pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Reconfiguration of Untimed Discrete-Event Systems
Authors: Matin Macktoobian, W. Murray Wonham
Date: 2022-10-04 00:00:00

This work introduces a general formulation of the reconfiguration problem for untimed discrete-event systems (DES), which can be treated directly by supervisory control theory (SCT). To model the reconfiguration requirements we introduce the concept of reconfiguration specification (RS); here reconfiguration events (RE) are introduced to force a transition from one system configuration to another. Standard SCT synthesis is employed to obtain a reconfiguration supervisor (RSUP) in which designated states serve as the source states for RE. The reconfiguration problem itself is formulated as that of establishing guaranteed finite reachability of a desired RE source state in RSUP from the current state in RSUP at which a change in configuration is commanded by an external user. The solvability (or otherwise) of this reachability problem is established by backtracking as in standard dynamic programming.
Reconfigurable Timed Discrete-Event Systems
Authors: Matin Macktoobian
Date: 2022-10-04 00:00:00

In this paper, we present the first general solution to the automatic reconfiguration problem of timed discrete-event systems. We extend the recursive forcible backtracking approach which had been already solved the automatic reconfiguration problem of untimed discrete-event systems. In particular, we first solve the timed centralized reconfiguration problem using a specific timed eligibility set. Then, we study the identity between the solutions to an arbitrary timed centralized reconfiguration problem and its corresponding decentralized version. It turns out that the solutions to both cases are identical to each other. So, the solution obtained by the proposed theory is interestingly invariant to systematic distributions.
A Fixed-Point Algorithm for the AC Power Flow Problem
Authors: Liangjie Chen, John W. Simpson-Porco
Date: 2022-10-04 00:00:00

This paper presents an algorithm that solves the AC power flow problem for balanced, three-phase transmission systems at steady state. The algorithm extends the "fixed-point power flow" algorithm in the literature to include transmission losses, phase-shifting transformers, and a distributed slack bus model. The algorithm is derived by vectorizing the component-wise AC power flow equations and manipulating them into a novel equivalent fixed-point form. Preliminary theoretical results guaranteeing convergence are reported for the case of a two-bus power system. We validate the algorithm through extensive simulations on test systems of various sizes under different loading levels, and compare its convergence behavior against those of classic power flow algorithms.
Split-Spectrum Based Distributed State Estimation for Linear Systems
Authors: Lili Wang, Ji Liu, Brian B. O. Anderson, A. Stephen Morse
Date: 2022-10-03 00:00:00
Tasks: State Estimation

This paper studies a distributed state estimation problem for both continuous- and discrete-time linear systems. A simply structured distributed estimator (comprising interconnected local estimators) is first described for estimating the state of a continuous and multi-channel linear system whose sensed outputs are distributed across a fixed multi-agent network. The estimator is then extended to non-stationary networks whose graphs switch according to a switching signal. The estimator is guaranteed to solve the problem, provided a network-widely shared high gain condition achieving a form of spectrum separation is satisfied. As an alternative to sharing a common gain across the network, a fully distributed version of the estimator is also studied in which each agent adaptively adjusts a local gain, though the practicality of this approach is subject to a robustness issue common to adaptive control. A discrete-time version of the distributed state estimation problem is also studied, and a corresponding estimator based again on spectrum separation, but not high gain, is proposed for time-varying networks. For each scenario, it is explained how to construct the estimator so that the state estimation errors in the local estimators all converge to zero exponentially fast at a fixed but arbitrarily chosen rate, provided the network's graph is strongly connected for all time. The proposed estimators are inherently resilient to abrupt changes in the number of agents and communication links in the inter-agent communication graph upon which the algorithms depend, provided the network is redundantly strongly connected and redundantly jointly observable.
Neural-network solutions to stochastic reaction networks
Authors: Ying Tang, Jiayu Weng, Pan Zhang
Date: 2022-09-29 00:00:00

The stochastic reaction network in which chemical species evolve through a set of reactions is widely used to model stochastic processes in physics, chemistry and biology. To characterize the evolving joint probability distribution in the state space of species counts requires solving a system of ordinary differential equations, the chemical master equation, where the size of the counting state space increases exponentially with the type of species, making it challenging to investigate the stochastic reaction network. Here, we propose a machine-learning approach using the variational autoregressive network to solve the chemical master equation. Training the autoregressive network employs the policy gradient algorithm in the reinforcement learning framework, which does not require any data simulated in prior by another method. Different from simulating single trajectories, the approach tracks the time evolution of the joint probability distribution, and supports direct sampling of configurations and computing their normalized joint probabilities. We apply the approach to representative examples in physics and biology, and demonstrate that it accurately generates the probability distribution over time. The variational autoregressive network exhibits a plasticity in representing the multimodal distribution, cooperates with the conservation law, enables time-dependent reaction rates, and is efficient for high-dimensional reaction networks with allowing a flexible upper count limit. The results suggest a general approach to investigate stochastic reaction networks based on modern machine learning.
Predicting CO$_2$ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks
Authors: Yue Jian, Yuyang Wang, Amir Barati Farimani
Date: 2022-09-29 00:00:00

