pwc-archive/papers-with-abstracts huggingface dataset:
Statistical discrimination and statistical informativeness
Authors: Matteo Escudé, Paula Onuchic, Ludvig Sinander, Quitzé Valenzuela-Stookey
Date: 2022-05-14 00:00:00
Tasks: Informativeness

We study the link between Phelps-Aigner-Cain-type statistical discrimination and familiar notions of statistical informativeness. Our central insight is that Blackwell's Theorem, suitably relabeled, characterizes statistical discrimination in terms of statistical informativeness. This delivers one-half of Chambers and Echenique's (2021) characterization of statistical discrimination as a corollary, and suggests a different interpretation of it: that discrimination is inevitable. In addition, Blackwell's Theorem delivers a number of finer-grained insights into the nature of statistical discrimination. We argue that the discrimination-informativeness link is quite general, illustrating with an informativeness characterization of a different type of discrimination.
DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions
Authors: Qiqi Dai, Yee Hui Lee, Hai-Han Sun, Genevieve Ow, Mohamed Lokman Mohd Yusof, Abdulkadir C. Yucel
Date: 2022-05-16 00:00:00
Tasks: GPR

Traditional ground-penetrating radar (GPR) data inversion leverages iterative algorithms which suffer from high computation costs and low accuracy when applied to complex subsurface scenarios. Existing deep learning-based methods focus on the ideal homogeneous subsurface environments and ignore the interference due to clutters and noise in real-world heterogeneous environments. To address these issues, a two-stage deep neural network (DNN), called DMRF-UNet, is proposed to reconstruct the permittivity distributions of subsurface objects from GPR B-scans under heterogeneous soil conditions. In the first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1) is built to remove the clutters due to inhomogeneity of the heterogeneous soil. Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns the inverse mapping relationship and reconstructs the permittivity distribution of subsurface objects. To avoid information loss, an end-to-end training method combining the loss functions of two stages is introduced. A wide range of subsurface heterogeneous scenarios and B-scans are generated to evaluate the inversion performance. The test results in the numerical experiment and the real measurement show that the proposed network reconstructs the permittivities, shapes, sizes, and locations of subsurface objects with high accuracy. The comparison with existing methods demonstrates the superiority of the proposed methodology for the inversion under heterogeneous soil conditions.
A Framework to Map VMAF with the Probability of Just Noticeable Difference between Video Encoding Recipes
Authors: Jingwen Zhu, Suiyi Ling, Yoann Baveye, Patrick Le Callet
Date: 2022-05-16 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

Just Noticeable Difference (JND) model developed based on Human Vision System (HVS) through subjective studies is valuable for many multimedia use cases. In the streaming industries, it is commonly applied to reach a good balance between compression efficiency and perceptual quality when selecting video encoding recipes. Nevertheless, recent state-of-the-art deep learning based JND prediction model relies on large-scale JND ground truth that is expensive and time consuming to collect. Most of the existing JND datasets contain limited number of contents and are limited to a certain codec (e.g., H264). As a result, JND prediction models that were trained on such datasets are normally not agnostic to the codecs. To this end, in order to decouple encoding recipes and JND estimation, we propose a novel framework to map the difference of objective Video Quality Assessment (VQA) scores, i.e., VMAF, between two given videos encoded with different encoding recipes from the same content to the probability of having just noticeable difference between them. The proposed probability mapping model learns from DCR test data, which is significantly cheaper compared to standard JND subjective test. As we utilize objective VQA metric (e.g., VMAF that trained with contents encoded with different codecs) as proxy to estimate JND, our model is agnostic to codecs and computationally efficient. Throughout extensive experiments, it is demonstrated that the proposed model is able to estimate JND values efficiently.
Nearly optimal resolution estimate for the two-dimensional super-resolution and a new algorithm for direction of arrival estimation with uniform rectangular array
Authors: Ping Liu, Habib Ammari
Date: 2022-05-14 00:00:00
Tasks: Direction of Arrival Estimation, Super-Resolution

