pwc-archive/papers-with-abstracts huggingface dataset:
Evolutionary games and spatial periodicity
Authors: Te Wu, Feng Fu, Long Wang
Date: 2022-09-17 00:00:00

We establish a theoretical framework to address evolutionary dynamics of spatial games under strong selection. As the selection intensity tends to infinity, strategy competition unfolds in the deterministic way of winners taking all. We rigorously prove that the evolutionary process soon or later either enters a cycle and from then on repeats the cycle periodically, or stabilizes at some state almost everywhere. This conclusion holds for any population graph and a large class of finite games. This framework suffices to reveal the underlying mathematical rationale for the kaleidoscopic cooperation of Nowak and May's pioneering work on spatial games: highly symmetric starting configuration causes a very long transient phase covering a large number of extremely beautiful spatial patterns. For all starting configurations, spatial patterns transit definitely over generations, so cooperators and defectors persist definitely. This framework can be extended to explore games including the snowdrift game, the public goods games (with or without loner, punishment), and repeated games on graphs. Aspiration dynamics can also be fully addressed when players deterministically switch strategy for unmet aspirations by virtue of our framework. Our results have potential implications for exploring the dynamics of a large variety of spatially extended systems in biology and physics.
Efficient Climate Simulation via Machine Learning Method
Authors: Xin Wang, Wei Xue, Yilun Han, Guangwen Yang
Date: 2022-08-15 00:00:00

Hybrid modeling combining data-driven techniques and numerical methods is an emerging and promising research direction for efficient climate simulation. However, previous works lack practical platforms, making developing hybrid modeling a challenging programming problem. Furthermore, the lack of standard data sets and evaluation metrics may hamper researchers from comprehensively comparing various algorithms under a uniform condition. To address these problems, we propose a framework called NeuroClim for hybrid modeling under the real-world scenario, a basic setting to simulate the real climate that we live in. NeuroClim consists of three parts: (1) Platform. We develop a user-friendly platform NeuroGCM for efficiently developing hybrid modeling in climate simulation. (2) Dataset. We provide an open-source dataset for data-driven methods in hybrid modeling. We investigate the characteristics of the data, i.e., heterogeneity and stiffness, which reveals the difficulty of regressing climate simulation data; (3) Metrics. We propose a methodology for quantitatively evaluating hybrid modeling, including the approximation ability of machine learning models and the stability during simulation. We believe that NeuroClim allows researchers to work without high level of climate-related expertise and focus only on machine learning algorithm design, which will accelerate hybrid modeling research in the AI-Climate intersection. The codes and data are released at https://github.com/x-w19/NeuroClim.
Precision measurement of the return distribution property of the Chinese stock market index
Authors: Peng Liu, Yanyan Zheng
Date: 2022-09-18 00:00:00

This paper systematically conducts an analysis of the composite index 1-min datasets over the 17-year period (2005-2021) for both the Shanghai and Shenzhen stock exchanges. To reveal the difference between the Chinese and the mature stock markets, here we precisely measure the property of return distribution of composite index over the time scale $\Delta t$ ranging from 1 min up to almost 4,000 min. The main findings are as follows. (1) Return distribution presents a leptokurtic, fat-tailed, and almost symmetrical shape, which is similar to that of mature markets. (2) The central part of return distribution is well described by the symmetrical L\'{e}vy $\alpha$-stable process with a stability parameter comparable with the value of about 1.4 extracted in the U.S. stock market. (3) Return distribution can be well described by the student's t-distribution within a wider return range than the L\'{e}vy $\alpha$-stable distribution. (4) Distinctively, the stability parameter shows a potential change when $\Delta t$ increases, and thus a crossover region at 15 $< \Delta t <$ 60 min is observed. This is different from the finding in the U.S. stock market where a single value of about 1.4 holds over 1 $\le \Delta t \le$ 1,000 min. (5) The tail distribution of returns at small $\Delta t$ decays as an asymptotic power-law with an exponent of about 3, which is a value widely existing in mature markets. However, it decays exponentially when $\Delta t \ge$ 240 min, which is not observed in mature markets. (6) Return distributions gradually converge to Gaussian as $\Delta t$ increases. This observation is different from the finding of a critical $\Delta t =$ 4 days in the U.S. stock market.
Statistical Treatment Rules under Social Interaction
Authors: Seungjin Han, Julius Owusu, Youngki Shin
Date: 2022-09-19 00:00:00