Ionic Liquids (ILs) provide a promising solution for CO$_2$ capture and storage to mitigate global warming. However, identifying and designing the high-capacity IL from the giant chemical space requires expensive, and exhaustive simulations and experiments. Machine learning (ML) can accelerate the process of searching for desirable ionic molecules through accurate and efficient property predictions in a data-driven manner. But existing descriptors and ML models for the ionic molecule suffer from the inefficient adaptation of molecular graph structure. Besides, few works have investigated the explainability of ML models to help understand the learned features that can guide the design of efficient ionic molecules. In this work, we develop both fingerprint-based ML models and Graph Neural Networks (GNNs) to predict the CO$_2$ absorption in ILs. Fingerprint works on graph structure at the feature extraction stage, while GNNs directly handle molecule structure in both the feature extraction and model prediction stage. We show that our method outperforms previous ML models by reaching a high accuracy (MAE of 0.0137, $R^2$ of 0.9884). Furthermore, we take the advantage of GNNs feature representation and develop a substructure-based explanation method that provides insight into how each chemical fragments within IL molecules contribute to the CO$_2$ absorption prediction of ML models. We also show that our explanation result agrees with some ground truth from the theoretical reaction mechanism of CO$_2$ absorption in ILs, which can advise on the design of novel and efficient functional ILs in the future.
What is the Price of a Skill? The Value of Complementarity
Authors: Fabian Stephany, Ole Teutloff
Date: 2022-10-04 00:00:00

The global workforce is urged to constantly reskill, as technological change favours particular new skills while making others redundant. But which skills are a good investment for workers and firms? As skills are seldomly applied in isolation, we propose that complementarity strongly determines a skill's economic value. For 962 skills, we demonstrate that their value is strongly determined by complementarity - that is, how many different skills, ideally of high value, a competency can be combined with. We show that the value of a skill is relative, as it depends on the skill background of the worker. For most skills, their value is highest when used in combination with skills of a different type. We put our model to the test with a set of skills related to Artificial Intelligence (AI). We find that AI skills are particularly valuable - increasing worker wages by 21% on average - because of their strong complementarities and their rising demand in recent years. The model and metrics of our work can inform the policy and practice of digital re-skilling to reduce labour market mismatches. In cooperation with data and education providers, researchers and policy makers should consider using this blueprint to provide learners with personalised skill recommendations that complement their existing capacities and fit their occupational background.
Collaborative knowledge exchange promotes innovation
Authors: Tomoya Mori, Jonathan Newton, Shosei Sakaguchi
Date: 2022-10-04 00:00:00

Considering collaborative patent development, we provide micro-level evidence for innovation through exchanges of differentiated knowledge. Knowledge embodied in a patent is proxied by word pairs appearing in its abstract, while novelty is measured by the frequency with which these word pairs have appeared in past patents. Inventors are assumed to possess the knowledge associated with patents in which they have previously participated. We find that collaboration by inventors with more mutually differentiated knowledge sets is likely to result in patents with higher novelty.
Wetland Quality as a Determinant of Economic Value of Ecosystem Services: an Exploration
Authors: Hongyan Chen, Pushpam Kumar, Tom Barker
Date: 2022-10-03 00:00:00
Tasks: Decision Making, regression, valid

Wetland quality is a critical factor in determining values of wetland goods and services. However, in many studies on wetland valuation, wetland quality has been ignored. While those studies might give useful information to the local people for decision-making, their lack of wetland quality information may lead to the difficulty in integrating wetland quality into a cross-studies research like meta-analysis. In a meta-analysis, a statistical regression function needs withdrawing from those individual studies for analysis and prediction. This research introduces the wetland quality factor, a critical but frequently missed factor, into a meta-analysis and simultaneously considers other influential factors, such as study method, socio-economic state and other wetland site characteristics, as well. Thus, a more accurate and valid meta-regression function is expected. Due to no obvious quality information in primary studies, we extract two kinds of wetland states from the study context as relative but globally consistent quality measurement to use in the analysis, as the first step to explore the effect of wetland quality on values of ecosystem services.
The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022
Authors: Weiqing Wang, Xiaoyi Qin, Ming Cheng, Yucong Zhang, Kangyue Wang, Ming Li
Date: 2022-10-04 00:00:00
Tasks: Action Detection, Activity Detection, Clustering, Speaker Recognition

