pwc-archive/papers-with-abstracts huggingface dataset:
Keep it green, simple and socially fair: a choice experiment on prosumers' preferences for peer to peer electricity trading in the Netherlands
Authors: Elena Georgarakis, Thomas Bauwens, Anne-Marie Pronk, Tarek Alskaif
Date: 2021-09-06 00:00:00
Tasks: energy trading, Marketing

While the potential for peer-to-peer electricity trading, where households trade surplus electricity with peers in a local energy market, is rapidly growing, the drivers of participation in this trading scheme have been understudied so far. In particular, there is a dearth of research on the role of non-monetary incentives for trading surplus electricity, despite their potentially important role. This paper presents the first discrete choice experiment conducted with prosumers (i.e. proactive households actively managing their electricity production and consumption) in the Netherlands. Electricity trading preferences are analyzed regarding economic, environmental, social and technological parameters, based on survey data (N = 74). The dimensions most valued by prosumers are the environmental and, to a lesser extent, economic dimensions, highlighting the key motivating roles of environmental factors. Furthermore, a majority of prosumers stated they would provide surplus electricity for free or for non-monetary compensations, especially to energy-poor households. These observed trends were more pronounced among members of energy cooperatives. This suggests that peer-to-peer energy trading can advance a socially just energy transition. Regarding policy recommendations, these findings point to the need for communicating environmental and economic benefits when marketing P2P electricity trading platforms and for technical designs enabling effortless and customizable transactions
Narratives in economics
Authors: Michael Roos, Matthias Reccius
Date: 2021-09-06 00:00:00

There is growing awareness within the economics profession of the important role narratives play in the economy. Even though empirical approaches that try to quantify economic narratives are getting increasingly popular, there is no theory or even a universally accepted definition of economic narratives underlying this research. First, we review and categorize the economic literature concerned with narratives and work out the different paradigms that are at play. Only a subset of the literature considers narratives to be active drivers of economic activity. In order to solidify the foundation of narrative economics, we propose a definition of collective economic narratives, isolating five important characteristics. We argue that, for a narrative to be economically relevant, it must be a sense-making story that emerges in a social context and suggests action to a social group. We also systematize how a collective economic narrative differs from a topic and from other kinds of narratives that are likely to have less impact on the economy. With regard to the popular use of topic modeling as an empirical strategy, we suggest that the complementary use of other canonical methods from the natural language processing toolkit and the development of new methods is inevitable to go beyond identifying topics and be able to move towards true empirical narrative economics.
Reaping the Rewards Later: How Education Improves Old-Age Cognition in South Africa
Authors: Plamen Nikolov, Steve Yeh
Date: 2021-09-05 00:00:00
Tasks: Decision Making

Cognition, a component of human capital, is fundamental for decision-making, and understanding the causes of human capital depreciation in old age is especially important in aging societies. Using various proxy measures of cognitive performance from a longitudinal survey in South Africa, we study how education affects cognition in late adulthood. We show that an extra year of schooling improves memory performance and general cognition. We find evidence of heterogeneous effects by gender: the effects are stronger among women. We explore potential mechanisms, and we show that a more supportive social environment, improved health habits, and reduced stress levels likely play a critical role in mediating the beneficial effects of educational attainment on cognition among the elderly.
Income inequality and mobility in geometric Brownian motion with stochastic resetting: theoretical results and empirical evidence of non-ergodicity
Authors: Viktor Stojkoski, Petar Jolakoski, Arnab Pal, Trifce Sandev, Ljupco Kocarev, Ralf Metzler
Date: 2021-09-04 00:00:00

