pwc-archive/papers-with-abstracts huggingface dataset:
A fast asynchronous MCMC sampler for sparse Bayesian inference
Authors: Yves Atchadé, LiWei Wang
Date: 2021-08-14 00:00:00
Tasks: Bayesian Inference

We propose a very fast approximate Markov Chain Monte Carlo (MCMC) sampling framework that is applicable to a large class of sparse Bayesian inference problems, where the computational cost per iteration in several models is of order $O(ns)$, where $n$ is the sample size, and $s$ the underlying sparsity of the model. This cost can be further reduced by data sub-sampling when stochastic gradient Langevin dynamics are employed. The algorithm is an extension of the asynchronous Gibbs sampler of Johnson et al. (2013), but can be viewed from a statistical perspective as a form of Bayesian iterated sure independent screening (Fan et al. (2009)). We show that in high-dimensional linear regression problems, the Markov chain generated by the proposed algorithm admits an invariant distribution that recovers correctly the main signal with high probability under some statistical assumptions. Furthermore we show that its mixing time is at most linear in the number of regressors. We illustrate the algorithm with several models.
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Authors: Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl
Date: 2021-08-16 00:00:00
Tasks: Collaborative Filtering, Music Recommendation, Recommendation Systems

Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing work predominantly adopts simple statistical measures, such as the difference of mean or median popularity, to quantify popularity bias. Moreover, it does so irrespective of user characteristics other than the inclination to popular content. In this work, in contrast, we propose to investigate popularity differences (between the user profile and recommendation list) in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall's tau rank-order correlation). This results in a more detailed picture of the characteristics of popularity bias. Furthermore, we investigate whether such algorithmic popularity bias affects users of different genders in the same way. We focus on music recommendation and conduct experiments on the recently released standardized LFM-2b dataset, containing listening profiles of Last.fm users. We investigate the algorithmic popularity bias of seven common recommendation algorithms (five collaborative filtering and two baselines). Our experiments show that (1) the studied metrics provide novel insights into popularity bias in comparison with only using average differences, (2) algorithms less inclined towards popularity bias amplification do not necessarily perform worse in terms of utility (NDCG), (3) the majority of the investigated recommenders intensify the popularity bias of the female users.
Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals
Authors: Kawsar Noor, Lukasz Roguski, Alex Handy, Roman Klapaukh, Amos Folarin, Luis Romao, Joshua Matteson, Nathan Lea, Leilei Zhu, Wai Keong Wong, Anoop Shah, Richard J Dobson
Date: 2021-08-15 00:00:00
Tasks: Information Retrieval, Retrieval

As more healthcare organisations transition to using electronic health record (EHR) systems it is important for these organisations to maximise the secondary use of their data to support service improvement and clinical research. These organisations will find it challenging to have systems which can mine information from the unstructured data fields in the record (clinical notes, letters etc) and more practically have such systems interact with all of the hospitals data systems (legacy and current). To tackle this problem at University College London Hospitals, we have deployed an enhanced version of the CogStack platform; an information retrieval platform with natural language processing capabilities which we have configured to process the hospital's existing and legacy records. The platform has improved data ingestion capabilities as well as better tools for natural language processing. To date we have processed over 18 million records and the insights produced from CogStack have informed a number of clinical research use cases at the hospitals.
Automated Enterprise Architecture Model Mining
Authors: Peter Hillmann, Erik Heiland, Andreas Karcher
Date: 2021-08-15 00:00:00
Tasks: model

Metadata are like the steam engine of the 21st century, driving businesses and offer multiple enhancements. Nevertheless, many companies are unaware that these data can be used efficiently to improve their own operation. This is where the Enterprise Architecture Framework comes in. It empowers an organisation to get a clear view of their business, application, technical and physical layer. This modelling approach is an established method for organizations to take a deeper look into their structure and processes. The development of such models requires a great deal of effort, is carried out manually by interviewing stakeholders and requires continuous maintenance. Our new approach enables the automated mining of Enterprise Architecture models. The system uses common technologies to collect the metadata based on network traffic, log files and other information in an organisation. Based on this, the new approach generates EA models with the desired views points. Furthermore, a rule and knowledge-based reasoning is used to obtain a holistic overview. This offers a strategic decision support from business structure over process design up to planning the appropriate support technology. Therefore, it forms the base for organisations to act in an agile way. The modelling can be performed in different modelling languages, including ArchiMate and the Nato Architecture Framework (NAF). The designed approach is already evaluated on a small company with multiple services and an infrastructure with several nodes.
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Authors: Yankai Chen, Menglin Yang, Yingxue Zhang, Mengchen Zhao, Ziqiao Meng, Jianye Hao, Irwin King
Date: 2021-08-14 00:00:00
Tasks: Knowledge-Aware Recommendation, Knowledge Graphs, Recommendation Systems

Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention. Via unifying the KG with user-item interactions into a tripartite graph, recent works explore the graph topologies to learn the low-dimensional representations of users and items with rich semantics. However, these real-world tripartite graphs are usually scale-free, the intrinsic hierarchical graph structures of which are underemphasized in existing works, consequently, leading to suboptimal recommendation performance. To address this issue and provide more accurate recommendation, we propose a knowledge-aware recommendation method with the hyperbolic geometry, namely Lorentzian Knowledge-enhanced Graph convolutional networks for Recommendation (LKGR). LKGR facilitates better modeling of scale-free tripartite graphs after the data unification. Specifically, we employ different information propagation strategies in the hyperbolic space to explicitly encode heterogeneous information from historical interactions and KGs. Our proposed knowledge-aware attention mechanism enables the model to automatically measure the information contribution, producing the coherent information aggregation in the hyperbolic space. Extensive experiments on three real-world benchmarks demonstrate that LKGR outperforms state-of-the-art methods by 3.6-15.3% of Recall@20 on Top-K recommendation.
Multi-Objective Recommendations: A Tutorial
Authors: Yong Zheng, David, Wang
Date: 2021-08-13 00:00:00
Tasks: Decision Making, Recommendation Systems

Recommender systems (RecSys) have been well developed to assist user decision making. Traditional RecSys usually optimize a single objective (e.g., rating prediction errors or ranking quality) in the model. There is an emerging demand in multi-objective optimization recently in RecSys, especially in the area of multi-stakeholder and multi-task recommender systems. This article provides an overview of multi-objective recommendations, followed by the discussions with case studies. The document is considered as a supplementary material for our tutorial on multi-objective recommendations at ACM SIGKDD 2021.
Strategic Exploration for Innovation
Authors: Shangen Li
Date: 2021-08-16 00:00:00

This paper introduces a framework to study innovation in a strategic setting, in which innovators allocate their resources between exploration and exploitation in continuous time. Exploration creates public knowledge, while exploitation delivers private benefits. Through the analysis of a class of Markov equilibria, we demonstrate that knowledge spillovers accelerate knowledge creation and expedite its availability, thereby encouraging innovators to increase exploration. The prospect of the ensuing superior long-term innovations further motivates exploration, giving rise to a positive feedback loop. This novel feedback loop can substantially mitigate the free-riding problem arising from knowledge spillovers.
On-the-Fly, Sample-Tailored Optimisation of NMR Experiments
Authors: Jonathan R. J. Yong, Mohammadali Foroozandeh
Date: 2021-08-16 00:00:00

NMR experiments, indispensable to chemists in many areas of research, are often run with generic, unoptimised experimental parameters. This approach makes robust and automated acquisition on different samples and instruments extremely challenging. Here, we introduce NMR-POISE (Parameter Optimisation by Iterative Spectral Evaluation), the first demonstration of on-the-fly, sample-tailored, and fully automated optimisation of a wide range of NMR experiments. We illustrate how POISE maximises spectral sensitivity and quality with a diverse set of 1D and 2D examples, ranging from HSQC and NOESY experiments to ultrafast and pure shift techniques. Our Python implementation of POISE has an interface integrated into Bruker's TopSpin software, one of the most widely used platforms for NMR acquisition and automation, allowing NMR optimisations to be run without direct user supervision. We predict that POISE will find widespread usage in academia and industry, where sample-specific and automated experiment optimisation is mandatory.
Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization
Authors: Jun Liu, Jiayao Gao, Sanjay Jha, Wen Hu
Date: 2021-08-16 00:00:00
Tasks: Outdoor Localization, Super-Resolution

Localization is important for a large number of Internet of Things (IoT) endpoint devices connected by LoRaWAN. Due to the bandwidth limitations of LoRaWAN, existing localization methods without specialized hardware (e.g., GPS) produce poor performance. To increase the localization accuracy, we propose a super-resolution localization method, called Seirios, which features a novel algorithm to synchronize multiple non-overlapped communication channels by exploiting the unique features of the radio physical layer to increase the overall bandwidth. By exploiting both the original and the conjugate of the physical layer, Seirios can resolve the direct path from multiple reflectors in both indoor and outdoor environments. We design a Seirios prototype and evaluate its performance in an outdoor area of 100 m $\times$ 60 m, and an indoor area of 25 m $\times$ 15 m, which shows that Seirios can achieve a median error of 4.4 m outdoors (80% samples < 6.4 m), and 2.4 m indoors (80% samples < 6.1 m), respectively. The results show that Seirios produces 42% less localization error than the baseline approach. Our evaluation also shows that, different to previous studies in Wi-Fi localization systems that have wider bandwidth, time-of-fight (ToF) estimation is less effective for LoRaWAN localization systems with narrowband radio signals.
Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment
Authors: Yuanyuan Liu, Nelly Penttilä, Tiina Ihalainen, Juulia Lintula, Rachel Convey, Okko Räsänen
Date: 2021-08-16 00:00:00

