pwc-archive/papers-with-abstracts huggingface dataset:
Efficient learning of hidden state LTI state space models of unknown order
Authors: Boualem Djehiche, Othmane Mazhar
Date: 2022-02-03 00:00:00
Tasks: State Space Models

The aim of this paper is to address two related estimation problems arising in the setup of hidden state linear time invariant (LTI) state space systems when the dimension of the hidden state is unknown. Namely, the estimation of any finite number of the system's Markov parameters and the estimation of a minimal realization for the system, both from the partial observation of a single trajectory. For both problems, we provide statistical guarantees in the form of various estimation error upper bounds, $\rank$ recovery conditions, and sample complexity estimates. Specifically, we first show that the low $\rank$ solution of the Hankel penalized least square estimator satisfies an estimation error in $S_p$-norms for $p \in [1,2]$ that captures the effect of the system order better than the existing operator norm upper bound for the simple least square. We then provide a stability analysis for an estimation procedure based on a variant of the Ho-Kalman algorithm that improves both the dependence on the dimension and the least singular value of the Hankel matrix of the Markov parameters. Finally, we propose an estimation algorithm for the minimal realization that uses both the Hankel penalized least square estimator and the Ho-Kalman based estimation procedure and guarantees with high probability that we recover the correct order of the system and satisfies a new fast rate in the $S_2$-norm with a polynomial reduction in the dependence on the dimension and other parameters of the problem.
The RoyalFlush System of Speech Recognition for M2MeT Challenge
Authors: Shuaishuai Ye, Peiyao Wang, Shunfei Chen, Xinhui Hu, Xinkang Xu
Date: 2022-02-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Decoder, Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation

This paper describes our RoyalFlush system for the track of multi-speaker automatic speech recognition (ASR) in the M2MeT challenge. We adopted the serialized output training (SOT) based multi-speakers ASR system with large-scale simulation data. Firstly, we investigated a set of front-end methods, including multi-channel weighted predicted error (WPE), beamforming, speech separation, speech enhancement and so on, to process training, validation and test sets. But we only selected WPE and beamforming as our frontend methods according to their experimental results. Secondly, we made great efforts in the data augmentation for multi-speaker ASR, mainly including adding noise and reverberation, overlapped speech simulation, multi-channel speech simulation, speed perturbation, front-end processing, and so on, which brought us a great performance improvement. Finally, in order to make full use of the performance complementary of different model architecture, we trained the standard conformer based joint CTC/Attention (Conformer) and U2++ ASR model with a bidirectional attention decoder, a modification of Conformer, to fuse their results. Comparing with the official baseline system, our system got a 12.22% absolute Character Error Rate (CER) reduction on the validation set and 12.11% on the test set.
Deep Layer-wise Networks Have Closed-Form Weights
Authors: Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy
Date: 2022-02-01 00:00:00
Tasks: Form

There is currently a debate within the neuroscience community over the likelihood of the brain performing backpropagation (BP). To better mimic the brain, training a network \textit{one layer at a time} with only a "single forward pass" has been proposed as an alternative to bypass BP; we refer to these networks as "layer-wise" networks. We continue the work on layer-wise networks by answering two outstanding questions. First, $\textit{do they have a closed-form solution?}$ Second, $\textit{how do we know when to stop adding more layers?}$ This work proves that the Kernel Mean Embedding is the closed-form weight that achieves the network global optimum while driving these networks to converge towards a highly desirable kernel for classification; we call it the $\textit{Neural Indicator Kernel}$.
Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition
Authors: Md Mofijul Islam, Tariq Iqbal
Date: 2021-04-01 00:00:00
Proceeding: IEEE ROBOTICS AND AUTOMATION LETTERS 2021 4
Tasks: Activity Recognition, Human Activity Recognition, Mixture-of-Experts, Multimodal Activity Recognition, Representation Learning

Recognizing human activities is one of the crucial capabilities that a robot needs to have to be useful around people. Although modern robots are equipped with various types of sensors, human activity recognition (HAR) still remains a challenging problem, particularly in the presence of noisy sensor data. In this work, we introduce a multimodal graphical attention-based HAR approach, called Multi-GAT, which hierarchically learns complementary multimodal features. We develop a multimodal mixture-of-experts model to disentangle and extract salient modality-specific features that enable feature interactions. Additionally, we introduce a novel message-passing based graphical attention approach to capture cross-modal relation for extracting complementary multimodal features. The experimental results on two multimodal human activity datasets suggest that Multi-GAT outperformed state-of-the-art HAR algorithms across all datasets and metrics tested. Finally, the experimental results with noisy sensor data indicate that Multi-GAT consistently outperforms all the evaluated baselines. The robust performance suggests that Multi-GAT can enable seamless human-robot collaboration in noisy human environments.
MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion
Authors: Md Mofijul Islam, Tariq Iqbal
Date: 2022-02-22 00:00:00
Proceeding: AAAI 2022 2
Tasks: Activity Recognition, Human Activity Recognition, Multimodal Activity Recognition

