pwc-archive/papers-with-abstracts huggingface dataset:
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach
Authors: Haotian Shi, Yang Zhou, Keshu Wu, Xin Wang, Yangxin Lin, Bin Ran
Date: 2020-12-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a cooperative strategy of connected and automated vehicles (CAVs) longitudinal control for partially connected and automated traffic environment based on deep reinforcement learning (DRL) algorithm, which enhances the string stability of mixed traffic, car following efficiency, and energy efficiency. Since the sequences of mixed traffic are combinatory, to reduce the training dimension and alleviate communication burdens, we decomposed mixed traffic into multiple subsystems where each subsystem is comprised of human-driven vehicles (HDV) followed by cooperative CAVs. Based on that, a cooperative CAV control strategy is developed based on a deep reinforcement learning algorithm, enabling CAVs to learn the leading HDV's characteristics and make longitudinal control decisions cooperatively to improve the performance of each subsystem locally and consequently enhance performance for the whole mixed traffic flow. For training, a distributed proximal policy optimization is applied to ensure the training convergence of the proposed DRL. To verify the effectiveness of the proposed method, simulated experiments are conducted, which shows the performance of our proposed model has a great generalization capability of dampening oscillations, fulfilling the car following and energy-saving tasks efficiently under different penetration rates and various leading HDVs behaviors. Keywords: partially connected automated traffic environment, cooperative control, deep reinforcement learning, traffic oscillation dampening, energy efficiency
Bayesian Quantile-Based Portfolio Selection
Authors: Taras Bodnar, Mathias Lindholm, Vilhelm Niklasson, Erik Thorsén
Date: 2020-12-03 00:00:00

We study the optimal portfolio allocation problem from a Bayesian perspective using value at risk (VaR) and conditional value at risk (CVaR) as risk measures. By applying the posterior predictive distribution for the future portfolio return, we derive relevant quantiles needed in the computations of VaR and CVaR, and express the optimal portfolio weights in terms of observed data only. This is in contrast to the conventional method where the optimal solution is based on unobserved quantities which are estimated, leading to suboptimality. We also obtain the expressions for the weights of the global minimum VaR and CVaR portfolios, and specify conditions for their existence. It is shown that these portfolios may not exist if the confidence level used for the VaR or CVaR computation are too low. Moreover, analytical expressions for the mean-VaR and mean-CVaR efficient frontiers are presented and the extension of theoretical results to general coherent risk measures is provided. One of the main advantages of the suggested Bayesian approach is that the theoretical results are derived in the finite-sample case and thus they are exact and can be applied to large-dimensional portfolios. By using simulation and real market data, we compare the new Bayesian approach to the conventional method by studying the performance and existence of the global minimum VaR portfolio and by analysing the estimated efficient frontiers. It is concluded that the Bayesian approach outperforms the conventional one, in particular at predicting the out-of-sample VaR.
Multiscale Comparative Connectomics
Authors: Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford, Benjamin D. Pedigo, Jesús Arroyo, Lucy Upchurch, G. Allan Johnson, Nian Wang, Youngser Park, Carey E. Priebe, Joshua T. Vogelstein
Date: 2020-11-30 00:00:00

The connectome, a map of the structural and/or functional connections in the brain, provides a complex representation of the neurobiological phenotypes on which it supervenes. This information-rich data modality has the potential to transform our understanding of the relationship between patterns in brain connectivity and neurological processes, disorders, and diseases. However, existing computational techniques used to analyze connectomes are oftentimes insufficient for interrogating multi-subject connectomics datasets: many current methods are either solely designed to analyze single connectomes or leverage heuristic graph statistics that are unable to capture the complete topology of multiscale connections between brain regions. To enable more rigorous connectomics analysis, we introduce a set of robust and interpretable effect size measures motivated by recent theoretical advances in random graph models. These measures facilitate simultaneous analysis of multiple connectomes across different scales of network topology, enabling the robust and reproducible discovery of hierarchical brain structures that vary in relation to phenotypic profiles. In addition to explaining the theoretical foundations and guarantees of our algorithms, we demonstrate their superiority over current state-of-the-art connectomics methods through extensive simulation studies and real-data experiments. Using a set of high-resolution connectomes obtained from genetically distinct mouse strains (including the BTBR mouse -- a standard model of autism -- and three behavioral wild-types), we illustrate how our methods successfully uncover latent information in multi-subject connectomics data and yield valuable insights into the connective correlates of neurological phenotypes that other methods do not capture. The data and code necessary to reproduce our analyses are available at https://github.com/neurodata/MCC.
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Authors: 蛋壳公寓租户
Date: 2020-12-01 00:00:00