Imprecise vowel articulation can be observed in people with Parkinson's disease (PD). Acoustic features measuring vowel articulation have been demonstrated to be effective indicators of PD in its assessment. Standard clinical vowel articulation features of vowel working space area (VSA), vowel articulation index (VAI) and formants centralization ratio (FCR), are derived the first two formants of the three corner vowels /a/, /i/ and /u/. Conventionally, manual annotation of the corner vowels from speech data is required before measuring vowel articulation. This process is time-consuming. The present work aims to reduce human effort in clinical analysis of PD speech by proposing an automatic pipeline for vowel articulation assessment. The method is based on automatic corner vowel detection using a language universal phoneme recognizer, followed by statistical analysis of the formant data. The approach removes the restrictions of prior knowledge of speaking content and the language in question. Experimental results on a Finnish PD speech corpus demonstrate the efficacy and reliability of the proposed automatic method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/). The automatically computed parameters are shown to be highly correlated with features computed with manual annotations of corner vowels. In addition, automatically and manually computed vowel articulation features have comparable correlations with experts' ratings on speech intelligibility, voice impairment and overall severity of communication disorder. Language-independence of the proposed approach is further validated on a Spanish PD database, PC-GITA, as well as on TORGO corpus of English dysarthric speech.
Equivalence of Linear Complementarity Problems: Theory and Application to Nonsmooth Bifurcations
Authors: Felix Miranda-Villatoro, Fernando Castaños, Alessio Franci
Date: 2021-08-16 00:00:00

Linear complementarity problems provide a powerful framework to model nonsmooth phenomena in a variety of real-world applications. In dynamical control systems, they appear coupled to a linear input-output system in the form of linear complementarity systems. Mimicking the program that led to the foundation of bifurcation theory in smooth maps, we introduce a novel notion of equivalence between linear complementarity problems that sets the basis for a theory of bifurcations in a large class of nonsmooth maps, including, but not restricted to, steadystate bifurcations in linear complementarity systems. Our definition exploits the rich geometry of linear complementarity problems and leads to constructive algebraic conditions for identifying and classifying the nonsmooth singularities associated with nonsmooth bifurcations. We thoroughly illustrate our theory on an extended applied example, the design of bistability in an electrical network, and a more theoretical one, the identification and classification of all possible equivalence classes in two-dimensional linear complementarity problems.
Receding Horizon Iterative Learning Control for Continuously Operated Systems
Authors: Maxwell Wu, Mitchell Cobb, James Reed, Kirti Mishra, Chris Vermillion, Kira Barton
Date: 2021-08-16 00:00:00

This paper presents an iterative learning control (ILC) scheme for continuously operated repetitive systems for which no initial condition reset exists. To accomplish this, we develop a lifted system representation that accounts for the effect of the initial conditions on dynamics and projects the dynamics over multiple future iterations. Additionally, we develop an economic cost function and update law that considers the performance over multiple iterations in the future, thus allowing for the prediction horizon to be larger than just the next iteration. Convergence of the iteration varying initial condition and applied input are proven and demonstrated using a simulated servo-positioning system test case.
CVaR-based Safety Analysis in the Infinite Time Horizon Setting
Authors: Chuanning Wei, Michael Fauss, Margaret P. Chapman
Date: 2021-08-15 00:00:00
Tasks: Management

We develop a risk-averse safety analysis method for stochastic systems on discrete infinite time horizons. Our method quantifies the notion of risk for a control system in terms of the severity of a harmful random outcome in a fraction of the worst cases. In contrast, classical methods quantify risk in terms of the probability of a harmful event. Our theoretical arguments are based on the analysis of a value iteration algorithm on an augmented state space. We provide conditions to guarantee the existence of an optimal policy on this space. We illustrate the method numerically using an example from the domain of stormwater management.
Nonlinear Controllability Assessment of Aerial Manipulator Systems using Lagrangian Reduction
Authors: Skylar X. Wei, Matthew R. Burkhardt, Joel Burdick
Date: 2021-08-15 00:00:00

This paper analyzes the nonlinear Small-Time Local Controllability (STLC) of a class of underatuated aerial manipulator robots. We apply methods of Lagrangian reduction to obtain their lowest dimensional equations of motion (EOM). The symmetry-breaking potential energy terms are resolved using advected parameters, allowing full $SE(3)$ reduction at the cost of additional advection equations. The reduced EOM highlights the shifting center of gravity due to manipulation and is readily in control-affine form, simplifying the nonlinear controllability analysis. Using Sussmann's sufficient condition, we conclude that the aerial manipulator robots are STLC near equilibrium condition, requiring Lie bracket motions up to degree three.
In-depth Analysis of Durations of Discretionary Lane Changes on Freeway Under Varying Traffic Conditions
Authors: Gen Li, Zhen Yang, Yiyong Pan, Jianxiao Ma
Date: 2021-08-15 00:00:00