In this paper, we develop a new technique to obtain nearly optimal estimates of the computational resolution limits introduced in Appl. Comput. Harmon. Anal. 56 (2022) 402-446; IEEE Trans. Inf. Theory 67(7) (2021) 4812-4827; Inverse Probl. 37(10) (2021) 104001 for two-dimensional super-resolution problems. Our main contributions are fivefold: (i) Our work improves the resolution estimate for number detection and location recovery in two-dimensional super-resolution problems to nearly optimal; (ii) As a consequence, we derive a stability result for a sparsity-promoting algorithm in two-dimensional super-resolution problems (or Direction of Arrival problems (DOA)). The stability result exhibits the optimal performance of sparsity promoting in solving such problems; (iii) Our techniques pave the way for improving the estimate for resolution limits in higher-dimensional super-resolutions to nearly optimal; (iv) Inspired by these new techniques, we propose a new coordinate-combination-based model order detection algorithm for two-dimensional DOA estimation and theoretically demonstrate its optimal performance, and (v) we also propose a new coordinate-combination-based MUSIC algorithm for super-resolving sources in two-dimensional DOA estimation. It has excellent performance and enjoys many advantages compared to the conventional DOA algorithms. The coordinate-combination idea seems to be a promising way for multi-dimensional DOA estimation.
A Tutorial on Decoding Techniques of Sparse Code Multiple Access
Authors: Saumya Chaturvedi, Zilong Liu, Vivek Ashok Bohara, Anand Srivastava, Pei Xiao
Date: 2022-05-16 00:00:00

Sparse Code Multiple Access (SCMA) is a disruptive code-domain non-orthogonal multiple access (NOMA) scheme to enable \color{black}future massive machine-type communication networks. As an evolved variant of code division multiple access (CDMA), multiple users in SCMA are separated by assigning distinctive sparse codebooks (CBs). Efficient multiuser detection is carried out at the receiver by employing the message passing algorithm (MPA) that exploits the sparsity of CBs to achieve error performance approaching to that of the maximum likelihood receiver. In spite of numerous research efforts in recent years, a comprehensive one-stop tutorial of SCMA covering the background, the basic principles, and new advances, is still missing, to the best of our knowledge. To fill this gap and to stimulate more forthcoming research, we provide a holistic introduction to the principles of SCMA encoding, CB design, and MPA based decoding in a self-contained manner. As an ambitious paper aiming to push the limits of SCMA, we present a survey of advanced decoding techniques with brief algorithmic descriptions as well as several promising directions.
A new algebraic approach to genome rearrangement models
Authors: Venta Terauds, Jeremy Sumner
Date: 2020-12-21 00:00:00

We present a unified framework for modelling genomes and their rearrangements in a genome algebra, as elements that simultaneously incorporate all physical symmetries. Building on previous work utilising the group algebra of the symmetric group, we explicitly construct the genome algebra for the case of unsigned circular genomes with dihedral symmetry and show that the maximum likelihood estimate (MLE) of genome rearrangement distance can be validly and more efficiently performed in this setting. We then construct the genome algebra for a more general case, that is, for genomes that may be represented by elements of an arbitrary group and symmetry group, and show that the MLE computations can be performed entirely within this framework. There is no prescribed model in this framework; that is, it allows any choice of rearrangements that preserve the set of regions, along with arbitrary weights. Further, since the likelihood function is built from path probabilities -- a generalisation of path counts -- the framework may be utilised for any distance measure that is based on path probabilities.
Nonparametric Value-at-Risk via Sieve Estimation
Authors: Philipp Ratz
Date: 2022-05-14 00:00:00
Tasks: Time Series, Time Series Analysis