Multimodal sensors (visual, non-visual, and wearable) can provide complementary information to develop robust perception systems for recognizing activities accurately. However, it is challenging to extract robust multimodal representations due to the heterogeneous characteristics of data from multimodal sensors and disparate human activities, especially in the presence of noisy and misaligned sensor data. In this work, we propose a cooperative multitask learning-based guided multimodal fusion approach, MuMu, to extract robust multimodal representations for human activity recognition (HAR). MuMu employs an auxiliary task learning approach to extract features specific to each set of activities with shared characteristics (activity-group). MuMu then utilizes activity-group-specific features to direct our proposed Guided Multimodal Fusion Approach (GM-Fusion) for extracting complementary multimodal representations, designed as the target task. We evaluated MuMu by comparing its performance to state-of-the-art multimodal HAR approaches on three activity datasets. Our extensive experimental results suggest that MuMu outperforms all the evaluated approaches across all three datasets. Additionally, the ablation study suggests that MuMu significantly outperforms the baseline models (p<0.05), which do not use our guided multimodal fusion. Finally, the robust performance of MuMu on noisy and misaligned sensor data posits that our approach is suitable for HAR in real-world settings.
Jamming Resilient Indoor Factory Deployments: Design and Performance Evaluation
Authors: Leonardo Chiarello, Paolo Baracca, Karthik Upadhya, Saeed R. Khosravirad, Silvio Mandelli, Thorsten Wild
Date: 2022-02-02 00:00:00
Tasks: Scheduling

In the framework of 5G-and-beyond Industry 4.0, jamming attacks for denial of service are a rising threat which can severely compromise the system performance. Therefore, in this paper we deal with the problem of jamming detection and mitigation in indoor factory deployments. We design two jamming detectors based on pseudo-random blanking of subcarriers with orthogonal frequency division multiplexing and consider jamming mitigation with frequency hopping and random scheduling of the user equipments. We then evaluate the performance of the system in terms of achievable BLER with ultra-reliable low-latency communications traffic and jamming missed detection probability. Simulations are performed considering a 3rd Generation Partnership Project spatial channel model for the factory floor with a jammer stationed outside the plant trying to disrupt the communication inside the factory. Numerical results show that jamming resiliency increases when using a distributed access point deployment and exploiting channel correlation among antennas for jamming detection, while frequency hopping is helpful in jamming mitigation only for strict BLER requirements.
The relationship between sentiment score and COVID-19 cases in the United States
Authors: Truong Luu, Rosangela Follmann
Date: 2022-01-09 00:00:00

The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders, and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19 related tweets. We connect users' sentiment with COVID-19 cases across the USA and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemic-related legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal
Authors: Tong Mu, Stephan Zheng, Alexander Trott
Date: 2022-01-18 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning

Principal-Agent (PA) problems describe a broad class of economic relationships characterized by misaligned incentives and asymmetric information. The Principal's problem is to find optimal incentives given the available information, e.g., a manager setting optimal wages for its employees. Whereas the Principal is often assumed rational, comparatively little is known about solutions when the Principal is boundedly rational, especially in the sequential setting, with multiple Agents, and with multiple information channels. Here, we develop RIRL, a deep reinforcement learning framework that solves such complex PA problems with a rationally inattentive Principal. Such a Principal incurs a cost for paying attention to information, which can model forms of bounded rationality. We use RIRL to analyze rich economic phenomena in manager-employee relationships. In the single-step setting, 1) RIRL yields wages that are consistent with theoretical predictions; and 2) non-zero attention costs lead to simpler but less profitable wage structures, and increased Agent welfare. In a sequential setting with multiple Agents, RIRL shows opposing consequences of the Principal's inattention to different information channels: 1) inattention to Agents' outputs closes wage gaps based on ability differences; and 2) inattention to Agents' efforts induces a social dilemma dynamic in which Agents work harder, but essentially for free. Moreover, RIRL reveals non-trivial relationships between the Principal's inattention and Agent types, e.g., if Agents are prone to sub-optimal effort choices, payment schedules are more sensitive to the Principal's attention cost. As such, RIRL can reveal novel economic relationships and enables progress towards understanding the effects of bounded rationality in dynamic settings.
Variational integrators for non-autonomous systems with applications to stabilization of multi-agent formations
Authors: Leonardo Colombo, Manuela Gamonal Fernández, David Martín de Diego
Date: 2022-02-03 00:00:00