In this paper we study treatment assignment rules in the presence of social interaction. We construct an analytical framework under the anonymous interaction assumption, where the decision problem becomes choosing a treatment fraction. We propose a multinomial empirical success (MES) rule that includes the empirical success rule of Manski (2004) as a special case. We investigate the non-asymptotic bounds of the expected utility based on the MES rule. Finally, we prove that the MES rule achieves the asymptotic optimality with the minimax regret criterion.
A Generalized Argmax Theorem with Applications
Authors: Gregory Cox
Date: 2022-09-19 00:00:00

The argmax theorem is a useful result for deriving the limiting distribution of estimators in many applications. The conclusion of the argmax theorem states that the argmax of a sequence of stochastic processes converges in distribution to the argmax of a limiting stochastic process. This paper generalizes the argmax theorem to allow the maximization to take place over a sequence of subsets of the domain. If the sequence of subsets converges to a limiting subset, then the conclusion of the argmax theorem continues to hold. We demonstrate the usefulness of this generalization in three applications: estimating a structural break, estimating a parameter on the boundary of the parameter space, and estimating a weakly identified parameter. The generalized argmax theorem simplifies the proofs for existing results and can be used to prove new results in these literatures.
A Structural Model for Detecting Communities in Networks
Authors: Alex Centeno
Date: 2022-09-17 00:00:00
Tasks: model

The objective of this paper is to identify and analyze the response actions of a set of players embedded in sub-networks in the context of interaction and learning. We characterize strategic network formation as a static game of interactions where players maximize their utility depending on the connections they establish and multiple interdependent actions that permit group-specific parameters of players. It is challenging to apply this type of model to real-life scenarios for two reasons: The computation of the Bayesian Nash Equilibrium is highly demanding and the identification of social influence requires the use of excluded variables that are oftentimes unavailable. Based on the theoretical proposal, we propose a set of simulant equations and discuss the identification of the social interaction effect employing multi-modal network autoregressive.
Skills and Liquidity Barriers to Youth Employment: Medium-term Evidence from a Cash Benchmarking Experiment in Rwanda
Authors: Craig McIntosh, Andrew Zeitlin
Date: 2022-09-18 00:00:00
Tasks: Benchmarking

We present results of an experiment benchmarking a workforce training program against cash transfers for underemployed young adults in Rwanda. 3.5 years after treatment, the training program enhances productive time use and asset investment, while the cash transfers drive productive assets, livestock values, savings, and subjective well-being. Both interventions have powerful effects on entrepreneurship. But while labor, sales, and profits all go up, the implied wage rate in these businesses is low. Our results suggest that credit is a major barrier to self-employment, but deeper reforms may be required to enable entrepreneurship to provide a transformative pathway out of poverty.
Peer Networks and Malleability of Educational Aspirations
Authors: Michelle González Amador, Robin Cowan, Eleonora Nillesen
Date: 2022-09-17 00:00:00

Continuing education beyond the compulsory years of schooling is one of the most important choices an adolescent has to make. Higher education is associated with a host of social and economic benefits both for the person and its community. Today, there is ample evidence that educational aspirations are an important determinant of said choice. We implement a multilevel, networked experiment in 45 Mexican high schools and provide evidence of the malleability of educational aspirations. We also show there exists an interdependence of students' choices and the effect of our intervention with peer networks. We find that a video intervention, which combines role models and information about returns to education is successful in updating students' beliefs and consequently educational aspirations.
Local political control in educational policy: Evidence from decentralized teacher pay reform under England's local education authorities
Authors: Yiang Li, Xingzuo Zhou
Date: 2022-09-17 00:00:00
Tasks: regression