Artificial Neural Networks (ANN) have been employed for a range of modelling and prediction tasks using financial data. However, evidence on their predictive performance, especially for time-series data, has been mixed. Whereas some applications find that ANNs provide better forecasts than more traditional estimation techniques, others find that they barely outperform basic benchmarks. The present article aims to provide guidance as to when the use of ANNs might result in better results in a general setting. We propose a flexible nonparametric model and extend existing theoretical results for the rate of convergence to include the popular Rectified Linear Unit (ReLU) activation function and compare the rate to other nonparametric estimators. Finite sample properties are then studied with the help of Monte-Carlo simulations to provide further guidance. An application to estimate the Value-at-Risk of portfolios of varying sizes is also considered to show the practical implications.
ProNet DB: A proteome-wise database for protein surface property representations and RNA-binding profiles
Authors: Junkang Wei, Jin Xiao, Siyuan Chen, Licheng Zong, Xin Gao, Yu Li
Date: 2022-05-16 00:00:00
Tasks: Drug Discovery

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures challenge users in computational biology for utilizing the structural information and protein surface property representation. Recently, AlphaFold2 released the comprehensive proteome of various species, and protein surface property representation plays a crucial role in protein-molecule interaction prediction such as protein-protein interaction, protein-nucleic acid interaction, and protein-compound interaction. Here, we proposed the first comprehensive database, namely ProNet DB, which incorporates multiple protein surface representations and RNA-binding landscape for more than 326,175 protein structures covering 16 model organism proteomes from AlphaFold Protein Structure Database (AlphaFold DB) and experimentally validated protein structures deposited in Protein Data Bank (PDB). For each protein, we provided the original protein structure, surface property representation including hydrophobicity, charge distribution, hydrogen bond, interacting face, and RNA-binding landscape such as RNA binding sites and RNA binding preference. To interpret protein surface property representation and RNA binding landscape intuitively, we also integrate Mol* and Online 3D Viewer to visualize the representation on the protein surface. The pre-computed features are available for the users instantaneously and boost computational biology development including molecular mechanism exploration, geometry-based drug discovery and novel therapeutics development. The server is now available on https://proj.cse.cuhk.edu.hk/aihlab/pronet/.
How can I investigate causal brain networks with iEEG?
Authors: Yuhao Huang, Corey Keller
Date: 2022-05-14 00:00:00
Tasks: Functional Connectivity

While many human imaging methodologies probe the structural and functional connectivity of the brain, techniques to investigate cortical networks in a causal and directional manner are critical but limited. The use of iEEG enables several approaches to directly characterize brain regions that are functionally connected and in some cases also establish directionality of these connections. In this chapter we focus on the basis, method and application of the cortico-cortical evoked potential (CCEP), whereby electrical pulses applied to one set of intracranial electrodes yields an electrically-induced brain response at local and remote regions. In this chapter, CCEPs are first contextualized within common brain connectivity methods used to define cortical networks and how CCEP adds unique information. Second, the practical and analytical considerations when using CCEP are discussed. Third, we review the neurophysiology underlying CCEPs and the applications of CCEPs including exploring functional and pathological brain networks and probing brain plasticity. Finally, we end with a discussion of limitations, caveats, and directions to improve CCEP utilization in the future.
Unfolding selection to infer individual risk heterogeneity for optimising disease forecasts and policy development
Authors: M. Gabriela M. Gomes, Nicholas A. Feasey, Marcelo U. Ferreira, E. James LaCourse, Kate E. Langwig, Lisa Reimer, Beate Ringwald, Jamie Rylance, J. Russell Stothard, Miriam Taegtmeyer, Dianne J. Terlouw, Rachel Tolhurst, Tom Wingfield, Stephen B. Gordon
Date: 2020-09-02 00:00:00
Tasks: Attribute, Epidemiology

Mathematical models are increasing adopted for setting targets for disease prevention and control. As model-informed policies are implemented, however, the inaccuracies of some forecasts become apparent, for example overprediction of infection burdens and overestimation of intervention impacts. Here, we attribute these discrepancies to methodological limitations in capturing the heterogeneities of real-world systems. The mechanisms underpinning single factors for infection and their interactions determine individual propensities to acquire disease. These are potentially so numerous that to attain a full mechanistic description may be unfeasible. To contribute constructively to the development of health policies, model developers either leave factors out (reductionism) or adopt a broader but coarse description (holism). In our view, predictive capacity requires holistic descriptions of heterogeneity which are currently underutilised in infectious disease epidemiology but common in other disciplines.
Using a physical model and aggregate data to estimate the spreading of Covid-19 in Israel in the presence of waning immunity and competing variants
Authors: Hilla De-Leon, Francesco Pederiva
Date: 2022-05-16 00:00:00