Numerical methods that preserve geometric invariants of the system, such as energy, momentum or the symplectic form, are called geometric integrators. Variational integrators are an important class of geometric integrators. The general idea for those variational integrators is to discretize Hamilton's principle rather than the equations of motion in a way that preserves some of the invariants of the original system. In this paper we construct variational integrators with fixed time step for time-dependent Lagrangian systems modelling an important class of autonomous dissipative systems. These integrators are derived via a family of discrete Lagrangian functions each one for a fixed time-step. This allows to recover at each step on the set of discrete sequences the preservation properties of variational integrators for autonomous Lagrangian systems, such as symplecticity or backward error analysis for these systems. We also present a discrete Noether theorem for this class of systems. Applications of the results are shown for the problem of formation stabilization of multi-agent systems.
A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation
Authors: Linjuan Cheng, Chengshi Zheng, Andong Li, Yuquan Wu, Renhua Peng, XiaoDong Li
Date: 2022-02-03 00:00:00
Tasks: Acoustic echo cancellation

In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, various types of noise in communication environments further reduce speech quality and intelligibility. It is difficult to extract the near-end signal from the microphone signal within one step, especially in low signal-to-noise ratio scenarios. In this paper, we propose a deep complex network approach to address this issue. Specially, we decompose the stereophonic acoustic echo cancellation into two stages, including linear stereophonic acoustic echo cancellation module and residual echo suppression module, where both modules are based on deep learning architectures. A multi-frame filtering strategy is introduced to benefit the estimation of linear echo by capturing more inter-frame information. Moreover, we decouple the complex spectral mapping into magnitude estimation and complex spectrum refinement. Experimental results demonstrate that our proposed approach achieves stage-of-the-art performance over previous advanced algorithms under various conditions.
A User-Friendly Environment for Battery Data Science
Authors: Robert Masse, Dan Ulery, Hardik Kamdar
Date: 2022-02-03 00:00:00
Tasks: Management

We report a user-friendly software environment for battery data science. It is designed to streamline data management, data cleaning, and data analysis to help bridge the gap between the domain expertise of most battery scientists and the tools needed as the field becomes increasingly data intensive. The software solution suitable for ingesting battery test data from disparate sources. By aggregating data in an intelligent way, users can streamline routine data analysis tasks and leverage Jupyter Notebook functionality to build advanced scripts and analytics, thereby making battery engineering teams more productive.
Enhancement of power grid monitoring based on data weighting
Authors: Parisa Ataeian, Abbas Rabiee, Mehdi Derafshian Maram, Mohsen Ghalei Monfared Zanjani
Date: 2022-02-03 00:00:00
Tasks: energy management, Management

With their expansion, national power grid have had to work with huge sets of data received from a vast number of substations and power plants. Given their large volume and variety, these data can be classified as big data. Managing this massive amount of data is certainly challenging. Depending on the application, parts of these data are more important for real-time network operation. Computing a network's observability score without assigning weights to different signals may not provide a complete picture of the received data's validity and thus lead to incorrect assessments of the network status. Consequently, signals critical to the network operation and functions of an energy management system (EMS) should be assigned higher weights in observability calculations. The weighted observability alongside the classic non-weighted observability can serve as an indicator of each area's condition in comparison to that of other areas and so greatly facilitate the monitoring and verification of transmitted data. For calculating a weighted observability, the current paper presents a method based on the Analytic Hierarchy Process (AHP), in which higher weights are assigned to data that are more valuable for and widely used by operators. The national electricity network of Iran was chosen for the evaluation of the proposed method's effect on data quality and operational risk. The results indicated that the introduced method positively affects the quality of received data and also corrects erroneous data in the network.
Comparison of Intelligent Approaches for Cycle Time Prediction in Injection Moulding of a Medical Device Product
Authors: Mandana Kariminejad, David Tormey, Saif Huq, Jim Morrison, Marion McAfee
Date: 2022-02-03 00:00:00

