pwc-archive/papers-with-abstracts huggingface dataset:
Smooth Converse Lyapunov-Barrier Theorems for Asymptotic Stability with Safety Constraints and Reach-Avoid-Stay Specifications
Authors: Yiming Meng, Yinan Li, Maxwell Fitzsimmons, Jun Liu
Date: 2020-09-09 00:00:00

Stability and safety are two important aspects in safety-critical control of dynamical systems. It has been a well established fact in control theory that stability properties can be characterized by Lyapunov functions. Reachability properties can also be naturally captured by Lyapunov functions for finite-time stability. Motivated by safety-critical control applications, such as in autonomous systems and robotics, there has been a recent surge of interests in characterizing safety properties using barrier functions. Lyapunov and barrier functions conditions, however, are sometimes viewed as competing objectives. In this paper, we provide a unified theoretical treatment of Lyapunov and barrier functions in terms of converse theorems for stability properties with safety guarantees and reach-avoid-stay type specifications. We show that if a system (modeled as a dynamical system with measurable perturbations) possesses a stability with safety property, then there exists a smooth Lyapunov function to certify such a property. This Lyapunov function is shown to be defined on the entire set of initial conditions from which solutions satisfy this property. A similar but slightly weaker statement is made for reach-avoid-stay specifications. We show by a simple example that the latter statement cannot be strengthened without additional assumptions. We further extend the results for systems with control inputs and prove existence of converse Lyapunov-barrier functions for reach-and-avoid specifications. While the converse Lyapunov-barrier theorems are not constructive, as with classical converse Lyapunov theorems, we believe that the unified necessary and sufficient conditions with a single Lyapunov-barrier function are of theoretical interest and can hopefully shed some light on computational approaches.
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
Authors: Kailun Wu, Zhangming Chan, Weijie Bian, Lejian Ren, Shiming Xiang, Shuguang Han, Hongbo Deng, Bo Zheng
Date: 2021-12-21 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems

Exploration-Exploitation (E{\&}E) algorithms are commonly adopted to deal with the feedback-loop issue in large-scale online recommender systems. Most of existing studies believe that high uncertainty can be a good indicator of potential reward, and thus primarily focus on the estimation of model uncertainty. We argue that such an approach overlooks the subsequent effect of exploration on model training. From the perspective of online learning, the adoption of an exploration strategy would also affect the collecting of training data, which further influences model learning. To understand the interaction between exploration and training, we design a Pseudo-Exploration module that simulates the model updating process after a certain item is explored and the corresponding feedback is received. We further show that such a process is equivalent to adding an adversarial perturbation to the model input, and thereby name our proposed approach as an the Adversarial Gradient Driven Exploration (AGE). For production deployment, we propose a dynamic gating unit to pre-determine the utility of an exploration. This enables us to utilize the limited amount of resources for exploration, and avoid wasting pageview resources on ineffective exploration. The effectiveness of AGE was firstly examined through an extensive number of ablation studies on an academic dataset. Meanwhile, AGE has also been deployed to one of the world-leading display advertising platforms, and we observe significant improvements on various top-line evaluation metrics.
Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions
Authors: Adam Lesnikowski, Gabriel de Souza Pereira Moreira, Sara Rabhi, Karl Byleen-Higley
Date: 2021-12-21 00:00:00
Tasks: Dataset Generation, Privacy Preserving, Recommendation Systems

Synthetic data and simulators have the potential to markedly improve the performance and robustness of recommendation systems. These approaches have already had a beneficial impact in other machine-learning driven fields. We identify and discuss a key trade-off between data fidelity and privacy in the past work on synthetic data and simulators for recommendation systems. For the important use case of predicting algorithm rankings on real data from synthetic data, we provide motivation and current successes versus limitations. Finally we outline a number of exciting future directions for recommendation systems that we believe deserve further attention and work, including mixing real and synthetic data, feedback in dataset generation, robust simulations, and privacy-preserving methods.
Stability-Preserving Automatic Tuning of PID Control with Reinforcement Learning
Authors: Ayub I. Lakhani, Myisha A. Chowdhury, Qiugang Lu
Date: 2021-12-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