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Estimating the Blood Supply Elasticity: Evidence from a Universal Scale Benefit Scheme
Authors: Sara R. Machado
Date: 2020-12-03 00:00:00

I estimate the semi-elasticity of blood donations with respect to a monetary benefit, namely the waiver of user fees when using the National Health Service, in Portugal. Using within-county variation over time in the value of the benefitI estimate both the unconditional elasticity, which captures overall response of the market, and the conditional elasticity, which holds constant the number of blood drives. This amounts to fixing a measure of the cost of donation to the blood donor. I instrument for the number of blood drives, which is endogenous, using a variable based on the number of weekend days and the proportion of blood drives on weekends. A one euro increase in the subsidy leads 1.8% more donations per 10000 inhabitants, conditional on the number of blood drives. The unconditional effect is smaller. The benefit does not attract new donors, instead it fosters repeated donation. Furthermore, the discontinuation of the benefit lead to a predicted decrease in donations of around 18%, on average. However, I show that blood drives have the potential to effectively substitute monetary incentives in solving market imbalances.
Massive MIMO goes Sub-GHz: Implementation and Experimental Exploration for LPWANs
Authors: Gilles Callebaut, Sara Gunnarsson, Andrea P. Guevara, Fredrik Tufvesson, Sofie Pollin, Liesbet Van der Perre, Anders J Johansson
Date: 2020-12-03 00:00:00

Low-Power Wide-Area Networks operating in the unlicensed bands are being deployed to connect a rapidly growing number of Internet-of-Things devices. While the unlicensed sub-GHz band offers favorable propagation for long-range connections, measurements show that the energy consumption of the nodes is still mostly dominated by the wireless transmission affecting their autonomy. We investigate the potential benefits of deploying massive MIMO technology to increase system reliability and at the same time support low-energy devices with good coverage at sub-GHz frequencies. The impact of different antenna configurations and propagation conditions is analyzed. Both actual average experienced array gain and channel hardening are examined. The assessment demonstrates the effect of channel hardening as well as the potential benefits of the experienced array gain. These measurements serve as a first assessment of the channel conditions of massive MIMO at sub-GHz frequencies and are, to the best of our knowledge, the first of its kind.
Fat Tailed Factors
Authors: Jan Rosenzweig
Date: 2020-11-27 00:00:00

Standard, PCA-based factor analysis suffers from a number of well known problems due to the random nature of pairwise correlations of asset returns. We analyse an alternative based on ICA, where factors are identified based on their non-Gaussianity, instead of their variance. Generalizations of portfolio construction to the ICA framework leads to two semi-optimal portfolio construction methods: a fat-tailed portfolio, which maximises return per unit of non-Gaussianity, and the hybrid portfolio, which asymptotically reduces variance and non-Gaussianity in parallel. For fat-tailed portfolios, the portfolio weights scale like performance to the power of $1/3$, as opposed to linear scaling of Kelly portfolios; such portfolio construction significantly reduces portfolio concentration, and the winner-takes-all problem inherent in Kelly portfolios. For hybrid portfolios, the variance is diversified at the same rate as Kelly PCA-based portfolios, but excess kurtosis is diversified much faster than in Kelly, at the rate of $n^{-2}$ compared to Kelly portfolios' $n^{-1}$ for increasing number of components $n$.
Consistently Large Cosmic Flows on Scales of 100 Mpc/h: a Challenge for the Standard LCDM Cosmology
Date: 2008-09-23 00:00:00

Peculiar velocity surveys have non-uniform spatial distributions of tracers, so that the bulk flow estimated from them does not correspond to that of a simple volume such as a sphere. Thus bulk flow estimates are generally not strictly comparable between surveys, even those whose effective depths are similar. In addition, the sparseness of typical surveys can lead to aliasing of small scale power into what is meant to be a probe of the largest scales. Here we introduce a new method of calculating bulk flow moments where velocities are weighted to give an optimal estimate of the bulk flow of an idealized survey, with the variance of the difference between the estimate and the actual flow being minimized. These "minimum variance" estimates can be designed to estimate the bulk flow on a particular scale with minimal sensitivity to small scale power, and are comparable between surveys. We compile all major peculiar velocity surveys and apply this new method to them. We find that most surveys we studied are highly consistent with each other. Taken together the data suggest that the bulk flow within a Gaussian window of radius 50 Mpc/h is 407 km/s toward l=287 and b=8. The large-scale bulk motion is consistent with predictions from the local density field. This indicates that there are significant density fluctuations on very large scales. A flow of this amplitude on such a large scale is not expected in the WMAP5-normalized LCDM cosmology, for which the predicted one-dimensional r.m.s. velocity is ~110 km/s. The large amplitude of the observed bulk flow favors the upper values of the WMAP5 error-ellipse, but even the point at the top of the WMAP5 95% confidence ellipse predicts a bulk flow which is too low compared to that observed at >98% confidence level.
Analytic models of plausible gravitational lens potentials
Date: 2007-05-04 00:00:00