Injection moulding is an increasingly automated industrial process, particularly when used for the production of high-value precision components such as polymeric medical devices. In such applications, achieving stringent product quality demands whilst also ensuring a highly efficient process can be challenging. Cycle time is one of the most critical factors which directly affects the throughput rate of the process and hence is a key indicator of process efficiency. In this work, we examine a production data set from a real industrial injection moulding process for manufacture of a high precision medical device. The relationship between the process input variables and the resulting cycle time is mapped with an artificial neural network (ANN) and an adaptive neuro-fuzzy system (ANFIS). The predictive performance of different training methods and neuron numbers in ANN and the impact of model type and the numbers of membership functions in ANFIS has been investigated. The strengths and limitations of the approaches are presented and the further research and development needed to ensure practical on-line use of these methods for dynamic process optimisation in the industrial process are discussed.
Variable Slope Trapezoidal Circulating Current Injection to Attenuate Capacitor Voltage Ripple in Modular Multilevel Converter Based Variable Speed Motor Drives Application
Authors: Kranthi Panuganti, Rohan Sandeep Burye, Sheron Figarado
Date: 2022-02-03 00:00:00

The main challenge in using the Modular Multilevel Converter-based constant-torque variable-speed motor drives is increased sub-module capacitor voltage ripples (SM-CVR) at lowfundamental frequency operation, due to the inverse relationship between SM-CVR and operating frequency. To address this issue, a variable slope trapezoidal circulating current (CC) is injected along with square wave common-mode voltage (CMV). Compared to sinusoidal CC and sinusoidal CMV injection, the proposed injection technique can reduce the peak of the CC in the range of 0% to 50%, resulting in lesser device stress and improved efficiency. Simulation results of the proposed technique are presented, and they are further compared with the existing injection techniques to show the superiority
Investigation of Torque Ripple in Voltage Source Inverter driven Induction Motor Drive operated with Space Vector based Harmonic Elimination Pulse Width Modulation Scheme
Authors: Rohan Sandeep Burye, Ravi Teja Arumalla, Sheron Figarado
Date: 2022-02-03 00:00:00

The lower order harmonic elimination using space vector pulse width modulation (SVPWM) technique is possible by introducing the dwell time division coefficient 'k' in space vector. In this paper, torque ripple analysis is carried out for space vector based harmonic elimination (SVHE) scheme and the obtained results are compared with the existing schemes such as conventional and advanced bus clamping SVPWM schemes. Further, the obtained results are validated through simulation results. The SVHE scheme gives the lower torque ripple than the conventional and advance bus clamping schemes at higher modulation indices.
Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer
Authors: Ali Wadi, Shayok Mukhopadhyay, Lotfi Romdhane
Date: 2022-02-03 00:00:00
Tasks: Friction

This paper proposes a friction model parameter identification routine that can work with highly nonlinear and chaotic systems. The chosen system for this study is a passively-actuated tilted Furuta pendulum, which is known to have a highly non linear and coupled model. The pendulum is tilted to ensure the existence of a stable equilibrium configuration for all its degrees of freedom, and the link weights are the only external forces applied to the system. A nonlinear analytical model of the pendulum is derived, and a continuous friction model considering static friction, dynamic friction, viscous friction, and the stribeck effect is selected from the literature. A high-gain Universal Adaptive Stabilizer (UAS) observer is designed to identify friction model parameters using joint angle measurements. The methodology is tested in simulation and validated on an experimental setup. Despite the high nonlinearity of the system, the methodology is proven to converge to the exact parameter values, in simulation, and to yield qualitative parameter magnitudes in experiments where the goodness of fit was around 85\% on average. The discrepancy between the simulation and the experimental results is attributed to the limitations of the friction model. The main advantage of the proposed method is the significant reduction in computational needs and the time required relative to conventional optimization-based identification routines. The proposed approach yielded more than 99\% reduction in the estimation time while being considerably more accurate than the optimization approach in every test performed. One more advantage is that the approach can be easily adapted to fit other models to experimental data.
Safe Learning for Uncertainty-Aware Planning via Interval MDP Abstraction
Authors: Jesse Jiang, Ye Zhao, Samuel Coogan
Date: 2022-02-03 00:00:00
Tasks: Robot Navigation

We study the problem of refining satisfiability bounds for partially-known stochastic systems against planning specifications defined using syntactically co-safe Linear Temporal Logic (scLTL). We propose an abstraction-based approach that iteratively generates high-confidence Interval Markov Decision Process (IMDP) abstractions of the system from high-confidence bounds on the unknown component of the dynamics obtained via Gaussian process regression. In particular, we develop a synthesis strategy to sample the unknown dynamics by finding paths which avoid specification-violating states using a product IMDP. We further provide a heuristic to choose among various candidate paths to maximize the information gain. Finally, we propose an iterative algorithm to synthesize a satisfying control policy for the product IMDP system. We demonstrate our work with a case study on mobile robot navigation.