We explore the role of non-ergodicity in the relationship between income inequality, the extent of concentration in the income distribution, and mobility, the feasibility of an individual to change their position in the income distribution. For this purpose, we explore the properties of an established model for income growth that includes "resetting" as a stabilising force which ensures stationary dynamics. We find that the dynamics of inequality is regime-dependent and may range from a strictly non-ergodic state where this phenomenon has an increasing trend, up to a stable regime where inequality is steady and the system efficiently mimics ergodic behaviour. Mobility measures, conversely, are always stable over time, but the stationary value is dependent on the regime, suggesting that economies become less mobile in non-ergodic regimes. By fitting the model to empirical data for the dynamics of income share of the top earners in the United States, we provide evidence that the income dynamics in this country is consistently in a regime in which non-ergodicity characterises inequality and immobility dynamics. Our results can serve as a simple rationale for the observed real world income dynamics and as such aid in addressing non-ergodicity in various empirical settings across the globe.
Extended Relative Maximum Likelihood Updating of Choquet Beliefs
Authors: Xiaoyu Cheng
Date: 2021-09-06 00:00:00

Cheng(2021) proposes and characterizes Relative Maximum Likelihood (RML) updating rule when the ambiguous beliefs are represented by a set of priors. Relatedly, this note proposes and characterizes Extended RML updating rule when the ambiguous beliefs are represented by a convex capacity. Two classical updating rules for convex capacities, Dempster-Shafer (Shafer, 1976) and Fagin-Halpern rules (Fagin and Halpern, 1990) are included as special cases of Extended RML.
Incentives for Collective Innovation
Authors: Gregorio Curello
Date: 2021-09-04 00:00:00

Agents exert hidden effort to produce randomly-sized innovations in a technology they share. Returns from using the technology grow as it develops, but so does the opportunity cost of effort, due to an 'exploration-exploitation' trade-off. As monitoring is imperfect, there exists a unique (strongly) symmetric equilibrium, and effort in any equilibrium ceases no later than in the single-agent problem. Small innovations may hurt all agents in the symmetric equilibrium, as they severely reduce effort. Allowing agents to discard innovations increases effort and payoffs, preserving uniqueness. Under natural conditions, payoffs rise above those of all equilibria with forced disclosure.
Infinite utility: counterparts and ultimate locations
Authors: Adam Jonsson
Date: 2021-09-04 00:00:00
Tasks: Ethics

The locations problem in infinite ethics concerns the relative moral status of different categories of potential bearers of value, the primary examples of which are people and points in time. The challenge is to determine which category of value bearers are of ultimate moral significance: the ultimate locations, for short. This paper defends the view that the ultimate locations are 'people at times'. A person at a time is not a specific person, but the person born at a specific point in time (de dicto). The main conclusion of the paper is that the unsettling implications of the time- and person-centered approaches to infinite ethics can be avoided. Most notably, a broad class of worlds that person-centered views deem incomparable can be strictly ranked.
Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data
Authors: Mohamed Amir Alaa Belmekki, Rachael Tobin, Gerald S. Buller, Stephen McLaughlin, Abderrahim Halimi
Date: 2021-09-03 00:00:00

3D single-photon LiDAR imaging plays an important role in numerous applications. However, long acquisition times and significant data volumes present a challenge to LiDAR imaging. This paper proposes a task-optimized adaptive sampling framework that enables fast acquisition and processing of high-dimensional single-photon LiDAR data. Given a task of interest, the iterative sampling strategy targets the most informative regions of a scene which are defined as those minimizing parameter uncertainties. The task is performed by considering a Bayesian model that is carefully built to allow fast per-pixel computations while delivering parameter estimates with quantified uncertainties. The framework is demonstrated on multispectral 3D single-photon LiDAR imaging when considering object classification and/or target detection as tasks. It is also analysed for both sequential and parallel scanning modes for different detector array sizes. Results on simulated and real data show the benefit of the proposed optimized sampling strategy when compared to fixed sampling strategies.
Resource and data efficient self supervised learning
Authors: Ozan Ciga, Tony Xu, Anne L. Martel
Date: 2021-09-03 00:00:00
Tasks: Diversity, Self-Supervised Learning