Gravitational lenses on galaxy scales are plausibly modelled as having ellipsoidal symmetry and a universal dark matter density profile, with a Sersic profile to describe the distribution of baryonic matter. Predicting all lensing effects requires knowledge of the total lens potential: in this work we give analytic forms for that of the above hybrid model. Emphasising that complex lens potentials can be constructed from simpler components in linear combination, we provide a recipe for attaining elliptical symmetry in either projected mass or lens potential. We also provide analytic formulae for the lens potentials of Sersic profiles for integer and half-integer index. We then present formulae describing the gravitational lensing effects due to smoothly-truncated universal density profiles in cold dark matter model. For our isolated haloes the density profile falls off as radius to the minus fifth or seventh power beyond the tidal radius, functional forms that allow all orders of lens potential derivatives to be calculated analytically, while ensuring a non-divergent total mass. We show how the observables predicted by this profile differ from that of the original infinite-mass NFW profile. Expressions for the gravitational flexion are highlighted. We show how decreasing the tidal radius allows stripped haloes to be modelled, providing a framework for a fuller investigation of dark matter substructure in galaxies and clusters. Finally we remark on the need for finite mass halo profiles when doing cosmological ray-tracing simulations, and the need for readily-calculable higher order derivatives of the lens potential when studying catastrophes in strong lenses.
Finding large average submatrices in high dimensional data
Date: 2009-05-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data. Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.) In this paper we propose and evaluate a statistically motivated biclustering procedure (LAS) that finds large average submatrices within a given real-valued data matrix. The procedure operates in an iterative-residual fashion, and is driven by a Bonferroni-based significance score that effectively trades off between submatrix size and average value. We examine the performance and potential utility of LAS, and compare it with a number of existing methods, through an extensive three-part validation study using two gene expression datasets. The validation study examines quantitative properties of biclusters, biological and clinical assessments using auxiliary information, and classification of disease subtypes using bicluster membership. In addition, we carry out a simulation study to assess the effectiveness and noise sensitivity of the LAS search procedure. These results suggest that LAS is an effective exploratory tool for the discovery of biologically relevant structures in high dimensional data. Software is available at https://genome.unc.edu/las/.
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data
Authors: Hongzuo Xu, Yongjun Wang, Zhiyue Wu, Yijie Wang
Date: 2019-07-01 00:00:00
Tasks: Outlier Detection

Non-IID categorical data are ubiquitous and distinct in real-world applications. Learning various kinds of couplings has been proved to be a reliable measure when detecting outliers in such non-IID data. However, it is a critical yet challenging problem to model, represent, and utilise high-order complex value couplings. Existing outlier detection methods normally only focus on pairwise primary value couplings and fail to uncover real relations that hide in complex couplings, resulting in suboptimal and unstable performance. This paper introduces a novel unsupervised embedding-based complex value coupling learning framework EMAC and its instance SCAN to address these issues. SCAN first models primary value couplings. Then, coupling bias is defined to capture complex value couplings with different granularities and highlight the essence of outliers. An embedding method is performed on the value network constructed via biased value couplings, which further learns high-order complex value couplings and embeds these couplings into a value representation matrix. Bidirectional selective value coupling learning is proposed to show how to estimate value and object outlierness through value couplings. Substantial experiments show that SCAN (i) significantly outperforms five state-of-the-art outlier detection methods on thirteen real-world datasets; and (ii) has much better resilience to noise than its competitors.
Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band
Authors: James R. Stieger 1, 2, Stephen Engel 2, Haiteng Jiang 1, Christopher C. Cline 2, Mary Jo Kreitzer 2, Bin He 1*
Date: 2020-03-13 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from 
long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI 
learning, but how it does so remains unknown. Here we show that MBAT allows participants to learn to 
volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We 
trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and 
compared performance and brain activity before and after training between randomly assigned trained and 
untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group 
throughout training. Alpha-band activity in EEG signals, recorded in the volitional resting state during task 
performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of 
alpha-band activity during the intentional resting state correlated reliably with individuals’ 
mindfulness practice as well as performance on a sustained attention task. Collectively, these results show 
that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients' control over their 
brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit 
from alternatives to direct motor control.
Multipath Computation Offloading for Mobile Augmented Reality
Authors: Tristan Braud, Pengyuan Zhou, Jussi Kangasharju, and Pan HUI
Date: 2020-03-23 00:00:00
Tasks: Edge-computing, Scheduling

