pwc-archive/papers-with-abstracts huggingface dataset:
Robust Generalised Bayesian Inference for Intractable Likelihoods
Authors: Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, Chris. J. Oates
Date: 2021-04-15 00:00:00
Tasks: Bayesian Inference

Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specification of the likelihood. Here we consider generalised Bayesian inference with a Stein discrepancy as a loss function, motivated by applications in which the likelihood contains an intractable normalisation constant. In this context, the Stein discrepancy circumvents evaluation of the normalisation constant and produces generalised posteriors that are either closed form or accessible using standard Markov chain Monte Carlo. On a theoretical level, we show consistency, asymptotic normality, and bias-robustness of the generalised posterior, highlighting how these properties are impacted by the choice of Stein discrepancy. Then, we provide numerical experiments on a range of intractable distributions, including applications to kernel-based exponential family models and non-Gaussian graphical models.
Internet of quantum blockchains: security modeling and dynamic resource pricing for stable digital currency
Authors: Wanyang Dai
Date: 2021-04-15 00:00:00

Internet of quantum blockchains (IoB) will be the future Internet. In this paper, we make two new contributions to IoB: developing a block based quantum channel networking technology to handle its security modeling in face of the quantum supremacy and establishing IoB based FinTech platform model with dynamic pricing for stable digital currency. The interaction between our new contributions is also addressed. In doing so, we establish a generalized IoB security model by quantum channel networking in terms of both time and space quantum entanglements with quantum key distribution (QKD). Our IoB can interact with general structured things (e.g., supply chain systems) having online trading and payment capability via stable digital currency and can handle vector-valued data streams requiring synchronized services. Thus, within our designed QKD, a generalized random number generator for private and public keys is proposed by a mixed zero-sum and non-zero-sum resource-competition pricing policy. The effectiveness of this policy is justified by diffusion modeling with approximation theory and numerical implementations.
Hyperbolic Neural Collaborative Recommender
Authors: Anchen Li, Bo Yang, Hongxu Chen, Guandong Xu
Date: 2021-04-15 00:00:00
Tasks: Collaborative Filtering, Representation Learning

This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relations among users/items for collaborative filtering (CF) tasks. HNCR contains two major phases: neighbor construction and recommendation framework. The first phase introduces a neighbor construction strategy to construct a semantic neighbor set for each user and item according to the user-item historical interaction. In the second phase, we develop a deep framework based on hyperbolic geometry to integrate constructed neighbor sets into recommendation. Via a series of extensive experiments, we show that HNCR outperforms its Euclidean counterpart and state-of-the-art baselines.
Empowering News Recommendation with Pre-trained Language Models
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Date: 2021-04-15 00:00:00
Tasks: Articles, Natural Language Understanding, News Recommendation

Personalized news recommendation is an essential technique for online news services. News articles usually contain rich textual content, and accurate news modeling is important for personalized news recommendation. Existing news recommendation methods mainly model news texts based on traditional text modeling methods, which is not optimal for mining the deep semantic information in news texts. Pre-trained language models (PLMs) are powerful for natural language understanding, which has the potential for better news modeling. However, there is no public report that show PLMs have been applied to news recommendation. In this paper, we report our work on exploiting pre-trained language models to empower news recommendation. Offline experimental results on both monolingual and multilingual news recommendation datasets show that leveraging PLMs for news modeling can effectively improve the performance of news recommendation. Our PLM-empowered news recommendation models have been deployed to the Microsoft News platform, and achieved significant gains in terms of both click and pageview in both English-speaking and global markets.
MM-Rec: Multimodal News Recommendation
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Date: 2021-04-15 00:00:00
Tasks: News Recommendation, object-detection, Object Detection

Accurate news representation is critical for news recommendation. Most of existing news representation methods learn news representations only from news texts while ignore the visual information in news like images. In fact, users may click news not only because of the interest in news titles but also due to the attraction of news images. Thus, images are useful for representing news and predicting user behaviors. In this paper, we propose a multimodal news recommendation method, which can incorporate both textual and visual information of news to learn multimodal news representations. We first extract region-of-interests (ROIs) from news images via object detection. Then we use a pre-trained visiolinguistic model to encode both news texts and news image ROIs and model their inherent relatedness using co-attentional Transformers. In addition, we propose a crossmodal candidate-aware attention network to select relevant historical clicked news for accurate user modeling by measuring the crossmodal relatedness between clicked news and candidate news. Experiments validate that incorporating multimodal news information can effectively improve news recommendation.
Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Date: 2021-04-15 00:00:00
Proceeding: Findings (ACL) 2022 5
Tasks: News Recommendation, Recommendation Systems