PID control has been the dominant control strategy in the process industry due to its simplicity in design and effectiveness in controlling a wide range of processes. However, traditional methods on PID tuning often require extensive domain knowledge and field experience. To address the issue, this work proposes an automatic PID tuning framework based on reinforcement learning (RL), particularly the deterministic policy gradient (DPG) method. Different from existing studies on using RL for PID tuning, in this work, we consider the closed-loop stability throughout the RL-based tuning process. In particular, we propose a novel episodic tuning framework that allows for an episodic closed-loop operation under selected PID parameters where the actor and critic networks are updated once at the end of each episode. To ensure the closed-loop stability during the tuning, we initialize the training with a conservative but stable baseline PID controller and the resultant reward is used as a benchmark score. A supervisor mechanism is used to monitor the running reward (e.g., tracking error) at each step in the episode. As soon as the running reward exceeds the benchmark score, the underlying controller is replaced by the baseline controller as an early correction to prevent instability. Moreover, we use layer normalization to standardize the input to each layer in actor and critic networks to overcome the issue of policy saturation at action bounds, to ensure the convergence to the optimum. The developed methods are validated through setpoint tracking experiments on a second-order plus dead-time system. Simulation results show that with our scheme, the closed-loop stability can be maintained throughout RL explorations and the explored PID parameters by the RL agent converge quickly to the optimum.
Product Form of Projection-Based Model Reduction and its Application to Multi-Agent Systems
Authors: Noam Leiter, Daniel Zelazo
Date: 2021-12-30 00:00:00
Tasks: Form

Orthogonal projection-based reduced order models (PROM) are the output of widely-used model reduction methods. In this work, a novel product form is derived for the reduction error system of these reduced models, and it is shown that any such PROM can be obtained from a sequence of 1-dimensional projection reductions. Investigating the error system product form, we then define interface-invariant PROMs, model order reductions with projection-invariant input and output matrices, and it is shown that for such PROMs the error product systems are strictly proper. Furthermore, exploiting this structure, an analytic $\mathcal{H}_{\infty}$ reduction error bound is obtained and an $\mathcal{H}_{\infty}$ bound optimization problem is defined. Interface-invariant reduced models are natural to graph-based model reduction of multi-agent systems where subsets of agents function as the input and output of the system. In the second part of this study, graph contractions are used as a constructive solution approach to the $\mathcal{H}_{\infty}$ bound optimization problem for multi-agent systems. Edge-based contractions are then utilized in a greedy-edge reduction algorithm and are demonstrated for the model reduction of a first-order Laplacian controlled consensus protocol.
FIFA ranking: Evaluation and path forward
Authors: Leszek Szczecinski, Iris-Ioana Roatis
Date: 2021-12-20 00:00:00

In this work we study the ranking algorithm used by F\'ed\'eration Internationale de Football Association (FIFA); we analyze the parameters it currently uses, show the formal probabilistic model from which it can be derived, and optimize the latter. In particular, analyzing the games since the introduction of the algorithm in 2018, we conclude that the game's "importance" (as defined by FIFA) used in the algorithm is counterproductive from the point of view of the predictive capability of the algorithm. We also postulate the algorithm to be rooted in the formal modelling principle, where the Davidson model proposed in 1970 seems to be an excellent candidate, preserving the form of the algorithm currently used. The results indicate that the predictive capability of the algorithm is notably improved by using the home-field advantage and the explicit model for the draws in the game. Moderate, but notable improvement may be attained by introducing the weighting of the results with the goal differential, which although not rooted in a formal modelling principle, is compatible with the current algorithm and can be tuned to the characteristics of the football competition.
Improved Topic modeling in Twitter through Community Pooling
Authors: Federico Albanese, Esteban Feuerstein
Date: 2021-12-20 00:00:00
Tasks: Retrieval

Social networks play a fundamental role in propagation of information and news. Characterizing the content of the messages becomes vital for different tasks, like breaking news detection, personalized message recommendation, fake users detection, information flow characterization and others. However, Twitter posts are short and often less coherent than other text documents, which makes it challenging to apply text mining algorithms to these datasets efficiently. Tweet-pooling (aggregating tweets into longer documents) has been shown to improve automatic topic decomposition, but the performance achieved in this task varies depending on the pooling method. In this paper, we propose a new pooling scheme for topic modeling in Twitter, which groups tweets whose authors belong to the same community (group of users who mainly interact with each other but not with other groups) on a user interaction graph. We present a complete evaluation of this methodology, state of the art schemes and previous pooling models in terms of the cluster quality, document retrieval tasks performance and supervised machine learning classification score. Results show that our Community polling method outperformed other methods on the majority of metrics in two heterogeneous datasets, while also reducing the running time. This is useful when dealing with big amounts of noisy and short user-generated social media texts. Overall, our findings contribute to an improved methodology for identifying the latent topics in a Twitter dataset, without the need of modifying the basic machinery of a topic decomposition model.
Using Mobility Patterns for the Planning of Vehicle-to-Grid Infrastructures that Support Photovoltaics in Cities
Authors: Markus Schläpfer, Hong Jun Chew, Seanglidet Yean, Bu-Sung Lee
Date: 2021-12-30 00:00:00

The vehicle-to-grid (V2G) concept utilises electric vehicles as distributed energy storage and thus may help to balance out the intermittent availability of renewable energy sources such as photovoltaics. V2G is therefore considered to play an important role for achieving low-carbon energy and transportation systems in cities. However, the adequate planning of city-wide V2G infrastructures requires detailed knowledge of the aggregate mobility patterns of individuals and also needs to keep track with ongoing developments of urban transportation modes. Here, we introduce an initial framework that infers population-wide mobility patterns from anonymised mobile phone location data and subsequently superimposes a vehicle charging and discharging scheme. The framework allows for the estimation of the aggregate V2G energy supply and demand at fine-grained spatial and temporal scales under a given electric vehicle usage scenario. This information provides an adequate basis for assessing the role of V2G in the context of maximising the deployment of photovoltaics, as well as for the sizing and placement of the required vehicle (dis)charging infrastructure. The proposed framework is applied to Singapore as a case study.
Rank4Class: A Ranking Formulation for Multiclass Classification
Authors: Nan Wang, Zhen Qin, Le Yan, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork
Date: 2021-12-17 00:00:00
Tasks: Classification, image-classification, Image Classification, Information Retrieval, Learning-To-Rank, Representation Learning, Retrieval

Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes. In the deep learning era, extensive efforts have been spent on developing more powerful neural embedding models to better represent the instance for improving MCC performance. In this paper, we do not aim to propose new neural models for instance representation learning, but to show that it is promising to boost MCC performance with a novel formulation through the lens of ranking. In particular, by viewing MCC as to rank classes for an instance, we first argue that ranking metrics, such as Normalized Discounted Cumulative Gain, can be more informative than the commonly used Top-$K$ metrics. We further demonstrate that the dominant neural MCC recipe can be transformed to a neural ranking framework. Based on such generalization, we show that it is intuitive to leverage advanced techniques from the learning to rank literature to improve the MCC performance out of the box. Extensive empirical results on both text and image classification tasks with diverse datasets and backbone neural models show the value of our proposed framework.
Data-Driven State Estimation for Light-Emitting Diode Underwater Optical Communication
Authors: Yingquan Li, Zhenwen Liang, Ibrahima N'Doye, Xiangliang Zhang, Mohamed-Slim Alouini, Taous-Meriem Laleg-Kirati
Date: 2021-12-30 00:00:00
Tasks: State Estimation

Light-Emitting Diodes (LEDs) based underwater optical wireless communications (UOWCs), a technology with low latency and high data rates, have attracted significant importance for underwater robots. However, maintaining a controlled line of sight link between transmitter and receiver is challenging due to the constant movement of the underlying optical platform caused by the dynamic uncertainties of the LED model and vibration effects. Additionally, the alignment angle required for tracking is not directly measured and has to be estimated. Besides, the light scattering propagates beam pulse in water temporally, resulting in time-varying underwater optical links with interference. We address the state estimation problem by designing an LED communication system that provides the angular position and velocity information to overcome the challenges. In this way, we leverage the power of deep learning-based observer design to explore the LED communication's state space properly. Simulation results are presented to illustrate the performance of the data-driven LED state estimation.
Product Information Browsing Support System Using Analytic Hierarchy Process
Authors: Weijian Li, Masato Kikuchi, Tadachika Ozono
Date: 2021-12-17 00:00:00
Tasks: Decision Making, Information Retrieval, Retrieval

Large-scale e-commerce sites can collect and analyze a large number of user preferences and behaviors, and thus can recommend highly trusted products to users. However, it is very difficult for individuals or non-corporate groups to obtain large-scale user data. Therefore, we consider whether knowledge of the decision-making domain can be used to obtain user preferences and combine it with content-based filtering to design an information retrieval system. This study describes the process of building a product information browsing support system with high satisfaction based on product similarity and multiple other perspectives about products on the Internet. We present the architecture of the proposed system and explain the working principle of its constituent modules. Finally, we demonstrate the effectiveness of the proposed system through an evaluation experiment and a questionnaire.
On-Policy Robust Adaptive Discrete-Time Regulator for Passive Unidirectional System using Stochastic Hill-climbing Algorithm and Associated Search Element
Authors: Mohsen Jafarzadeh, Nicholas Gans, Yonas Tadesse
Date: 2021-12-30 00:00:00

