provides an overview of recent technological advances in the field, focusing on closed-loop experimental systems where multiphoton microscopy (the only method capable of recording and controlling targeted population activity of neurons at a single-cell resolution in vivo) works through real-time feedback. Specifically, we present some examples of brain machine interfaces (BMIs) using in vivo two-photon calcium imaging and discuss applications of two-photon optogenetic stimulation and adaptive optics to real-time BMIs. We also consider conditions for realizing future optical BMIs at the synaptic level, and their possible roles in understanding the computational principles of the brain.
Building and Managing a Tropical Fish Facility: A Do-It-Yourself Guide
Authors: Claudius F. Kratochwil, Muktai Kuwalekar, Jan Haege, Nidal Karagic
Date: 2023-09-01 00:00:00

At the core of most research in zoological disciplines, ranging from developmental biology to genetics to behavioral biology, is the ability to keep animals in captivity. While facilities for traditional model organisms often benefit from well-established designs, construction of a facility for less commonly studied organisms can present a challenge. Here, we detail the process of designing, constructing, and operating a specialized 10,000-liter aquatic facility dedicated to housing cichlid fishes for research purposes. The facility, comprising 42 aquaria capable of division into up to 126 compartments, a flow-through rack for juveniles, egg tumblers for eggs and embryos, and a microinjection setup, provides a comprehensive environment for all life stages of cichlid fishes. We anticipate that a similar design can be also used also for other tropical teleost fishes. This resource is designed to promote increased efficiency and success in cichlid fish breeding and research, thereby offering significant insights for aquatic research labs seeking to build or optimize their own infrastructures.
QuanAnts Machine: A Quantum Algorithm for Biomarker Discovery
Authors: Phuong-Nam Nguyen
Date: 2023-05-10 00:00:00

The discovery of biomarker sets for a targeted pathway is a challenging problem in biomedical medicine, which is computationally prohibited on classical algorithms due to the massive search space. Here, I present a quantum algorithm named QuantAnts Machine to address the task. The proposed algorithm is a quantum analog of the classical Ant Colony Optimization (ACO). We create the mixture of multi-domain from genetic networks by representation theory, enabling the search of biomarkers from the multi-modality of the human genome. Although the proposed model can be generalized, we investigate the RAS-mutational activation in this work. To the end, QuantAnts Machine discovers rarely-known biomarkers in clinical-associated domain for RAS-activation pathway, including COL5A1, COL5A2, CCT5, MTSS1 and NCAPD2. Besides, the model also suggests several therapeutic-targets such as JUP, CD9, CD34 and CD74.
Mathematical models of Plasmodium vivax transmission: a scoping review
Authors: Md Nurul Anwar, Lauren Smith, Angela Devine, Somya Mehra, Camelia R. Walker, Elizabeth Ivory, Eamon Conway, Ivo Mueller, James M. McCaw, Jennifer A. Flegg, Roslyn I. Hickson
Date: 2023-09-01 00:00:00

Plasmodium vivax is one of the most geographically widespread malaria parasites in the world due to its ability to remain dormant in the human liver as hypnozoites and subsequently reactivate after the initial infection (i.e. relapse infections). More than 80% of P. vivax infections are due to hypnozoite reactivation. Mathematical modelling approaches have been widely applied to understand P. vivax dynamics and predict the impact of intervention outcomes. In this article, we provide a scoping review of mathematical models that capture P. vivax transmission dynamics published between January 1988 and May 2023 to provide a comprehensive summary of the mathematical models and techniques used to model P. vivax dynamics. We aim to assist researchers working on P. vivax transmission and other aspects of P. vivax malaria by highlighting best practices in currently published models and highlighting where future model development is required. We provide an overview of the different strategies used to incorporate the parasite's biology, use of multiple scales (within-host and population-level), superinfection, immunity, and treatment interventions. In most of the published literature, the rationale for different modelling approaches was driven by the research question at hand. Some models focus on the parasites' complicated biology, while others incorporate simplified assumptions to avoid model complexity. Overall, the existing literature on mathematical models for P. vivax encompasses various aspects of the parasite's dynamics. We recommend that future research should focus on refining how key aspects of P. vivax dynamics are modelled, including spatial heterogeneity in exposure risk, the accumulation of hypnozoite variation, the interaction between P. falciparum and P. vivax, acquisition of immunity, and recovery under superinfection.
GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation
Authors: Avigail Taylor, Valentine M Macaulay, Anand K Maurya, Matthieu J Miossec, Francesca M Buffa
Date: 2023-08-31 00:00:00