Recall and ranking are two critical steps in personalized news recommendation. Most existing news recommender systems conduct personalized news recall and ranking separately with different models. However, maintaining multiple models leads to high computational cost and poses great challenge to meeting the online latency requirement of news recommender systems. In order to handle this problem, in this paper we propose UniRec, a unified method for recall and ranking in news recommendation. In our method, we first infer user embedding for ranking from the historical news click behaviors of a user using a user encoder model. Then we derive the user embedding for recall from the obtained user embedding for ranking by using it as the attention query to select a set of basis user embeddings which encode different general user interests and synthesize them into a user embedding for recall. The extensive experiments on benchmark dataset demonstrate that our method can improve both efficiency and effectiveness for recall and ranking in news recommendation.
Dynamic Graph Neural Networks for Sequential Recommendation
Authors: Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang
Date: 2021-04-15 00:00:00
Tasks: Graph Attention, Graph Neural Network, Link Prediction, Sequential Recommendation

Modeling user preference from his historical sequences is one of the core problems of sequential recommendation. Existing methods in this field are widely distributed from conventional methods to deep learning methods. However, most of them only model users' interests within their own sequences and ignore the dynamic collaborative signals among different user sequences, making it insufficient to explore users' preferences. We take inspiration from dynamic graph neural networks to cope with this challenge, modeling the user sequence and dynamic collaborative signals into one framework. We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information. Furthermore, we design a Dynamic Graph Recommendation Network to extract user's preferences from the dynamic graph. Consequently, the next-item prediction task in sequential recommendation is converted into a link prediction between the user node and the item node in a dynamic graph. Extensive experiments on three public benchmarks show that DGSR outperforms several state-of-the-art methods. Further studies demonstrate the rationality and effectiveness of modeling user sequences through a dynamic graph.
DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation
Authors: Jingwei Yi, Fangzhao Wu, Chuhan Wu, Qifei Li, Guangzhong Sun, Xing Xie
Date: 2021-04-15 00:00:00
Tasks: Articles, News Recommendation

News recommendation is critical for personalized news access. Existing news recommendation methods usually infer users' personal interest based on their historical clicked news, and train the news recommendation models by predicting future news clicks. A core assumption behind these methods is that news click behaviors can indicate user interest. However, in practical scenarios, beyond the relevance between user interest and news content, the news click behaviors may also be affected by other factors, such as the bias of news presentation in the online platform. For example, news with higher positions and larger sizes are usually more likely to be clicked. The bias of clicked news may bring noises to user interest modeling and model training, which may hurt the performance of the news recommendation model. In this paper, we propose a bias-aware personalized news recommendation method named DebiasRec, which can handle the bias information for more accurate user interest inference and model training. The core of our method includes a bias representation module, a bias-aware user modeling module, and a bias-aware click prediction module. The bias representation module is used to model different kinds of news bias and their interactions to capture their joint effect on click behaviors. The bias-aware user modeling module aims to infer users' debiased interest from the clicked news articles by using their bias information to calibrate the interest model. The bias-aware click prediction module is used to train a debiased news recommendation model from the biased click behaviors, where the click score is decomposed into a preference score indicating user's interest in the news content and a news bias score inferred from its different bias features. Experiments on two real-world datasets show that our method can effectively improve the performance of news recommendation.
A Large-Scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search
Authors: Svitlana Vakulenko, Evangelos Kanoulas, Maarten de Rijke
Date: 2021-04-14 00:00:00
Tasks: Conversational Search

Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this paper we help to position it with respect to other research areas within conversational Artificial Intelligence (AI) by analysing the structural properties of an information-seeking dialogue. To this end, we perform a large-scale dialogue analysis of more than 150K transcripts from 16 publicly available dialogue datasets. These datasets were collected to inform different dialogue-based tasks including conversational search. We extract different patterns of mixed initiative from these dialogue transcripts and use them to compare dialogues of different types. Moreover, we contrast the patterns found in information-seeking dialogues that are being used for research purposes with the patterns found in virtual reference interviews that were conducted by professional librarians. The insights we provide (1) establish close relations between conversational search and other conversational AI tasks; and (2) uncover limitations of existing conversational datasets to inform future data collection tasks.
EnvGAN: Adversarial Synthesis of Environmental Sounds for Data Augmentation
Authors: Aswathy Madhu, Suresh K
Date: 2021-04-15 00:00:00
Tasks: Data Augmentation, Environmental Sound Classification, Sound Classification

