pwc-archive/papers-with-abstracts huggingface dataset:
On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization
Authors: Yuetian Luo, Xudong Li, Anru R. Zhang
Date: 2021-10-23 00:00:00
Tasks: Riemannian optimization

In this paper, we propose a general procedure for establishing the geometric landscape connections of a Riemannian optimization problem under the embedded and quotient geometries. By applying the general procedure to the fixed-rank positive semidefinite (PSD) and general matrix optimization, we establish an exact Riemannian gradient connection under two geometries at every point on the manifold and sandwich inequalities between the spectra of Riemannian Hessians at Riemannian first-order stationary points (FOSPs). These results immediately imply an equivalence on the sets of Riemannian FOSPs, Riemannian second-order stationary points (SOSPs), and strict saddles of fixed-rank matrix optimization under the embedded and the quotient geometries. To the best of our knowledge, this is the first geometric landscape connection between the embedded and the quotient geometries for fixed-rank matrix optimization and it provides a concrete example of how these two geometries are connected in Riemannian optimization. In addition, the effects of the Riemannian metric and quotient structure on the landscape connection are discussed. We also observe an algorithmic connection between two geometries with some specific Riemannian metrics in fixed-rank matrix optimization: there is an equivalence between gradient flows under two geometries with shared spectra of Riemannian Hessians. A number of novel ideas and technical ingredients including a unified treatment for different Riemannian metrics, novel metrics for the Stiefel manifold, and new horizontal space representations under quotient geometries are developed to obtain our results. The results in this paper deepen our understanding of geometric and algorithmic connections of Riemannian optimization under different Riemannian geometries and provide a few new theoretical insights to unanswered questions in the literature.
Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding
Authors: Qiyu Hu, Yunlong Cai, Kai Kang, Guanding Yu, Jakob Hoydis, Yonina C. Eldar
Date: 2021-10-22 00:00:00

In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, which consists of deep neural network (DNN)-aided pilot training, channel feedback, and hybrid analog-digital (HAD) precoding. Specifically, we develop a DNN architecture that maps the received pilots into feedback bits at the receiver, and then further maps the feedback bits into the hybrid precoder at the transmitter. To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed. The analog precoders are designed by the long-term DNN based on the CSI statistics and updated once in a frame consisting of a number of time slots. In contrast, the digital precoders are optimized by the short-term DNN at each time slot based on the estimated low-dimensional equivalent CSI matrices. A two-timescale training method is also developed for the proposed DNN with a binary layer. We then analyze the generalization ability and signaling overhead for the proposed DNN based algorithm. Simulation results show that our proposed technique significantly outperforms conventional schemes in terms of bit-error rate performance with reduced signaling overhead and shorter pilot sequences.
Parallel Feedforward Compensation for Output Synchronization: Fully Distributed Control and Indefinite Laplacian
Authors: Mengmou Li, Ioannis Lestas, Li Qiu
Date: 2021-10-25 00:00:00
Tasks: Distributed Optimization

This work is associated with the use of parallel feedforward compensators (PFCs) for the problem of output synchronization over heterogeneous agents and the benefits this approach can provide. Specifically, it addresses the addition of stable PFCs on agents that interact with each other using diffusive couplings. The value in the application of such PFC is twofold. Firstly, it has been an issue that output synchronization among passivity-short systems requires global information for the design of controllers in the cases when initial conditions need to be taken into account, such as average consensus and distributed optimization. We show that a stable PFC can be designed to passivate a passivity-short system while its output asymptotically vanishes as its input tends to zero. As a result, output synchronization is achieved among these systems by fully distributed controls without altering the original consensus results. Secondly, in the literature of output synchronization over signed weighted graphs, it is generally required that the graph Laplacian be positive semidefinite, i.e., $L \geq 0$ for undirected graphs or $L + L^T \geq 0$ for balanced directed graphs. We show that the PFC serves as output feedback to the communication graph to enhance the robustness against negative weight edges. As a result, output synchronization is achieved over a signed weighted and balanced graph, even if the corresponding Laplacian is not positive semidefinite.
Interaction and Conflict Management in AI-assisted Operational Control Loops in 6G
Authors: Saeedeh Parsaeefard, Pooyan Habibi, Alberto Leon Garcia
Date: 2021-10-22 00:00:00
Tasks: Management, Scheduling

This paper studies autonomous and AI-assisted control loops (ACLs) in the next generation of wireless networks in the lens of multi-agent environments. We will study the diverse interactions and conflict management among these loops. We propose "interaction and conflict management" (ICM) modules to achieve coherent, consistent and interactions among these ACLs. We introduce three categories of ACLs based on their sizes, their cooperative and competitive behaviors, and their sharing of datasets and models. These categories help to introduce conflict resolution and interaction management mechanisms for ICM. Using Kubernetes, we present an implementation of ICM to remove the conflicts in the scheduling and rescheduling of Pods for different ACLs in networks.
Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments
Authors: Abdulaziz Shamsah, Zhaoyuan Gu, Jonas Warnke, Seth Hutchinson, Ye Zhao
Date: 2021-10-23 00:00:00
Tasks: Motion Planning, Task and Motion Planning

This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level task planner employs linear temporal logic (LTL) for a reactive game synthesis between the robot and its environment and provides a formal guarantee on navigation safety and task completion. To address environmental partial observability, a belief abstraction is employed at the high-level navigation planner to estimate the dynamic obstacles' location. Accordingly, a synthesized action planner sends a set of locomotion actions to the middle-level motion planner, while incorporating safe locomotion specifications extracted from safety theorems based on a reduced-order model (ROM) of the locomotion process. The motion planner employs the ROM to design safety criteria and a sampling algorithm to generate non-periodic motion plans that accurately track high-level actions. At the low level, a foot placement controller based on an angular-momentum linear inverted pendulum model is implemented and integrated with an ankle-actuated passivity-based controller for full-body trajectory tracking. To address external perturbations, this study also investigates safe sequential composition of the keyframe locomotion state and achieves robust transitions against external perturbations through reachability analysis. The overall TAMP framework is validated with extensive simulations and hardware experiments on bipedal walking robots Cassie and Digit designed by Agility Robotics.
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Authors: Jiachen Li, Shuo Cheng, Zhenyu Liao, Huayan Wang, William Yang Wang, Qinxun Bai
Date: 2021-10-22 00:00:00
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Improving the sample efficiency of reinforcement learning algorithms requires effective exploration. Following the principle of $\textit{optimism in the face of uncertainty}$ (OFU), we train a separate exploration policy to maximize the approximate upper confidence bound of the critics in an off-policy actor-critic framework. However, this introduces extra differences between the replay buffer and the target policy regarding their stationary state-action distributions. To mitigate the off-policy-ness, we adapt the recently introduced DICE framework to learn a distribution correction ratio for off-policy RL training. In particular, we correct the training distribution for both policies and critics. Empirically, we evaluate our proposed method in several challenging continuous control tasks and show superior performance compared to state-of-the-art methods. We also conduct extensive ablation studies to demonstrate the effectiveness and rationality of the proposed method.
Automatic Impact-sounding Acoustic Inspection of Concrete Structure
Authors: Jinglun Feng, Hua Xiao, Ejup Hoxha, Yifeng Song, Liang Yang, Jizhong Xiao
Date: 2021-10-25 00:00:00
Tasks: Position, Simultaneous Localization and Mapping

Impact sounding signal has been shown to contain information about structural integrity flaws and subsurface objects from previous research. As non-destructive testing (NDT) method, one of the biggest challenges in impact sounding based inspection is the subsurface targets detection and reconstruction. This paper presents the importance and practicability of using solenoids to trigger impact sounding signal and using acoustic data to reconstruct subsurface objects to address this issue. First, by taking advantage of Visual Simultaneous Localization and Mapping (V-SLAM), we could obtain the 3D position of the robot during the inspection. Second, our NDE method is based on Frequency Density (FD) analysis for the Fast Fourier Transform (FFT) of the impact sounding signal. At last, by combining the 3D position data and acoustic data, this paper creates a 3D map to highlight the possible subsurface objects. The experimental results demonstrate the feasibility of the method.
On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-varying Sampling Rate Offsets and Speaker Changes
Authors: Tobias Gburrek, Joerg Schmalenstroeer, Reinhold Haeb-Umbach
Date: 2021-10-25 00:00:00

A wireless acoustic sensor network records audio signals with sampling time and sampling rate offsets between the audio streams, if the analog-digital converters (ADCs) of the network devices are not synchronized. Here, we introduce a new sampling rate offset model to simulate time-varying sampling frequencies caused, for example, by temperature changes of ADC crystal oscillators, and propose an estimation algorithm to handle this dynamic aspect in combination with changing acoustic source positions. Furthermore, we show how deep neural network based estimates of the distances between microphones and human speakers can be used to determine the sampling time offsets. This enables a synchronization of the audio streams to reflect the physical time differences of flight.
Controllable and Interpretable Singing Voice Decomposition via Assem-VC
Authors: Kang-wook Kim, Junhyeok Lee
Date: 2021-10-25 00:00:00
Tasks: Voice Conversion