In 2012, the School Teachers' Review Body discontinued central guidance and allowed school discretion in determining teachers' pay in England. Meanwhile, local education authorities (LEAs) offer non-statutory teacher pay recommendations to LEA-controlled schools. This study examines how LEAs' political party control determines their guidance regarding whether to adopt flexible performance pay or continue seniority-based pay. A regression discontinuity design is used to address the endogeneity of political control and educational policy-making. We find that marginally Conservative-controlled LEAs are more inclined to recommend market-oriented flexible pay structures. The results remain robust to alternative specifications. This study reveals that politics matter in England's local educational policy-making, which has broad implications for future policy.
Ambiguous Cheap Talk
Authors: Longjian Li
Date: 2022-09-18 00:00:00

This paper explores how ambiguity affects communication. We consider a cheap talk model in which the receiver evaluates the sender's message with respect to its worst-case expected payoff generated by multiplier preferences. We characterize the receiver's optimal strategy and show that the receiver's posterior action is consistent with his ex-ante action. We find that in some situations, ambiguity improves communication by shifting the receiver's optimal action upwards, and these situations are not rare.
Prospecting a Possible Quadratic Wormhole Between Quantum Mechanics and Plurality
Authors: Michal Fabinger, Michael H. Freedman, E. Glen Weyl
Date: 2022-09-16 00:00:00

We illustrate some formal symmetries between Quadratic Funding (Buterin et al., 2019), a mechanism for the (approximately optimal) determination of public good funding levels, and the Born (1926) rule in Quantum Mechanics, which converts the wave representation into a probability distribution, through a bridging formulation we call "Quantum Quartic Finance". We suggest further directions for investigating the practical utility of these symmetries. We discuss potential interpretations in greater depth in a companion blog post.
Spatio-temporal motion correction and iterative reconstruction of in-utero fetal fMRI
Authors: Athena Taymourtash, Hamza Kebiri, Ernst Schwartz, Karl-Heinz Nenning, Sebastien Tourbier, Gregor Kasprian, Daniela Prayer, Meritxell Bach Cuadra, Georg Langs
Date: 2022-09-17 00:00:00

Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses have limited its clinical applicability. Previous studies have focused primarily on the accurate estimation of the motion parameters employing a single step 3D interpolation at each individual time frame to recover a motion-free 4D fMRI image. Using only information from a 3D spatial neighborhood neglects the temporal structure of fMRI and useful information from neighboring timepoints. Here, we propose a novel technique based on four dimensional iterative reconstruction of the motion scattered fMRI slices. Quantitative evaluation of the proposed method on a cohort of real clinical fetal fMRI data indicates improvement of reconstruction quality compared to the conventional 3D interpolation approaches.
Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method
Authors: Varun Mannam
Date: 2022-09-06 00:00:00
Tasks: Object Recognition

The growing demand for the internet of things (IoT) makes it necessary to implement computer vision tasks such as object recognition in low-power devices. Convolutional neural networks (CNNs) are a potential approach for object recognition and detection. However, the convolutional layer in CNN consumes significant energy compared to the fully connected layers. To mitigate this problem, a new approach based on the Hadamard transformation as an alternative to the convolution operation is demonstrated using two fundamental datasets, MNIST and CIFAR10. The mathematical expression of the Hadamard method shows the clear potential to save energy consumption compared to convolutional layers, which are helpful with BigData applications. In addition, to the test accuracy of the MNIST dataset, the Hadamard method performs similarly to the convolution method. In contrast, with the CIFAR10 dataset, test data accuracy is dropped (due to complex data and multiple channels) compared to the convolution method. Finally, the demonstrated method is helpful for other computer vision tasks when the kernel size is smaller than the input image size.
AutoPET Challenge 2022: Step-by-Step Lesion Segmentation in Whole-body FDG-PET/CT
Authors: Zhantao Liu, Shaonan Zhong, Junyang Mo
Date: 2022-09-04 00:00:00
Tasks: Lesion Segmentation, Segmentation

Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesions with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel step-by-step 3D segmentation method to address this problem. We achieved Dice score of 0.92, false positive volume of 0.89 and false negative volume of 0.53 on preliminary test set.The code of our work is available on the following link: https://github.com/rightl/autopet.
Robustness of an Artificial Intelligence Solution for Diagnosis of Normal Chest X-Rays
Authors: Tom Dyer, Jordan Smith, Gaetan Dissez, Nicole Tay, Qaiser Malik, Tom Naunton Morgan, Paul Williams, Liliana Garcia-Mondragon, George Pearse, Simon Rasalingham
Date: 2022-08-31 00:00:00
Tasks: Medical Diagnosis

Purpose: Artificial intelligence (AI) solutions for medical diagnosis require thorough evaluation to demonstrate that performance is maintained for all patient sub-groups and to ensure that proposed improvements in care will be delivered equitably. This study evaluates the robustness of an AI solution for the diagnosis of normal chest X-rays (CXRs) by comparing performance across multiple patient and environmental subgroups, as well as comparing AI errors with those made by human experts. Methods: A total of 4,060 CXRs were sampled to represent a diverse dataset of NHS patients and care settings. Ground-truth labels were assigned by a 3-radiologist panel. AI performance was evaluated against assigned labels and sub-groups analysis was conducted against patient age and sex, as well as CXR view, modality, device manufacturer and hospital site. Results: The AI solution was able to remove 18.5% of the dataset by classification as High Confidence Normal (HCN). This was associated with a negative predictive value (NPV) of 96.0%, compared to 89.1% for diagnosis of normal scans by radiologists. In all AI false negative (FN) cases, a radiologist was found to have also made the same error when compared to final ground-truth labels. Subgroup analysis showed no statistically significant variations in AI performance, whilst reduced normal classification was observed in data from some hospital sites. Conclusion: We show the AI solution could provide meaningful workload savings by diagnosis of 18.5% of scans as HCN with a superior NPV to human readers. The AI solution is shown to perform well across patient subgroups and error cases were shown to be subjective or subtle in nature.
A Hybrid Labeled Multi-Bernoulli Filter With Amplitude For Tracking Fluctuating Targets
Authors: Weizhen Ma, Zhongliang Jing, Peng Dong, Henry Leung
Date: 2022-09-19 00:00:00

The amplitude information of target returns has been incorporated into many tracking algorithms for performance improvements. One of the limitations of employing amplitude feature is that the signal-to-noise ratio (SNR) of the target, i.e., the parameter of amplitude likelihood, is usually assumed to be known and constant. In practice, the target SNR is always unknown, and is dependent on aspect angle hence it will fluctuate. In this paper we propose a hybrid labeled multi-Bernoulli (LMB) filter that introduces the signal amplitude into the LMB filter for tracking targets with unknown and fluctuating SNR. The fluctuation of target SNR is modeled by an autoregressive gamma process and amplitude likelihoods for Swerling 1 and 3 targets are considered. Under Rao-Blackwell decomposition, an approximate Gamma estimator based on Laplace transform and Markov Chain Monte Carlo method is proposed to estimate the target SNR, and the kinematic state is estimated by a Gaussian mixture filter conditioned on the target SNR. The performance of the proposed hybrid filter is analyzed via a tracking scenario including three crossing targets. Simulation results verify the efficacy of the proposed SNR estimator and quantify the benefits of incorporating amplitude information for multi-target tracking.
Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width Learning
Authors: Razieh Torkamani, Hadi Zayyani, Farokh Marvasti
Date: 2022-09-19 00:00:00

This paper proposes a robust adaptive algorithm for smooth graph signal recovery which is based on generalized correntropy. A proper cost function is defined for this purpose. The proposed algorithm is derived and a kernel width learning-based version of the algorithm is suggested which the simulation results show the superiority of it to the fixed correntropy kernel version of the algorithm. Moreover, some theoretical analysis of the proposed algorithm are provided. In this regard, firstly, the convexity analysis of the cost function is discussed. Secondly, the uniform stability of the algorithm is investigated. Thirdly, the mean convergence analysis is also added. Finally, the complexity analysis of the algorithm is incorporated. In addition, some synthetic and real-world experiments show the advantage of the proposed algorithm in comparison to some other adaptive algorithms in the literature of adaptive graph signal recovery.
Simple HF antenna efficiency comparisons using the WSPR system
Authors: Jens Zander
Date: 2022-09-19 00:00:00