The research in Environmental Sound Classification (ESC) has been progressively growing with the emergence of deep learning algorithms. However, data scarcity poses a major hurdle for any huge advance in this domain. Data augmentation offers an excellent solution to this problem. While Generative Adversarial Networks (GANs) have been successful in generating synthetic speech and sounds of musical instruments, they have hardly been applied to the generation of environmental sounds. This paper presents EnvGAN, the first ever application of GANs for the adversarial generation of environmental sounds. Our experiments on three standard ESC datasets illustrate that the EnvGAN can synthesize audio similar to the ones in the datasets. The suggested method of augmentation outshines most of the futuristic techniques for audio augmentation.
Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale
Authors: Michael Neumann, Oliver Roesler, Jackson Liscombe, Hardik Kothare, David Suendermann-Oeft, David Pautler, Indu Navar, Aria Anvar, Jochen Kumm, Raquel Norel, Ernest Fraenkel, Alexander V. Sherman, James D. Berry, Gary L. Pattee, Jun Wang, Jordan R. Green, Vikram Ramanarayanan
Date: 2021-04-15 00:00:00
Tasks: Specificity

We propose a cloud-based multimodal dialog platform for the remote assessment and monitoring of Amyotrophic Lateral Sclerosis (ALS) at scale. This paper presents our vision, technology setup, and an initial investigation of the efficacy of the various acoustic and visual speech metrics automatically extracted by the platform. 82 healthy controls and 54 people with ALS (pALS) were instructed to interact with the platform and completed a battery of speaking tasks designed to probe the acoustic, articulatory, phonatory, and respiratory aspects of their speech. We find that multiple acoustic (rate, duration, voicing) and visual (higher order statistics of the jaw and lip) speech metrics show statistically significant differences between controls, bulbar symptomatic and bulbar pre-symptomatic patients. We report on the sensitivity and specificity of these metrics using five-fold cross-validation. We further conducted a LASSO-LARS regression analysis to uncover the relative contributions of various acoustic and visual features in predicting the severity of patients' ALS (as measured by their self-reported ALSFRS-R scores). Our results provide encouraging evidence of the utility of automatically extracted audiovisual analytics for scalable remote patient assessment and monitoring in ALS.
Quantifying firm-level economic systemic risk from nation-wide supply networks
Authors: Christian Diem, András Borsos, Tobias Reisch, János Kertész, Stefan Thurner
Date: 2021-04-15 00:00:00
Tasks: Management

Crises like COVID-19 or the Japanese earthquake in 2011 exposed the fragility of corporate supply networks. The production of goods and services is a highly interdependent process and can be severely impacted by the default of critical suppliers or customers. While knowing the impact of individual companies on national economies is a prerequisite for efficient risk management, the quantitative assessment of the involved economic systemic risks (ESR) is hitherto practically non-existent, mainly because of a lack of fine-grained data in combination with coherent methods. Based on a unique value added tax dataset we derive the detailed production network of an entire country and present a novel approach for computing the ESR of all individual firms. We demonstrate that a tiny fraction (0.035%) of companies has extraordinarily high systemic risk impacting about 23% of the national economic production should any of them default. Firm size alone cannot explain the ESR of individual companies; their position in the production networks does matter substantially. If companies are ranked according to their economic systemic risk index (ESRI), firms with a rank above a characteristic value have very similar ESRI values, while for the rest the rank distribution of ESRI decays slowly as a power-law; 99.8% of all companies have an impact on less than 1% of the economy. We show that the assessment of ESR is impossible with aggregate data as used in traditional Input-Output Economics. We discuss how simple policies of introducing supply chain redundancies can reduce ESR of some extremely risky companies.
An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence
Authors: Hongde Wu, Noel E. O'Connor, Jennifer Bruton, Mingming Liu
Date: 2021-04-15 00:00:00
Tasks: Anomaly Detection, Management

In this paper, we investigate a key problem of Internet of Things (IoT) applications in practice. Our research objective is to optimize the transmission frequencies for a group of IoT edge devices under practical constraints. Our key assumption is that different IoT devices may have different priority levels when transmitting data in a resource-constrained environment and that those priority levels may only be locally defined and accessible by edge devices for privacy concerns. To address this problem, we leverage the well-known Alternating Direction Method of Multipliers (ADMM) optimization method and demonstrate its applicability for effectively managing various IoT data streams in a decentralized framework. Our experimental results show that the transmission frequency of each edge device can converge to optimality with little delay using ADMM, and the proposed system can be adjusted dynamically when a new device connects to the system. In addition, we also introduce an anomaly detection mechanism to the system when a device's transmission frequency may be compromised by external manipulation, showing that the proposed system is robust and secure for various IoT applications.
The Effect of Population Flow on Epidemic Spread: Analysis and Control
Authors: Brooks Butler, Ciyuan Zhang, Ian Walter, Nishant Nair, Raphael Stern, Philip E. Paré
Date: 2021-04-15 00:00:00