Mobile Augmented Reality (MAR) applications employ computationally demanding vision algorithms on resourcelimited devices. In parallel, communication networks are becoming more ubiquitous. Offloading to distant servers can thus overcome the device limitations at the cost of network delays. Multipath networking has been proposed to overcome network limitations but it is not easily adaptable to edge computing due to the server proximity and networking differences. In this article, we extend the current mobile edge offloading models and present a model for multi-server device-to-device, edge, and cloud offloading. We then introduce a new task allocation algorithm exploiting this model for MAR offloading. Finally, we evaluate the allocation algorithm against naive multipath scheduling and single path models through both a real-life experiment and extensive simulations. In case of sub-optimal network conditions, our model allows reducing the latency compared to single-path offloading, and significantly decreases packet loss compared to random task allocation. We also display the impact of the variation of WiFi parameters on task completion. We finally demonstrate the robustness of our system in case of network instability. With only 70% WiFi availability, our system keeps the excess latency below 9 ms. We finally evaluate the capabilities of the upcoming 5G and 802.11ax
Lateral Gene Transfer from the Dead
Date: 2012-11-19 00:00:00

In phylogenetic studies, the evolution of molecular sequences is assumed to have taken place along the phylogeny traced by the ancestors of extant species. In the presence of lateral gene transfer (LGT), however, this may not be the case, because the species lineage from which a gene was transferred may have gone extinct or not have been sampled. Because it is not feasible to specify or reconstruct the complete phylogeny of all species, we must describe the evolution of genes outside the represented phylogeny by modelling the speciation dynamics that gave rise to the complete phylogeny. We demonstrate that if the number of sampled species is small compared to the total number of existing species, the overwhelming majority of gene transfers involve speciation to, and evolution along extinct or unsampled lineages. We show that the evolution of genes along extinct or unsampled lineages can to good approximation be treated as those of independently evolving lineages described by a few global parameters. Using this result, we derive an algorithm to calculate the probability of a gene tree and recover the maximum likelihood reconciliation given the phylogeny of the sampled species. Examining 473 near universal gene families from 36 cyanobacteria, we find that nearly a third of transfer events -- 28% -- appear to have topological signatures of evolution along extinct species, but only approximately 6% of transfers trace their ancestry to before the common ancestor of the sampled cyanobacteria.
Systematic effects on dark energy from 3D weak shear
Date: 2008-01-21 00:00:00

We present an investigation into the potential effect of systematics inherent in multi-band wide field surveys on the dark energy equation of state determination for two 3D weak lensing methods. The weak lensing methods are a geometric shear-ratio method and 3D cosmic shear. The analysis here uses an extension of the Fisher matrix framework to jointly include photometric redshift systematics, shear distortion systematics and intrinsic alignments. We present results for DUNE and Pan-STARRS surveys. We show that assuming systematic parameters are fixed, but possibly biased, results in potentially large biases in dark energy parameters. We quantify any potential bias by defining a Bias Figure of Merit. We also show the effect on the dark energy Figure of Merit of marginalising over each systematic parameter individually. We find that the largest effect on the Figure of Merit comes from uncertainty in the photometric redshift systematic parameters. These can reduce the Figure of Merit by up to a factor of 2 to 4 in both 3D weak lensing methods, if no informative prior on the systematic parameters is applied. Shear distortion systematics have a smaller overall effect. Intrinsic alignment effects can reduce the Figure of Merit by up to a further factor of 2. This, however, is a worst case scenario. By including prior information on systematic parameters the Figure of Merit can be recovered to a large extent. We conclude that, as a rule of thumb, given a realistic current understanding of intrinsic alignments and photometric redshifts, then including all three primary systematic effects reduces the Figure of Merit by at most a factor of 2, but that in reality this factor should be much less. [abridged]