In more than two years since the COVID-19 virus was first detected in China, hundreds of millions of individuals have been infected, and millions have died. Aside from the immediate need for medical solutions (such as vaccines and medications) to treat the epidemic, the Corona pandemic has strengthened the demand for mathematical models that can predict the spread of the pandemic in an ever-changing reality. Here, we present a novel, dynamic particle model based on the basic principles of statistical physics that enables the prediction of the spreading of Covid-19 in the presence of effective vaccines. This particle model enables us to accurately examine the effects of the vaccine on different subgroups of the vaccinated population and the entire population and to identify the vaccine waning. Furthermore, a particle model can predict the prevalence of two competing variants over time and their associated morbidity.
The interactions of SARS-CoV-2 with co-circulating pathogens: Epidemiological implications and current knowledge gaps
Authors: Anabelle Wong, Laura Barrero, Elizabeth Goult, Michael Briga, Sarah C. Kramer, Aleksandra Kovacevic, Lulla Opatowski, Matthieu Domenech de Cellès
Date: 2022-05-16 00:00:00

Despite the availability of effective vaccines, the persistence of SARS-CoV-2 suggests that co-circulation with other pathogens and resulting multi-epidemics may become increasingly frequent. To better forecast and control the risk of such multi-epidemics, it is essential to elucidate the potential interactions of SARS-CoV-2 with other pathogens; these interactions, however, remain poorly defined. Here, we aimed to review the current body of evidence about SARS-CoV-2 interactions. To study pathogen interactions in a systematic way, we first developed a general framework to capture their major components: sign, strength, symmetry, duration, and mechanism. We then reviewed the experimental evidence from animal models about SARS-CoV-2 interactions. Of the 14 studies identified, 11 focused on the outcomes of co-infection with non-attenuated influenza A viruses and generally demonstrated that co-infection increased disease severity compared with either mono-infection. By contrast, the effect of co-infection on the viral load of either virus was variable and inconsistent across studies. Next, we reviewed the epidemiological evidence about SARS-CoV-2 interactions in human populations. Although numerous studies were identified, only few were specifically designed to infer interaction and many were prone to multiple biases, including confounding. Nevertheless, their results suggested that influenza and pneumococcal conjugate vaccinations were associated with reduced riskof SARS-CoV-2 infection. Finally, we formulated simple transmission models of SARS-CoV-2 co-circulation with a viral or a bacterial pathogen, showing how they can naturally incorporate the proposed framework. More generally, we argue that such models, when designed with an integrative and multidisciplinary perspective, will be invaluable tools to resolve the substantial uncertainties that remain about SARS-CoV-2 interactions.
Backward bifurcation, basic reinfection number and robustness of a SEIRE epidemic model with reinfection
Authors: Shaoli Wang, Tengfei Wang, Ya-nen Qi, Fei Xu
Date: 2022-05-15 00:00:00

Recent evidences show that individuals who recovered from COVID-19 can be reinfected. However, this phenomenon has rarely been studied using mathematical models. In this paper, we propose a SEIRE epidemic model to describe the spread of the epidemic with reinfection. We obtain the important thresholds $R_0$ (the basic reproduction number) and Rc (a threshold less than one). Our investigations show that when $R_0 > 1$, the system has an endemic equilibrium, which is globally asymptotically stable. When $R_c < R_0 < 1$, the epidemic system exhibits bistable dynamics. That is, the system has backward bifurcation and the disease cannot be eradicated. In order to eradicate the disease, we must ensure that the basic reproduction number $R_0$ is less than $R_c$. The basic reinfection number is obtained to measure the reinfection force, which turns out to be a new tipping point for disease dynamics. We also give definition of robustness, a new concept to measure the difficulty of completely eliminating the disease for a bistable epidemic system. Numerical simulations are carried out to verify the conclusions.
Mack-Net model: Blending Mack's model with Recurrent Neural Networks
Authors: Eduardo Ramos-Pérez, Pablo J. Alonso-González, José Javier Núñez-Velázquez
Date: 2022-05-15 00:00:00
Tasks: Management, model, Position