This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our system contains a fused voice activity detection model, a clustering-based diarization model, and a target-speaker voice activity detection-based overlap detection model. Overall, the submitted system is similar to our previous year's system in VoxSRC-21. The difference is that we use a much better speaker embedding and a fused voice activity detection, which significantly improves the performance. Finally, we fuse 4 different systems using DOVER-lap and achieve 4.75 of the diarization error rate, which ranks the 1st place in track 4.
An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification
Authors: Junyi Peng, Oldrich Plchot, Themos Stafylakis, Ladislav Mosner, Lukas Burget, Jan Cernocky
Date: 2022-10-03 00:00:00
Tasks: Self-Supervised Learning, Speaker Verification

In recent years, self-supervised learning paradigm has received extensive attention due to its great success in various down-stream tasks. However, the fine-tuning strategies for adapting those pre-trained models to speaker verification task have yet to be fully explored. In this paper, we analyze several feature extraction approaches built on top of a pre-trained model, as well as regularization and learning rate schedule to stabilize the fine-tuning process and further boost performance: multi-head factorized attentive pooling is proposed to factorize the comparison of speaker representations into multiple phonetic clusters. We regularize towards the parameters of the pre-trained model and we set different learning rates for each layer of the pre-trained model during fine-tuning. The experimental results show our method can significantly shorten the training time to 4 hours and achieve SOTA performance: 0.59%, 0.79% and 1.77% EER on Vox1-O, Vox1-E and Vox1-H, respectively.
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDU
Authors: Charles Millard, Mark Chiew
Date: 2022-10-04 00:00:00
Tasks: Denoising, MRI Reconstruction, Self-Supervised Learning

Most existing methods for Magnetic Resonance Imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a high signal-to-noise ratio (SNR), fully sampled dataset is available for training. In many circumstances, however, such a dataset is highly impractical or even technically infeasible to acquire. Recently, a number of self-supervised methods for MR reconstruction have been proposed, which use sub-sampled data only. However, the majority of such methods, such as Self-Supervised Learning via Data Undersampling (SSDU), are susceptible to reconstruction errors arising from noise in the measured data. In response, we propose Robust SSDU, which provably recovers clean images from noisy, sub-sampled training data by simultaneously estimating missing k-space samples and denoising the available samples. Robust SSDU trains the reconstruction network to map from a further noisy and sub-sampled version of the data to the original, singly noisy and sub-sampled data, and applies an additive Noisier2Noise correction term at inference. We also present a related method, Noiser2Full, that recovers clean images when noisy, fully sampled data is available for training. Both proposed methods are applicable to any network architecture, straight-forward to implement and have similar computational cost to standard training. We evaluate our methods on the multi-coil fastMRI brain dataset with a novel denoising-specific architecture and find that it performs competitively with a benchmark trained on clean, fully sampled data.
An Anatomy-aware Framework for Automatic Segmentation of Parotid Tumor from Multimodal MRI
Authors: Yifan Gao, Yin Dai, Fayu Liu, Weibing Chen, Lifu Shi
Date: 2022-10-04 00:00:00
Tasks: Anatomy, Segmentation, Tumor Segmentation

Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the parotid tumor, where accurate segmentation of tumors is highly desired for determining appropriate treatment plans and avoiding unnecessary surgery. However, the task remains nontrivial and challenging due to ambiguous boundaries and various sizes of the tumor, as well as the presence of a large number of anatomical structures around the parotid gland that are similar to the tumor. To overcome these problems, we propose a novel anatomy-aware framework for automatic segmentation of parotid tumors from multimodal MRI. First, a Transformer-based multimodal fusion network PT-Net is proposed in this paper. The encoder of PT-Net extracts and fuses contextual information from three modalities of MRI from coarse to fine, to obtain cross-modality and multi-scale tumor information. The decoder stacks the feature maps of different modalities and calibrates the multimodal information using the channel attention mechanism. Second, considering that the segmentation model is prone to be disturbed by similar anatomical structures and make wrong predictions, we design anatomy-aware loss. By calculating the distance between the activation regions of the prediction segmentation and the ground truth, our loss function forces the model to distinguish similar anatomical structures with the tumor and make correct predictions. Extensive experiments with MRI scans of the parotid tumor showed that our PT-Net achieved higher segmentation accuracy than existing networks. The anatomy-aware loss outperformed state-of-the-art loss functions for parotid tumor segmentation. Our framework can potentially improve the quality of preoperative diagnosis and surgery planning of parotid tumors.
Semantic Communication with Conceptual Spaces
Authors: Dylan Wheeler, Erin E. Tripp, Balasubramaniam Natarajan
Date: 2022-10-04 00:00:00
Tasks: Semantic Communication