Determining the efficiency of an HF-antenna by measurements requires is a complex procedure involving expensive equipment, calibrated instruments for field strengths. In this paper we evaluate a simple, inexpensive method to determine the relative efficiency of an antenna relative a reference antenna. The method uses the Weak Signal Propagation Reporter(WSPR) network of receivers that are located all over the world. These receivers report the estimated signal-to-noise ratio of received beacon signals to the WSPR.net database where the data can be retrieved (almost) in real time. In the paper we analyze the method, estimate its accuracy and discuss advantages and limitations. Some preliminary measurement results are presented.
Localization Coverage Analysis of THz Communication Systems with a 3D Array
Authors: Pinjun Zheng, Tarig Ballal, Hui Chen, Henk Wymeersch, Tareq Y. Al-Naffouri
Date: 2022-09-19 00:00:00
Tasks: Position

This paper considers the problem of estimating the position and orientation of a user equipped with a three-dimensional (3D) array receiving downlink far-field THz signals from multiple base stations with known positions and orientations. We derive the Cram\'er-Rao Bound for the localization problem and define the coverage of the considered system. We compare the error lower bound distributions of the conventional planar array and the 3D array configurations at different user equipment (UE) positions and orientations. Our numerical results obtained for array configurations with an equal number of elements show very limited coverage of the planar-array configuration, especially across the UE orientation range. Conversely, a 3D array configuration offers an overall higher coverage with minor performance loss in certain UE positions and orientations.
Multibeam Sparse Tiled Planar Array for Joint Communication and Sensing
Authors: Hadi Alidoustaghdam, André Kokkeler, Yang Miao
Date: 2022-09-19 00:00:00

Multibeam analog arrays have been proposed for millimeter-wave joint communication and sensing (JCAS). We study multibeam planar arrays for JCAS, providing time division duplex communication and full-duplex sensing with steerable beams. In order to have a large aperture with a narrow beamwidth in the radiation pattern, we propose to design a sparse tiled planar array (STPA) aperture with affordable number of phase shifters. The modular tiling and sparse design of the array are non-convex optimization problems, however, we exploit the fact that the more irregularity of the antenna array geometry, the less the side lobe level. We propose to first solve the optimization by the maximum entropy in the phase centers of tiles in the array; then we perform sparse subarray selection leveraging the geometry of the sunflower array. While maintaining the same spectral efficiency in the communication link as conventional uniform planar array (CUPA), the STPA improves angle of arrival estimation when the line-of-sight path is dominant, e.g., the STPA with 125 elements distinguishes two adjacent targets with 20$^\circ$ difference in the proximity of boresight whereas CUPA cannot. Moreover, the STPA has a 40$\%$ shorter blockage time compared to the CUPA when a blocker moves in the elevation angles.
A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture
Authors: Boyu Hua, Tongtong Zhou, Qiuming Zhu, Kai Mao, Junwei Bao, Weizhi Zhong, Naeem Ahmed
Date: 2022-09-19 00:00:00

Considering the unmanned aerial vehicle (UAV) three-dimensional (3D) posture, a novel 3D non-stationary geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) UAV-to-vehicle (U2V) channels. It consists of a line-of-sight (LoS) and non-line-of-sight (NLoS) components. The factor of fuselage posture is considered by introducing a time-variant 3D posture matrix. Some important statistical properties, i.e. the temporal autocorrelation function (ACF) and spatial cross correlation function (CCF), are derived and investigated. Simulation results show that the fuselage posture has significant impact on the U2V channel characteristic and aggravate the non-stationarity. The agreements between analytical, simulated, and measured results verify the correctness of proposed model and derivations. Moreover, it is demonstrated that the proposed model is also compatible to the existing GBSM without considering fuselage posture.