We investigate the utility of pretraining by contrastive self supervised learning on both natural-scene and medical imaging datasets when the unlabeled dataset size is small, or when the diversity within the unlabeled set does not lead to better representations. We use a two step approach which is analogous to supervised training with ImageNet initialization, where we pretrain networks that are already pretrained on ImageNet dataset to improve downstream task performance on the domain of interest. To improve the speed of convergence and the overall performance, we propose weight scaling and filter selection methods prior to second step of pretraining. We demonstrate the utility of this approach on three popular contrastive techniques, namely SimCLR, SWaV and BYOL. Benefits of double pretraining include better performance, faster convergence, ability to train with smaller batch sizes and smaller image dimensions with negligible differences in performance. We hope our work helps democratize self-supervision by enabling researchers to fine-tune models without requiring large clusters or long training times.
Global-Local Transformer for Brain Age Estimation
Authors: Sheng He, P. Ellen Grant, Yangming Ou
Date: 2021-09-03 00:00:00
Tasks: Age Estimation

Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract the global-context information from the whole input image and a local-pathway to extract the local fine-grained details from local patches. The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age. We evaluate the proposed method on 8 public datasets with 8,379 healthy brain MRIs with the age range of 0-97 years. 6 datasets are used for cross-validation and 2 datasets are used for evaluating the generality. Comparing with other state-of-the-art methods, the proposed global-local transformer reduces the mean absolute error of the estimated ages to 2.70 years and increases the correlation coefficient of the estimated age and the chronological age to 0.9853. In addition, our proposed method provides regional information of which local patches are most informative for brain age estimation. Our source code is available on: \url{https://github.com/shengfly/global-local-transformer}.
Joint Multi-User Communication and Sensing Exploiting Both Signal and Environment Sparsity
Authors: Xin Tong, Zhaoyang Zhang, Jue Wang, Chongwen Huang, Merouane Debbah
Date: 2021-09-06 00:00:00
Tasks: object-detection, Object Detection

As a potential technology feature for 6G wireless networks, the idea of sensing-communication integration requires the system not only to complete reliable multi-user communication but also to achieve accurate environment sensing. In this paper, we consider such a joint communication and sensing (JCAS) scenario, in which multiple users use the sparse code multiple access (SCMA) scheme to communicate with the wireless access point (AP). Part of the user signals are scattered by the environment object and reflected by an intelligent reflective surface (IRS) before they arrive at the AP. We exploit the sparsity of both the structured user signals and the unstructured environment and propose an iterative and incremental joint multi-user communication and environment sensing scheme, in which the two processes, i.e., multi-user information detection and environment object detection, interweave with each other thanks to their intrinsic mutual dependence. The proposed algorithm is sliding-window based and also graph based, which can keep on sensing the environment as long as there are illuminating user signals. The trade-off relationship between the key system parameters is analyzed, and the simulation result validates the convergence and effectiveness of the proposed algorithm.
Height-Dependent LoS Probability Model for A2G MmWave Communications under Built-up Scenarios
Authors: Minghui Pang, Qiuming Zhu, Fei Bai, Zhuo Li, Hanpeng Li, Kai Mao, Yue Tian
Date: 2021-09-06 00:00:00

Based on the three-dimensional propagation characteristic under built-up scenarios, a height-dependent line-of-sight (LoS) probability model for air-to-ground (A2G) millimeter wave (mmWave) communications is proposed in this paper. With comprehensive considerations of scenario factors, i.e., building height distribution, building width, building space, and the heights of transceivers, this paper upgrades the prediction method of International Telecommunication Union-Radio (ITU-R) standard to both low altitude and high altitude cases. In order to speed up the LoS probability prediction, an approximate parametric model is also developed based on the theoretical expression. The simulation results based on ray-tracing (RT) method show that the proposed model has good consistency with existing models at the low altitude. However, it has better performance at the high altitude. The new model can be used for the A2G channel modeling and performance analysis such as cell coverage, outage probability, and bit error rate of A2G communication systems.
Geometry-Based Stochastic Line-of-Sight Probability Model for A2G Channels under Urban Scenarios
Authors: Qiuming Zhu, Fei Bai, Minghui Pang, Jie Li, Weizhi Zhong, Xiaomin Chen, Kai Mao
Date: 2021-09-06 00:00:00