Despite the fact that Shannon and Weaver's Mathematical Theory of Communication was published over 70 years ago, all communication systems continue to operate at the first of three levels defined in this theory: the technical level. In this letter, we argue that a transition to the semantic level embodies a natural, important step in the evolution of communication technologies. Furthermore, we propose a novel approach to engineering semantic communication using conceptual spaces and functional compression. We introduce a model of semantic communication utilizing this approach, and simulate communication of image semantics demonstrating a 99.79% reduction in rate.
Energy-Efficient Cell-Free Network Assisted by Hybrid RISs
Authors: Wanting Lyu, Yue Xiu, Songjie Yang, Chau Yuen, Zhongpei Zhang
Date: 2022-10-04 00:00:00

In this letter, we investigate a cell-free network aided by hybrid reconfigurable intelligent surfaces (RISs), which consists of a mixture of passive and active elements that are capable of amplifying and reflecting the incident signal. To maximize the energy efficiency (EE) of the system, we formulate a joint transmit beamforming and RIS coefficients optimization problem. To deal with the fractional objective function, Dinkelbach transform, Lagrangian dual reformulation, and quadratic transform are utilized, with a block coordinate descent (BCD) based algorithm proposed to decouple the variables. In addition, successive convex approximation (SCA) method is applied to iteratively to tackle the non-convexity of the sub-problems. Simulation results illustrate the effectiveness and convergence of the proposed algorithm through analyzing the EE and sum rate performance with varying parameter settings. The proposed hybrid RISs schemes can achieve 92% of the sum rate but 188% of EE of active RISs schemes. As compared with passive RISs, 11% gain in sum rate can be achieved with comparable EE.
Beamforming Design and Trajectory Optimization for UAV-Empowered Adaptable Integrated Sensing and Communication
Authors: Cailian Deng, Xuming Fang, Xianbin Wang
Date: 2022-10-04 00:00:00
Tasks: Integrated sensing and communication, ISAC

Unmanned aerial vehicle (UAV) has high flexibility and controllable mobility, therefore it is considered as a promising enabler for future integrated sensing and communication (ISAC). In this paper, we propose a novel adaptable ISAC (AISAC) mechanism in the UAV-enabled system, where the UAV performs sensing on demand during communication and the sensing duration is configured flexibly according to the application requirements rather than keeping the same with the communication duration. Our designed mechanism avoids the excessive sensing and waste of radio resources, therefore improving the resource utilization and system performance. In the UAV-enabled AISAC system, we aim at maximizing the average system throughput by optimizing the communication and sensing beamforming as well as UAV trajectory while guaranteeing the quality-of-service requirements of communication and sensing. To efficiently solve the considered non-convex optimization problem, we first propose an efficient alternating optimization algorithm to optimize the communication and sensing beamforming for a given UAV location, and then develop a low-complexity joint beamforming and UAV trajectory optimization algorithm that sequentially searches the optimal UAV location until reaching the final location. Numerical results validate the superiority of the proposed adaptable mechanism and the effectiveness of the designed algorithm.
Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications
Authors: Xi Zheng, Jun Fang, Hongwei Wang, Peilan Wang, Hongbin Li
Date: 2022-10-04 00:00:00
Tasks: compressed sensing

We consider the problem of channel estimation and joint active and passive beamforming for reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We show that, with a well-designed frame-based training protocol, the received pilot signal can be organized into a low-rank third-order tensor that admits a canonical polyadic decomposition (CPD). Based on this observation, we propose two CPD-based methods for estimating the cascade channels associated with different subcarriers. The proposed methods exploit the intrinsic low-rankness of the CPD formulation, which is a result of the sparse scattering characteristics of mmWave channels, and thus have the potential to achieve a significant training overhead reduction. Specifically, our analysis shows that the proposed methods have a sample complexity that scales quadratically with the sparsity of the cascade channel. Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels. Simulation results show that the proposed CPD-based channel estimation methods attain mean square errors that are close to the Cramer-Rao bound (CRB) and present a clear advantage over the compressed sensing-based method. In addition, the proposed joint beamforming method can effectively utilize the estimated channel parameters to achieve superior beamforming performance.