We propose a singing decomposition system that encodes time-aligned linguistic content, pitch, and source speaker identity via Assem-VC. With decomposed speaker-independent information and the target speaker's embedding, we could synthesize the singing voice of the target speaker. In conclusion, we made a perfectly synced duet with the user's singing voice and the target singer's converted singing voice.
A Study of Acoustic Features in Arabic Speaker Identification under Noisy Environmental Conditions
Authors: Zhor Benhafid, Kawthar Yasmine Zergat, Abderrahmane Amrouche
Date: 2021-10-23 00:00:00
Tasks: Speaker Identification

One of the major parts of the voice recognition field is the choice of acoustic features which have to be robust against the variability of the speech signal, mismatched conditions, and noisy environments. Thus, different speech feature extraction techniques have been developed. In this paper, we investigate the robustness of several front-end techniques in Arabic speaker identification. We evaluate five different features in babble, factory and subway conditions at the various signal to noise ratios (SNR). The obtained results showed that two of the auditory feature i.e. gammatone frequency cepstral coefficient (GFCC) and power normalization cepstral coefficients (PNCC), unlike their combination performs substantially better than a conventional speaker features i.e. Mel-frequency cepstral coefficients (MFCC).
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification
Authors: Yu Wang, Charu Aggarwal, Tyler Derr
Date: 2021-10-22 00:00:00
Tasks: Classification, Graph Neural Network, Metric Learning, Node Classification, Self-Supervised Learning

Recent years have witnessed the significant success of applying graph neural networks (GNNs) in learning effective node representations for classification. However, current GNNs are mostly built under the balanced data-splitting, which is inconsistent with many real-world networks where the number of training nodes can be extremely imbalanced among the classes. Thus, directly utilizing current GNNs on imbalanced data would generate coarse representations of nodes in minority classes and ultimately compromise the classification performance. This therefore portends the importance of developing effective GNNs for handling imbalanced graph data. In this work, we propose a novel Distance-wise Prototypical Graph Neural Network (DPGNN), which proposes a class prototype-driven training to balance the training loss between majority and minority classes and then leverages distance metric learning to differentiate the contributions of different dimensions of representations and fully encode the relative position of each node to each class prototype. Moreover, we design a new imbalanced label propagation mechanism to derive extra supervision from unlabeled nodes and employ self-supervised learning to smooth representations of adjacent nodes while separating inter-class prototypes. Comprehensive node classification experiments and parameter analysis on multiple networks are conducted and the proposed DPGNN almost always significantly outperforms all other baselines, which demonstrates its effectiveness in imbalanced node classification. The implementation of DPGNN is available at \url{https://github.com/YuWVandy/DPGNN}.
The network signature of constellation line figures
Authors: Doina Bucur
Date: 2021-10-19 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

In traditional astronomies across the world, groups of stars in the night sky were linked into constellations -- symbolic representations rich in meaning and with practical roles. In some sky cultures, constellations are represented as line (or connect-the-dot) figures, which are spatial networks drawn over the fixed background of stars. We analyse 1802 line figures from 56 sky cultures spanning all continents, in terms of their network, spatial, and brightness features, and ask what associations exist between these visual features and culture type or sky region. First, an embedded map of constellations is learnt, to show clusters of line figures. We then form the network of constellations (as linked by their similarity), to study how similar cultures are by computing their assortativity (or homophily) over the network. Finally, we measure the diversity (or entropy) index for the set of constellations drawn per sky region. Our results show distinct types of line figures, and that many folk astronomies with oral traditions have widespread similarities in constellation design, which do not align with cultural ancestry. In a minority of sky regions, certain line designs appear universal, but this is not the norm: in the majority of sky regions, the line geometries are diverse.
Multi-vehicle experiment platform: A Digital Twin Realization Method
Authors: Chunying Yang, Jianghong Dong, Qing Xu, Mengchi Cai, Hongmao Qin, Jianqiang Wang, Keqiang Li
Date: 2021-10-25 00:00:00
Tasks: Sand