Line-of-sight (LoS) path is essential for the reliability of air-to-ground (A2G) communications, but the existence of LoS path is difficult to predict due to random obstacles on the ground. Based on the statistical geographic information and Fresnel clearance zone, a general stochastic LoS probability model for three-dimensional (3D) A2G channels under urban scenarios is developed. By considering the factors, i.e., building height distribution, building width, building space, carrier frequency, and transceiver's heights, the proposed model is suitable for different frequencies and altitudes. Moreover, in order to get a closed-form expression and reduce the computational complexity, an approximate parametric model is also built with the machine-learning (ML) method to estimate model parameters. The simulation results show that the proposed model has good consistency with existing models at the low altitude. When the altitude increases, it has better performance by comparing with that of the ray-tracing Monte-Carlo simulation data. The analytical results of proposed model are helpful for the channel modeling and performance analysis such as cell coverage, outage probability, and bit error rate in A2G communications.
Machine Learning-Based 3D Channel Modeling for U2V mmWave Communications
Authors: Kai Mao, Qiuming Zhu, Maozhong Song, Hanpeng Li, Benzhe Ning, Boyu Hua, Wei Fan
Date: 2021-09-05 00:00:00
Tasks: BIG-bench Machine Learning, Generative Adversarial Network

Unmanned aerial vehicle (UAV) millimeter wave (mmWave) technologies can provide flexible link and high data rate for future communication networks. By considering the new features of three-dimensional (3D) scattering space, 3D velocity, 3D antenna array, and especially 3D rotations, a machine learning (ML) integrated UAV-to-Vehicle (U2V) mmWave channel model is proposed. Meanwhile, a ML-based network for channel parameter calculation and generation is developed. The deterministic parameters are calculated based on the simplified geometry information, while the random ones are generated by the back propagation based neural network (BPNN) and generative adversarial network (GAN), where the training data set is obtained from massive ray-tracing (RT) simulations. Moreover, theoretical expressions of channel statistical properties, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), and cross-correlation function (CCF) are derived and analyzed. Finally, the U2V mmWave channel is generated under a typical urban scenario at 28 GHz. The generated PDP and DPSD show good agreement with RT-based results, which validates the effectiveness of proposed method. Moreover, the impact of 3D rotations, which has rarely been reported in previous works, can be observed in the generated CCF and ACF, which are also consistent with the theoretical and measurement results.
Efficient Estimation of Sensor Biases for the 3-Dimensional Asynchronous Multi-Sensor System
Authors: Wenqiang Pu, Ya-Feng Liu, Zhi-Quan Luo
Date: 2021-09-04 00:00:00

An important preliminary procedure in multi-sensor data fusion is \textit{sensor registration}, and the key step in this procedure is to estimate sensor biases from their noisy measurements. There are generally two difficulties in this bias estimation problem: one is the unknown target states which serve as the nuisance variables in the estimation problem, and the other is the highly nonlinear coordinate transformation between the local and global coordinate systems of the sensors. In this paper, we focus on the 3-dimensional asynchronous multi-sensor scenario and propose a weighted nonlinear least squares (NLS) formulation by assuming that there is a target moving with a nearly constant velocity. We propose two possible choices of the weighting matrix in the NLS formulation, which correspond to classical and weighted NLS estimation and maximum likelihood (ML) estimation, respectively. To address the intrinsic nonlinearity, we propose a block coordinate descent (BCD) algorithm for solving the formulated problem, which alternately updates different kinds of bias estimates. Specifically, the proposed BCD algorithm involves solving linear LS problems and nonconvex quadratically constrained quadratic program (QCQP) problems with special structures. Instead of adopting the semidefinite relaxation technique, we develop a much more computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the nonconvex QCQP subproblems. The convergence of the ADMM to the global solution of the QCQP subproblems is established under mild conditions. The effectiveness and efficiency of the proposed BCD algorithm are demonstrated via numerical simulations.
Niche differentiation in the light spectrum promotes coexistence of phytoplankton species: a spatial modelling approach
Authors: Christopher M. Heggerud, King-Yeung Lam, Hao Wang
Date: 2021-09-06 00:00:00
Tasks: Diversity