In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007-2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is an indicator of the risk assumed by the company. Thus, measures that relate profitability with risk are crucial in order to understand the financial position of insurance firms. Taking advantage of the increasing computational power, this paper introduces a stochastic reserving model whose aim is to improve the performance of the traditional Mack's reserving model by applying an ensemble of Recurrent Neural Networks. The results demonstrate that blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities.
Volatility-inspired $σ$-LSTM cell
Authors: German Rodikov, Nino Antulov-Fantulin
Date: 2022-05-14 00:00:00
Tasks: Econometrics, Inductive Bias

Volatility models of price fluctuations are well studied in the econometrics literature, with more than 50 years of theoretical and empirical findings. The recent advancements in neural networks (NN) in the deep learning field have naturally offered novel econometric modeling tools. However, there is still a lack of explainability and stylized knowledge about volatility modeling with neural networks; the use of stylized facts could help improve the performance of the NN for the volatility prediction task. In this paper, we investigate how the knowledge about the "physics" of the volatility process can be used as an inductive bias to design or constrain a cell state of long short-term memory (LSTM) for volatility forecasting. We introduce a new type of $\sigma$-LSTM cell with a stochastic processing layer, design its learning mechanism and show good out-of-sample forecasting performance.
Market Impact: Empirical Evidence, Theory and Practice
Authors: Emilio Said
Date: 2022-05-15 00:00:00

We propose a theory of the market impact of metaorders based on a coarse-grained approach where the microscopic details of supply and demand is replaced by a single parameter $\rho \in [0,+\infty]$ shaping the supply-demand equilibrium and the market impact process during the execution of the metaorder. Our model provides an unified explanation of most of the empirical observations that have been reported and establishes a strong connection between the excess volatility puzzle and the order-driven view of the markets through the square-root law.
DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes
Authors: Baosheng Zhang, Xiaoguang Ma, Hongjun Ma, Chunbo Luo
Date: 2022-05-17 00:00:00
Tasks: Motion Estimation, Visual Odometry

Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images. However, in dynamic scenes mainly comprising moving pedestrians, vehicles, etc., there are insufficient robust static point features to enable accurate motion estimation, causing failures when reconstructing robotic motion. In this paper, we proposed DynPL-SVO, a complete dynamic SVO method that integrated united cost functions containing information between matched point features and re-projection errors perpendicular and parallel to the direction of the line features. Additionally, we introduced a \textit{dynamic} \textit{grid} algorithm to enhance its performance in dynamic scenes. The stereo camera motion was estimated through Levenberg-Marquard minimization of the re-projection errors of both point and line features. Comprehensive experimental results on KITTI and EuRoC MAV datasets showed that accuracy of the DynPL-SVO was improved by over 20\% on average compared to other state-of-the-art SVO systems, especially in dynamic scenes.
DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo
Authors: Woong Gyu La, Lingjie Kong, Sunil Muralidhara, Pratik Nichat
Date: 2022-05-17 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose DeepSim, a reinforcement learning environment build toolkit for ROS and Gazebo. It allows machine learning or reinforcement learning researchers to access the robotics domain and create complex and challenging custom tasks in ROS and Gazebo simulation environments. This toolkit provides building blocks of advanced features such as collision detection, behaviour control, domain randomization, spawner, and many more. DeepSim is designed to reduce the boundary between robotics and machine learning communities by providing Python interface. In this paper, we discuss the components and design decisions of DeepSim Toolkit.