With the development of V2X technology, multiple vehicles cooperative control has been widely studied. However, filed testing is rarely conducted due to financial and safety considerations. To solve this problem, this study proposes a digital twin method to carry out multi-vehicle experiments, which uses combination of physical and virtual vehicles to perform coordination tasks. To confirm effectiveness of this method, a prototype system is developed, which consists of sand table testbed, its twin system and cloud. Several aspects are quantified to describe system performance, including time delay and localization accuracy. Finally, a vehicle level experiment in platoon scenario is carried out and experiment results confirm effectiveness of this method.
Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision
Authors: Hanmin Cai, Philipp Heer
Date: 2021-10-25 00:00:00
Tasks: energy management, Management, Model Predictive Control

Active building energy management holds potential to reduce global energy-related emissions and support flexible operations of future low-carbon systems. This requires to integrate diverse objectives and engage multiple stakeholders. However, there remains a gap in comprehensive field insights into emission reduction, flexibility provision, and user impacts. This study examined how a real occupied building, with all its energy assets, could function as an emission-aware flexible prosumer. An existing building energy management system was enhanced by integrating a model predictive control strategy. The enhanced setup minimized the equivalent carbon emission due to electricity imports and provided flexibility to the energy system. The experimental results indicated an emission reduction of 12.5% compared to a rule-based controller that maximized PV self-consumption. In addition, a minimal flexibility provision experiment was demonstrated with a locally emulated distribution system operator. The results suggested that flexibility was provided without the risk of rebound effects. This is due to the flexibility envelope that was self-reported in advance. The study concluded by highlighting technical challenges in realizing emission reduction and flexibility in practice.
Continuous reset element: Transient and steady-state analysis for precision motion systems
Authors: Nima Karbasizadeh, S. Hassan HosseinNia
Date: 2021-10-25 00:00:00

This paper addresses the main goal of using reset control in precision motion control systems, breaking of the well-known "Waterbed effect". A new architecture for reset elements will be introduced which has a continuous output signal as opposed to conventional reset elements. A steady-state precision study is presented, showing the steady-state precision is preserved while the peak of sensitivity is reduced. The architecture is then used for a "Constant in Gain Lead in Phase" (CgLp) element and a numerical analysis on transient response shows a significant improvement in transient response. It is shown that by following the presented guideline for tuning, settling time can be reduced and at the same time a non-overshoot step response can be achieved. A practical example is presented to verify the results and also to show that the proposed element can achieve a complex-order behaviour.
Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes
Authors: Nami Hekmat, Hanmin Cai, Thierry Zufferey, Gabriela Hug, Philipp Heer
Date: 2021-10-25 00:00:00

Real-time quantification of residential building energy flexibility is needed to enable a cost-efficient operation of active distribution grids. A promising means is to use the so-called flexibility envelope concept to represent the time-dependent and inter-temporally coupled flexibility potential. However, existing optimization-based quantification entails high computational burdens limiting flexibility utilization in real-time applications, and a more computationally efficient quantification approach is desired. Additionally, the communication of a flexibility envelope to system operators in its original form is data-intensive. In order to address the computational burdens, this paper first trains several machine learning models based on historical quantification results for online use. Subsequently, probability distribution functions are proposed to approximate the flexibility envelopes with significantly fewer parameters, which can be communicated to system operators instead of the original flexibility envelope. The results show that the most promising prediction and approximation approaches allow for a minimum reduction of the computational burden by a factor of 9 and of the communication load by a factor of 6.6, respectively.
Data-driven Control of Dynamic Event-triggered Systems with Delays
Authors: Xin Wang, Jian Sun, Julian Berberich, Gang Wang, Frank Allgöwer, Jie Chen
Date: 2021-10-25 00:00:00

This paper studies data-driven control of unknown sampled-data systems with communication delays under an event-triggering transmission mechanism. Data-based representations for time-invariant linear systems with known or unknown system input matrices are first developed, along with a novel class of dynamic triggering schemes for sampled-data systems with time delays. A model-based stability condition for the resulting event-triggered time-delay system is established using a looped-functional approach. Combining this model-based condition with the data-driven representations, data-based stability conditions are derived. Building on the data-based conditions, methods for co-designing the controller gain and the event-triggering matrix are subsequently provided for both cases with or without using the input matrix. Finally, numerical examples are presented to corroborate the usefulness of additional prior knowledge of the input matrix in reducing the conservatism of stability conditions, as well as the merits of the proposed data-driven event-triggered control schemes relative to existing results.
Data-Driven Resilient Predictive Control under Denial-of-Service
Authors: Wenjie Liu, Jian Sun, Gang Wang, Francesco Bullo, Jie Chen
Date: 2021-10-25 00:00:00
Tasks: Model Predictive Control

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method.