In this paper, we present a discrete-time networked SEIR model using population flow, its derivation, and assumptions under which this model is well defined. We identify properties of the system's equilibria, namely the healthy states. We show that the set of healthy states is asymptotically stable, and that the value of the equilibria becomes equal across all sub-populations as a result of the network flow model. Furthermore, we explore closed-loop feedback control of the system by limiting flow between sub-populations as a function of the current infected states. These results are illustrated via simulation based on flight traffic between major airports in the United States. We find that a flow restriction strategy combined with a vaccine roll-out significantly reduces the total number of infections over the course of an epidemic, given that the initial flow restriction response is not delayed.
Stability of Finite Horizon Optimisation based Control without Terminal Weight
Authors: Wen-Hua Chen
Date: 2020-11-28 00:00:00
Tasks: Model Predictive Control

This paper presents a stability analysis tool for model predictive control (MPC) where control action is generated by optimising a cost function over a finite horizon. Stability analysis of MPC with a limited horizon but without terminal weight is a well known challenging problem. We define a new value function based on an auxiliary one-step optimisation related to stage cost, namely optimal one-step value function (OSVF). It is shown that a finite horizon MPC can be made to be asymptotically stable if OSVF is a (local) control Lyapunov function (CLF). More specifically, by exploiting the CLF property of OSFV to construct a contractive terminal set, a new stabilising MPC algorithm (CMPC) is proposed. We show that CMPC is recursively feasible and guarantees stability under the condition that OSVF is a CLF. Checking this condition and estimation of the maximal terminal set are discussed. Numerical examples are presented to demonstrate the effectiveness of the proposed stability condition and corresponding CMPC algorithm.
Day-Ahead and Intra-Day Planning of Integrated BESS-PV Systems providing Frequency Regulation
Authors: Francesco Conte, Stefano Massucco, Giacomo-Piero Schiapparelli, Federico Silvestro
Date: 2021-04-15 00:00:00
Tasks: Management, Stochastic Optimization

The paper proposes an optimal management strategy for a system composed by a battery and a photovoltaic power plant. This integrated system is called to deliver the photovoltaic power and to simultaneously provide droop-based primary frequency regulation to the main grid. The battery state-of-energy is controlled by power offset signals, which are determined using photovoltaic energy generation forecasts and predictions of the energy required to operate frequency regulation. A two level control architecture is developed. A day-ahead planning algorithm schedules the energy profile which is traded at the day-ahead market and defines the primary control reserve that the integrated system is able to provide in the considered day. During the day operations, a second level algorithm corrects the dispatched plan using updated information, in order to guarantee a continuous and reliable service. Both control algorithms take into account the uncertainties of the photovoltaic generation and of the frequency dynamics using stochastic optimization.
Optimal Positioning of PMUs for Fault Detection and Localization in Active Distribution Networks
Authors: F. Conte, B. Gabriele, G. -P. Schiapparelli, F. Silvestro, C. Bossi, M. Cabiati
Date: 2021-04-15 00:00:00
Tasks: Fault Detection, Fault localization

This paper considers the problem of fault detection and localization in active distribution networks using PMUs. The proposed algorithm consists in computing a set of weighted least squares state estimates whose results are used to detect, characterize and localize the occurrence of a fault. Moreover, a criteria to minimize the number of PMUs required to correctly perform the proposed algorithm is defined. Such a criteria, based on system observability conditions, allows the design of an optimization problem to set the positions of PMUs along the grid, in order to get the desired fault localization resolution. The performances of the strategy are tested via simulations on a benchmark distribution system.
A Novel Unified Framework for Solving Reachability, Viability and Invariance Problems
Authors: Wei Liao, Taotao Liang, Xiaohui Wei, Jizhou Lai
Date: 2021-04-15 00:00:00

The level set method is a widely used tool for solving reachability and invariance problems. However, some shortcomings, such as the difficulties of handling dissipation function and constructing terminal conditions for solving the Hamilton-Jacobi partial differential equation, limit the application of the level set method in some problems with non-affine nonlinear systems and irregular target sets. This paper proposes a method that can effectively avoid the above tricky issues and thus has better generality. In the proposed method, the reachable or invariant sets with different time horizons are characterized by some non-zero sublevel sets of a value function. This value function is not obtained by solving a viscosity solution of the partial differential equation but by recursion and interpolation approximation. At the end of this paper, some examples are taken to illustrate the accuracy and generality of the proposed method.