This paper aims to investigate the characteristics of durations of discretionary lane changes (LCs) on freeways based on an enriched dataset. A comprehensive analysis of LC durations was conducted based on vehicle types, LC directions and navigation speeds. It was found that the heavy vehicle takes longer time to complete LC maneuver. The LC direction significantly influences the durations of passenger cars but has no significant influence on heavy vehicles. The navigation speed was found to have important influence on LC durations. However, it has different impacts according to vehicle types and LC directions. Further analysis of LC durations at different stages showed that drivers of passenger cars might use different strategies to perform LCs when they change lanes to different directions. However, drivers of heavy vehicles in both directions used less time to occupy the target lanes. Results of this study can be beneficial to understand the mechanism of LC process and the influence of LC on traffic flow.
Relaxation Based Modeling of GMD Induced Cascading Failures in PowerModelsGMD.jl
Authors: Adam Mate, Arthur K. Barnes, Steven K. Morley, Jacob A. Friz-Trillo, Eduardo Cotilla-Sanchez, Sean P. Blake
Date: 2021-08-14 00:00:00

A major risk of geomagnetic disturbances (GMDs) is cascading failure of electrical grids. The modeling of GMD events and cascading outages in power systems is difficult, both independently and jointly, because of the many different mechanisms and physics involved. This paper introduces a relaxation based modeling of GMD-induced cascading failures:~the dc approximation-based DCSIMSEP solver was adapted to simulate cascading as a result of GMDs, the full set of ac power flow equations were relaxed to guarantee optimality, and the reactive power losses were modeled while keeping the problem convex. The developed algorithm was implemented in PowerModelsGMD.jl - an open-source software specifically designed to model and analyze geomagnetic hazards - and demonstrated to work on the RTS-GMLC-GIC-EAST synthetic test network.
Active fault tolerant control for twin wind turbine subject to asymmetric fault
Authors: Mariem Makni, Ihab Haidar, Jean-Pierre Barbot, Franck Plestan, Nabih Feki, Mohamed Slim Abbes
Date: 2021-08-13 00:00:00

This paper addresses the problem of control of a twin wind turbine which is subject to an electrical fault affecting only one stator phase of one turbine. An active fault tolerant control is proposed. The performance and robustness of the proposed control, comparing to a passive fault tolerant one developed in the literature, are shown through numerical simulations.
A Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission
Authors: Carl Shneider, Andong Hu, Ajay K. Tiwari, Monica G. Bobra, Karl Battams, Jannis Teunissen, Enrico Camporeale
Date: 2021-08-04 00:00:00
Tasks: BIG-bench Machine Learning

We present a Python tool to generate a standard dataset from solar images that allows for user-defined selection criteria and a range of pre-processing steps. Our Python tool works with all image products from both the Solar and Heliospheric Observatory (SoHO) and Solar Dynamics Observatory (SDO) missions. We discuss a dataset produced from the SoHO mission's multi-spectral images which is free of missing or corrupt data as well as planetary transits in coronagraph images, and is temporally synced making it ready for input to a machine learning system. Machine-learning-ready images are a valuable resource for the community because they can be used, for example, for forecasting space weather parameters. We illustrate the use of this data with a 3-5 day-ahead forecast of the north-south component of the interplanetary magnetic field (IMF) observed at Lagrange point one (L1). For this use case, we apply a deep convolutional neural network (CNN) to a subset of the full SoHO dataset and compare with baseline results from a Gaussian Naive Bayes classifier.
Statistical modeling of adaptive neural networks explains coexistence of avalanches and oscillations in resting human brain
Authors: Fabrizio Lombardi, Selver Pepić, Oren Shriki, Gašper Tkačik, Daniele De Martino
Date: 2021-08-15 00:00:00

Neurons in the brain are wired into adaptive networks that exhibit a range of collective dynamics. Oscillations, for example, are paradigmatic synchronous patterns of neural activity with a defined temporal scale. Neuronal avalanches, in contrast, are scale-free cascades of neural activity, often considered as evidence of brain tuning to criticality. While models have been developed to account for oscillations or avalanches separately, they typically do not explain both phenomena, are too complex to analyze analytically, or intractable to infer from data rigorously. Here we propose a non-equilibrium feedback-driven Ising like class of neural networks that simultaneously and quantitatively captures scale-free avalanches and scale-specific oscillations. In the most simple yet fully microscopic model version we can analytically compute the phase diagram and make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor oscillations and collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time bins. Importantly, the inferred parameters correlate with model-independent signatures of "closeness to criticality", indicating that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations.