pwc-archive/papers-with-abstracts huggingface dataset:
CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation
Authors: Xu Xie, Zhaoyang Liu, Shiwen Wu, Fei Sun, Cihang Liu, Jiawei Chen, Jinyang Gao, Bin Cui, Bolin Ding
Date: 2021-05-28 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clicks and purchases). However, they overlook the fact that users may purchase the items even without recommendations. To select these effective items, it is essential to estimate the causal effect of recommendations. The real effective items are the ones which can contribute to purchase probability uplift. Nevertheless, it is difficult to obtain the real causal effect since we can only recommend or not recommend an item to a user at one time. Furthermore, previous works usually rely on the randomized controlled trial~(RCT) experiment to evaluate their performance. However, it is usually not practicable in the recommendation scenario due to its unavailable time consuming. To tackle these problems, in this paper, we propose a causal collaborative filtering~(CausCF) method inspired by the widely adopted collaborative filtering~(CF) technique. It is based on the idea that similar users not only have a similar taste on items, but also have similar treatment effect under recommendations. CausCF extends the classical matrix factorization to the tensor factorization with three dimensions -- user, item, and treatment. Furthermore, we also employs regression discontinuity design (RDD) to evaluate the precision of the estimated causal effects from different models. With the testable assumptions, RDD analysis can provide an unbiased causal conclusion without RCT experiments. Through dedicated experiments on both the public datasets and the industrial application, we demonstrate the effectiveness of our proposed CausCF on the causal effect estimation and ranking performance improvement.
An In-Memory Analog Computing Co-Processor for Energy-Efficient CNN Inference on Mobile Devices
Authors: Mohammed Elbtity, Abhishek Singh, Brendan Reidy, Xiaochen Guo, Ramtin Zand
Date: 2021-05-24 00:00:00
Tasks: CPU

In this paper, we develop an in-memory analog computing (IMAC) architecture realizing both synaptic behavior and activation functions within non-volatile memory arrays. Spin-orbit torque magnetoresistive random-access memory (SOT-MRAM) devices are leveraged to realize sigmoidal neurons as well as binarized synapses. First, it is shown the proposed IMAC architecture can be utilized to realize a multilayer perceptron (MLP) classifier achieving orders of magnitude performance improvement compared to previous mixed-signal and digital implementations. Next, a heterogeneous mixed-signal and mixed-precision CPU-IMAC architecture is proposed for convolutional neural networks (CNNs) inference on mobile processors, in which IMAC is designed as a co-processor to realize fully-connected (FC) layers whereas convolution layers are executed in CPU. Architecture-level analytical models are developed to evaluate the performance and energy consumption of the CPU-IMAC architecture. Simulation results exhibit 6.5% and 10% energy savings for CPU-IMAC based realizations of LeNet and VGG CNN models, for MNIST and CIFAR-10 pattern recognition tasks, respectively.
Explainable Enterprise Credit Rating via Deep Feature Crossing Network
Authors: Weiyu Guo, Zhijiang Yang, Shu Wu, Fu Chen
Date: 2021-05-22 00:00:00

Due to the powerful learning ability on high-rank and non-linear features, deep neural networks (DNNs) are being applied to data mining and machine learning in various fields, and exhibit higher discrimination performance than conventional methods. However, the applications based on DNNs are rare in enterprise credit rating tasks because most of DNNs employ the "end-to-end" learning paradigm, which outputs the high-rank representations of objects and predictive results without any explanations. Thus, users in the financial industry cannot understand how these high-rank representations are generated, what do they mean and what relations exist with the raw inputs. Then users cannot determine whether the predictions provided by DNNs are reliable, and not trust the predictions providing by such "black box" models. Therefore, in this paper, we propose a novel network to explicitly model the enterprise credit rating problem using DNNs and attention mechanisms. The proposed model realizes explainable enterprise credit ratings. Experimental results obtained on real-world enterprise datasets verify that the proposed approach achieves higher performance than conventional methods, and provides insights into individual rating results and the reliability of model training.
Distributed adaptive stabilization
Authors: Zhiyong Sun, Anders Rantzer, Zhongkui Li, Anders Robertsson
Date: 2021-05-28 00:00:00

In this paper we consider distributed adaptive stabilization for uncertain multivariable linear systems with a time-varying diagonal matrix gain. We show that uncertain multivariable linear systems are stabilizable by diagonal matrix high gains if the system matrix is an H-matrix with positive diagonal entries. Based on matrix measure and stability theory for diagonally dominant systems, we consider two classes of uncertain linear systems, and derive a threshold condition to ensure their exponential stability by a monotonically increasing diagonal gain matrix. When each individual gain function in the matrix gain is updated by state-dependent functions using only local state information, the boundedness and convergence of both system states and adaptive matrix gains are guaranteed. We apply the adaptive distributed stabilization approach to adaptive synchronization control for large-scale complex networks consisting of nonlinear node dynamics and time-varying coupling weights. A unified framework for adaptive synchronization is proposed that includes several general design approaches for adaptive coupling weights to guarantee network synchronization.
Wireless for Control: Over-the-Air Controller
Authors: Pangun Park, Piergiuseppe Di Marco, Carlo Fischione
Date: 2021-05-27 00:00:00

In closed-loop wireless control systems, the state-of-the-art approach prescribes that a controller receives by wireless communications the individual sensor measurements, and then sends the computed control signal to the actuators. We propose an over-the-air controller scheme where all sensors attached to the plant simultaneously transmit scaled sensing signals directly to the actuator; then the feedback control signal is computed partially over the air and partially by a scaling operation at the actuator. Such over-the-air controller essentially adopts the over-the-air computation concept to compute the control signal for closed-loop wireless control systems. In contrast to the state-of-the-art sensor-to-controller and controller-to-actuator communication approach, the over-the-air controller exploits the superposition properties of multiple-access wireless channels to complete the communication and computation of a large number of sensing signals in a single communication resource unit. Therefore, the proposed scheme can obtain significant benefits in terms of low actuation delay and low wireless resource utilization by a simple network architecture that does not require a dedicated controller. Numerical results show that our proposed over-the-air controller achieves a huge widening of the stability region in terms of sampling time and delay, and a significant reduction of the computation error of the control signal.
End-to-End Deep Fault Tolerant Control
Authors: Daulet Baimukashev, Bexultan Rakhim, Matteo Rubagotti, Huseyin Atakan Varol
Date: 2021-05-28 00:00:00
Tasks: Fault Detection

PUBLISHED ON IEEE/ASME TRANSACTIONS ON MECHATRONICS, DOI: 10.1109/TMECH.2021.3100150. Ideally, accurate sensor measurements are needed to achieve a good performance in the closed-loop control of mechatronic systems. As a consequence, sensor faults will prevent the system from working correctly, unless a fault-tolerant control (FTC) architecture is adopted. As model-based FTC algorithms for nonlinear systems are often challenging to design, this paper focuses on a new method for FTC in the presence of sensor faults, based on deep learning. The considered approach replaces the phases of fault detection and isolation and controller design with a single recurrent neural network, which has the value of past sensor measurements in a given time window as input, and the current values of the control variables as output. This end-to-end deep FTC method is applied to a mechatronic system composed of a spherical inverted pendulum, whose configuration is changed via reaction wheels, in turn actuated by electric motors. The simulation and experimental results show that the proposed method can handle abrupt faults occurring in link position/velocity sensors. The provided supplementary material includes a video of real-world experiments and the software source code.
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions
Authors: Shanshan Wang, Toni Heittola, Annamaria Mesaros, Tuomas Virtanen
Date: 2021-05-28 00:00:00
Tasks: Classification, Data Augmentation, Scene Classification, Transfer Learning

This paper presents the details of the Audio-Visual Scene Classification task in the DCASE 2021 Challenge (Task 1 Subtask B). The task is concerned with classification using audio and video modalities, using a dataset of synchronized recordings. This task has attracted 43 submissions from 13 different teams around the world. Among all submissions, more than half of the submitted systems have better performance than the baseline. The common techniques among the top systems are the usage of large pretrained models such as ResNet or EfficientNet which are trained for the task-specific problem. Fine-tuning, transfer learning, and data augmentation techniques are also employed to boost the performance. More importantly, multi-modal methods using both audio and video are employed by all the top 5 teams. The best system among all achieved a logloss of 0.195 and accuracy of 93.8%, compared to the baseline system with logloss of 0.662 and accuracy of 77.1%.
Reset control systems: the zero-crossing resetting law
Authors: Alfonso Baños, Antonio Barreiro
Date: 2021-05-28 00:00:00

A novel representation of reset control systems with a zero-crossing resetting law, in the framework of hybrid inclusions, is postulated. The problems of well-posedness and stability of the resulting hybrid dynamical system are investigated, with a strong motivation in how non-deterministic behavior is accomplished in control practice. Several stability conditions, based on the eigenstructure of matrices related with periods of the reset interval sequences, and on Lyapunov function-based conditions, are developed.
Inaccuracy matters: accounting for solution accuracy in event-triggered nonlinear model predictive control
Authors: Omar J. Faqir, Eric C. Kerrigan
Date: 2021-05-28 00:00:00
Tasks: Model Predictive Control

We consider the effect of using approximate system predictions in event-triggered control schemes. Such approximations may result from using numerical transcription methods for solving continuous-time optimal control problems. Mesh refinement can guarantee upper bounds on the error in the differential equations which model the system dynamics. With the accuracy guarantees of a mesh refinement scheme, we show that the proposed event-triggering scheme -- which compares the measured system with approximate state predictions -- can be used with a guaranteed strictly positive inter-update time. We show that if we have knowledge of the employed transcription scheme or the approximation errors, then we can obtain better online estimates of inter-update times. We additionally detail a method of tightening constraints on the approximate system trajectory used in the nonlinear programming problem to guarantee constraint satisfaction of the continuous-time system. This is the first work to incorporate prediction accuracy in triggering metrics. Using the solution accuracy we can guarantee reliable lower bounds for inter-update times and perform solution dependent constraint tightening.
Control Architecture of the Double-Cross-Correlation Processor for Sampling-Rate-Offset Estimation in Acoustic Sensor Networks
Authors: Aleksej Chinaev, Sven Wienand, Gerald Enzner
Date: 2021-05-28 00:00:00
Tasks: Retrieval

Distributed hardware of acoustic sensor networks bears inconsistency of local sampling frequencies, which is detrimental to signal processing. Fundamentally, sampling rate offset (SRO) nonlinearly relates the discrete-time signals acquired by different sensor nodes. As such, retrieval of SRO from the available signals requires nonlinear estimation, like double-cross-correlation processing (DXCP), and frequently results in biased estimation. SRO compensation by asynchronous sampling rate conversion (ASRC) on the signals then leaves an unacceptable residual. As a remedy to this problem, multi-stage procedures have been devised to diminish the SRO residual with multiple iterations of SRO estimation and ASRC over the entire signal. This paper converts the mechanism of offline multi-stage processing into a continuous feedback-control loop comprising a controlled ASRC unit followed by an online implementation of DXCP-based SRO estimation. To support the design of an optimum internal model control unit for this closed-loop system, the paper deploys an analytical dynamical model of the proposed online DXCP. The resulting control architecture then merely applies a single treatment of each signal frame, while efficiently diminishing SRO bias with time. Evaluations with both speech and Gaussian input demonstrate that the high accuracy of multi-stage processing is maintained at the low complexity of single-stage (open-loop) processing.
Linear-Quadratic regulators for internal boundary control of lane-free automated vehicle traffic
Authors: Milad Malekzadeh, Ioannis Papamichail, Markos Papageorgiou
Date: 2020-12-31 00:00:00

Lane-free vehicle movement has been recently proposed for connected automated vehicles (CAV) due to various potential advantages. One such advantage stems from the fact that incremental changes of the road width in lane-free traffic lead to corresponding incremental changes of the traffic flow capacity. Based on this property, the concept of internal boundary control was recently introduced to flexibly share the total road width and capacity among the two traffic directions of a highway in real-time, in response to the prevailing traffic conditions, so as to maximize the cross-road (both directions) infrastructure utilization. Feedback-based Linear-Quadratic regulators with or without Integral action (LQI and LQ regulators) are appropriately developed in this paper to efficiently address the internal boundary control problem. Simulation investigations, involving a realistic highway stretch and different demand scenarios, demonstrate that the proposed simple regulators are robust and similarly efficient as an open-loop nonlinear constrained optimal control solution, while circumventing the need for accurate modelling and external demand prediction
On-site Online Feature Selection for Classification of Switchgear Actuations
Authors: Christina Nicolaou, Ahmad Mansour, Kristof Van Laerhoven
Date: 2021-05-28 00:00:00
Tasks: Classification, feature selection

As connected sensors continue to evolve, interest in low-voltage monitoring solutions is increasing. This also applies in the area of switchgear monitoring, where the detection of switch actions, their differentiation and aging are of fundamental interest. In particular, the universal applicability for various types of construction plays a major role. Methods in which design-specific features are learned in an offline training are therefore less suitable for assessing the condition of switchgears. A new computational efficient method for intelligent online feature selection is presented, which can be used to train a model for the addressed use cases on-site. Process- and design-specific features can be learned locally (e.g. on a sensor system) without the need of prior offline training. The proposed method is evaluated on four datasets of switchgear measurements, which were recorded using microelectromechanical system (MEMS) based sensors (acoustic and vibration). Furthermore, we show that the features selected by our method can be used to track changes in switching processes due to aging effects.
Essential System Services in Grids Dominated by Renewable Energy
Authors: Niraj Lal, Toby Price, Leon Kwek, Christopher Wilson, Farhad Billimoria, Trent Morrow, Matt Garbutt, Dean Sharafi
Date: 2021-05-28 00:00:00

As the proportion of variable inverter-based renewable energy generation in electricity systems increases from a minority to the majority of total supply, the complexity and cost of providing ancillary system services increases in parallel. Australia is experiencing this shift now - from having the third most carbon-intensive electricity sector in the world in 2010, to now having penetrations of variable renewable energy (VRE) regularly reaching 100 percent in some regions, with world-leading uptake of distributed energy resources (DER). This paper presents pioneering work exploring new technical, economic and regulatory frameworks for the provision of Essential System Services (ESS), also known as ancillary services, in power systems dominated by variable inverter-based renewable energy resources. We explore the recent application of the concept of demand curves and nomograms to the procurement of ESS, and emerging design principles for frameworks in facilitating the evolution from default provision of system services by synchronous generation, to co-optimisation of services through unit commitment, to independent provision of services through inverter based resources. The paper also analyses the recent technical and financial success of the world's largest battery, the Hornsdale Power Reserve in South Australia, and its ability to inform how future electricity market frameworks may incentivize and accommodate new technological capability, both first and Nth of a kind systems. Finally, the paper reviews emerging energy system technological capability, including the provision of synthetic inertia and RoCoF control, grid-forming inverters, and advanced DER aggregation in providing ESS and system restart capability for secure, resilient and island-able grids.
Reduced Complexity Neural Network Equalizers for Two-dimensional Magnetic Recording
Authors: Ahmed Aboutaleb, Nitin Nangare
Date: 2021-05-27 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper investigates reduced complexity neural network (NN) based architectures for equalization over the two-dimension magnetic recording (TDMR) digital communication channel for data storage. We use realistic waveforms measured from a hard disk drive (HDD) with TDMR technology. We show that the multilayer perceptron (MLP) non-linear equalizer achieves a $10.91\%$ reduction in bit error rate (BER) over the linear equalizer with cross-entropy-based optimization. However, the MLP equalizer's complexity is $6.6$ times the linear equalizer's complexity. Thus, we propose reduced complexity MLP (RC-MLP) equalizers. Each RC-MLP variant consists of finite-impulse response filters, a non-linear activation, and a hidden delay line. A proposed RC-MLP variant entails only $1.59$ times the linear equalizer's complexity while achieving a $8.23\%$ reduction in BER over the linear equalizer.
Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems
Authors: Hang Shuai, Fangxing, Li, Hector Pulgar-Painemal, Yaosuo Xue
Date: 2021-05-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Scheduling

This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties. The developed deep reinforcement learning (DRL) based microgrid online optimization strategy can achieve a linear increase in the number of neural network outputs with the number of distributed BESSs, which overcomes the curse of dimensionality caused by the charge and discharge decisions of multiple BESSs. Numerical simulations validate the effectiveness of the proposed method.
A comparison between central- and self-dispatch storage management principles in island systems
Authors: Georgios N. Psarros, Pantelis A. Dratsas, Stavros A. Papathanassiou
Date: 2021-05-27 00:00:00
Tasks: Management

This paper presents a comparative evaluation of central and self-dispatch management concepts for battery energy storage (BES) facilities in island power systems with a high renewable energy source (RES) penetration. BES facilities deployed to support the integration of additional wind capacity can be either centrally dispatched by the island System Operator or they can be self-dispatched within a Virtual Power Plant entity comprising renewables and storage, called a Hybrid Power Station (HPS). To explore the anticipated benefits of each BES management paradigm, annual simulations are performed for an example island system, employing a three-layer mixed integer linear programming (MILP) method to simulate the unit commitment and economic dispatch processes. The levelized cost of energy (LCOE) of combined BES and renewables investments is calculated and the achieved RES penetration levels and island system generation cost are evaluated, allowing the identification of Pareto optimal storage configurations, leading to lowest LCOE for a given RES penetration target. Overall, the centrally dispatched BES systems prove to be substantially more cost-effective, compared to the self-dispatched alternative, for achieving similar RES penetration levels.
Computation of invariant sets via immersion for discrete-time nonlinear systems
Authors: Zheming Wang, Raphaël M. Jungers, Chong-Jin Ong
Date: 2020-10-02 00:00:00

In this paper, we propose an approach for computing invariant sets of discrete-time nonlinear systems by lifting the nonlinear dynamics into a higher dimensional linear model. In particular, we focus on the \emph{maximal admissible invariant set} contained in some given constraint set. For special types of nonlinear systems, which can be exactly immersed into higher dimensional linear systems with state transformations, invariant sets of the original nonlinear system can be characterized using the higher dimensional linear representation. For general nonlinear systems without the immersibility property, \emph{approximate immersions} are defined in a local region within some tolerance and linear approximations are computed by leveraging the fixed-point iteration technique for invariant sets. Given the bound on the mismatch between the linear approximation and the original system, we provide an invariant inner approximation of the \emph{maximal admissible invariant set} by a tightening procedure.
Observability Conditions and Sensing Quality for Unicycle Systems with Constant External Forcing
Authors: Natalie Brace, Kristi A. Morgansen
Date: 2021-05-27 00:00:00

In certain systems which are subject to significant constant external forcing such as an airplane in wind or an underwater glider in ocean currents, the ability to detect the forcing depends on both the measurements available and whether appropriate control is being applied. Using analytical nonlinear observability techniques, we define the necessary characteristics required of a measurement function for systems with unicycle dynamics subject to constant forcing to be observable. We further consider the necessary associated motion characteristics required of this class of systems to enable the desired sensing capabilities. We then apply these results in combination with the empirical Gramian to quantify relative observability, optimal sensor selection, and sensing quality for different motion primitive modes for a Dubins path.