Non-linear discrete-time state-feedback regulators are widely used in passive unidirectional systems. Offline system identification is required for tuning parameters of these regulators. However, offline system identification is challenging in some applications. Furthermore, the parameters of a system may be slowly changing over time, which makes the system identification less effective. Many adaptive regulators have been proposed to tune the parameters online when the offline information is neither accessible nor time-invariant. Stability and convergence of these adaptive regulators are challenging, especially in unidirectional systems. In this paper, a novel adaptive regulator is proposed for first-order unidirectional passive systems. In this method, an associated search element checks the eligibility of the update law. Then, a stochastic hill-climbing algorithm updates the parameters of the discrete-time state-feedback regulator. Simulation results demonstrate the effectiveness of the proposed method. The experiments on regulating of two passive systems show the ability of the method in regulating of passive unidirectional system in the presence of noise and disturbance.
On Mathematics of Bubbles in Dynamical Systems
Authors: Petr Klan
Date: 2021-12-29 00:00:00

A new concept called biased derivative is proposed. It has a potential to better understand and model some aspects of dynamical systems associated with creating bubbles.
Theoretical Development and Numerical Validation of an Asymmetric Linear Bilateral Control Model- Case Study for an Automated Truck Platoon
Authors: M Sabbir Salek, Mashrur Chowdhury, Mizanur Rahman, Kakan Dey, Md Rafiul Islam
Date: 2021-12-29 00:00:00

In this paper, we theoretically develop and numerically validate an asymmetric linear bilateral control model (LBCM). The novelty of the asymmetric LBCM is that using this model all the follower vehicles in a platoon can adjust their acceleration and deceleration to closely follow a constant desired time gap to improve platoon operational efficiency while maintaining local and string stability. We theoretically analyze the local stability of the asymmetric LBCM using the condition for asymptotic stability of a linear time-invariant system and prove the string stability of the asymmetric LBCM using a space gap error attenuation approach. Then, we evaluate the efficacy of the asymmetric LBCM by simulating a closely coupled cooperative adaptive cruise control (CACC) platoon of fully automated trucks in various non-linear acceleration and deceleration states. We choose automated truck platooning as a case study since heavy-duty trucks experience higher delays and lags in the powertrain system, and limited acceleration and deceleration capabilities than passenger cars. To evaluate the platoon operational efficiency of the asymmetric LBCM, we compare the performance of the asymmetric LBCM to a baseline model, i.e., the symmetric LBCM, for different powertrain delays and lags. Our analyses found that the asymmetric LBCM can handle any combined powertrain delays and lags up to 0.6 sec while maintaining a constant desired time gap during a stable platoon operation, whereas the symmetric LBCM fails to ensure stable platoon operation as well as maintain a constant desired time gap for any combined powertrain delays and lags over 0.2 sec. These findings demonstrate the potential of the asymmetric LBCM in improving platoon operational efficiency and stability of an automated truck platoon.
Search Strategy Formulation for Systematic Reviews: issues, challenges and opportunities
Authors: Andrew MacFarlane, Tony Russell-Rose, Farhad Shokraneh
Date: 2021-12-17 00:00:00

Systematic literature reviews play a vital role in identifying the best available evidence for health and social care policy. The resources required to produce systematic reviews can be significant, and a key to the success of any review is the search strategy used to identify relevant literature. However, the methods used to construct search strategies can be complex, time consuming, resource intensive and error prone. In this review, we examine the state of the art in resolving complex structured information needs, focusing primarily on the healthcare context. We analyse the literature to identify key challenges and issues and explore appropriate solutions and workarounds. From this analysis we propose a way forward to facilitate trust and transparency and to aid explainability, reproducibility and replicability through a set of key design principles for tools to support the development of search strategies in systematic literature reviews.
Design and Implementation of Data-driven Predictive Cloud Control System
Authors: Runze Gao, Yuanqing Xia, Li Dai, Zhongqi Sun
Date: 2021-12-29 00:00:00
Tasks: Cloud Computing

Nowadays, the rapid increases of the scale and complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems. Cloud computing is concerned as a powerful solution to handle the complex large-scale control missions using sufficient computing resources. However, the developed computing ability enables more complex devices and mass data being involved and thus the applications of model-based algorithms are constrained. Motivated by the above, we propose an original data-driven predictive cloud control system. To achieve the proposed system, a practical data-driven predictive cloud control platform rather than only a numerical simulator is established and together a cloud-edge communication scheme is developed. Finally, the verification of simulations and experiments as well as discussions demonstrate the effectiveness of the proposed system.