Summary: GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarisation and visualisation tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to identify sets of genes putatively driving multiple enrichments and to explore gene-level quantitative data first used to identify input genes. Further, GeneFEAST can compare FEA results from multiple studies, making it possible, for example, to highlight patterns of gene expression amongst genes commonly differentially expressed in two sets of conditions, and giving rise to shared enrichments under those conditions. GeneFEAST offers a novel, effective way to address the complexities of linking up many overlapping FEA results to their underlying genes and data, advancing gene-centric hypotheses, and providing pivotal information for downstream validation experiments. Availability: GeneFEAST is available at https://github.com/avigailtaylor/GeneFEAST Contact: avigail.taylor@well.ox.ac.uk
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
Authors: Kelvin J. L. Koa, Yunshan Ma, Ritchie Ng, Tat-Seng Chua
Date: 2023-08-18 00:00:00
Tasks: Prediction, regression, Stock Prediction, Stock Price Prediction

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem also gets progressively harder with the introduction of the target price sequence, which also contains stochastic noise and reduces generalizability at test-time. To tackle these issues, we combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction through a stochastic generative process. The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data. Our Diffusion-VAE (D-Va) model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance. More importantly, the multi-step outputs can also allow us to form a stock portfolio over the prediction length. We demonstrate the effectiveness of our model outputs in the portfolio investment task through the Sharpe ratio metric and highlight the importance of dealing with different types of prediction uncertainties.
Chance or Chaos? Fractal geometry aimed to inspect the nature of Bitcoin
Authors: Esther Cabezas-Rivas, Felipe Sánchez-Coll, Isaac Tormo Xaixo
Date: 2023-09-01 00:00:00

The aim of this paper is to analyse the Bitcoin in order to shed some light on its nature and behaviour. We select 9 cryptocurrencies that account for almost 75\% of total market capitalisation and compare their evolution with that of a wide variety of traditional assets: commodities with spot and futures contracts, treasury bonds, stock indices, growth and value stocks. Fractal geometry will be applied to carry out a careful statistical analysis of the performance of the Bitcoin returns. As a main conclusion, we have detected a high degree of persistence in its prices, which decreases the efficiency but increases its predictability. Moreover, we observe that the underlying technology influences price dynamics, with fully decentralised cryptocurrencies being the only ones to exhibit self-similarity features at any time scale.
High Spectral Spatial Resolution Synthetic HyperSpectral Dataset form multi-source fusion
Authors: Yajie Sun, Ali Zia, Jun Zhou
Date: 2023-06-25 00:00:00
Tasks: Decision Making, Form

This research paper introduces a synthetic hyperspectral dataset that combines high spectral and spatial resolution imaging to achieve a comprehensive, accurate, and detailed representation of observed scenes or objects. Obtaining such desirable qualities is challenging when relying on a single camera. The proposed dataset addresses this limitation by leveraging three modalities: RGB, push-broom visible hyperspectral camera, and snapshot infrared hyperspectral camera, each offering distinct spatial and spectral resolutions. Different camera systems exhibit varying photometric properties, resulting in a trade-off between spatial and spectral resolution. RGB cameras typically offer high spatial resolution but limited spectral resolution, while hyperspectral cameras possess high spectral resolution at the expense of spatial resolution. Moreover, hyperspectral cameras themselves employ different capturing techniques and spectral ranges, further complicating the acquisition of comprehensive data. By integrating the photometric properties of these modalities, a single synthetic hyperspectral image can be generated, facilitating the exploration of broader spectral-spatial relationships for improved analysis, monitoring, and decision-making across various fields. This paper emphasizes the importance of multi-modal fusion in producing a high-quality synthetic hyperspectral dataset with consistent spectral intervals between bands.