The paradox of the plankton highlights the apparent contradiction between Gause's law of competitive exclusion and the observed diversity of phytoplankton. It is well known that phytoplankton dynamics depend heavily on two main resources: light and nutrients. Here we treat light as a continuum of resources rather than a single resource by considering the visible light spectrum. We propose a spatially explicit reaction-diffusion-advection model to explore under what circumstance coexistence is possible from mathematical and biological perspectives. Furthermore, we provide biological context as to when coexistence is expected based on the degree of niche differentiation within the light spectrum and overall turbidity of the water.
An axiomatization of $Λ$-quantiles
Authors: Fabio Bellini, Ilaria Peri
Date: 2021-09-06 00:00:00

We give an axiomatic foundation to $\Lambda$-quantiles, a family of generalized quantiles introduced by Frittelli et al. (2014) under the name of Lambda Value at Risk. Under mild assumptions, we show that these functionals are characterized by a property that we call "locality", that means that any change in the distribution of the probability mass that arises entirely above or below the value of the $\Lambda$-quantile does not modify its value. We compare with a related axiomatization of the usual quantiles given by Chambers (2009), based on the stronger property of "ordinal covariance", that means that quantiles are covariant with respect to increasing transformations. Further, we present a systematic treatment of the properties of $\Lambda$-quantiles, refining some of the results of Frittelli et al. (2014) and Burzoni et al. (2017) and showing that in the case of a nonincreasing $\Lambda$ the properties of $\Lambda$-quantiles closely resemble those of the usual quantiles.
The Koha Code: A Biological Theory of Memory
Authors: Lum Ramabaja
Date: 2021-09-05 00:00:00

This work introduces the Koha model, a new theory that aims to explain two unresolved phenomena within biological neural networks: How information is processed and stored within neural circuits, and how neurons learn to become pattern detectors. In the Koha model, the dendritic spines of a neuron serve as computational units that scan for precise spike patterns in their synaptic inputs. The model proposes the existence of a temporal code within each dendritic spine, which is used for the dampening or amplification of signals, depending on the temporal information of incoming spike trains. Compelling evidence is provided and a concrete process is described for how signal filtration occurs within spine necks. A competitive learning algorithm is then proposed that describes how neurons use their internal temporal codes to become pattern detectors.
Voxel selection framework based on meta-heuristic search and mutual information for brain decoding
Authors: Osama Hourani, Nasrollah Moghadam Charkari, Saeed Jalili
Date: 2021-07-29 00:00:00
Tasks: Brain Computer Interface, Brain Decoding, Decoder, Heuristic Search

Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. One of the crucial problems in the brain decoder is the selecting informative voxels. We propose a meta-heuristic voxel selection framework for brain decoding. It is composed of four phases: preprocessing of fMRI data; filtering insignificant voxels; postprocessing; and meta-heuristics selection. The main contribution is benefiting a meta-heuristics search algorithm to guide a wrapper voxel selection. The main criterion to nominate a voxel is based on its mutual information with the provided stimulus label. The results show impressive accuracy rates which are 90.66 +/- 3.66 and 91.61 +/- 8.24 for DS105 and DS107, respectively. This outperforms the most of existing brain decoders in similar validation conditions. The experimental results are very encouraging which can be successfully useId in the brain-computer interface.