pwc-archive/papers-with-abstracts huggingface dataset:
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning
Authors: Jiadong Yu, Yang Li, Xiaolan Liu, Bo Sun, Yuan Wu, Danny H. K. Tsang
Date: 2022-09-20 00:00:00
Tasks: Edge-computing, Multi-agent Reinforcement Learning, Reinforcement Learning (RL)

By employing powerful edge servers for data processing, mobile edge computing (MEC) has been recognized as a promising technology to support emerging computation-intensive applications. Besides, non-orthogonal multiple access (NOMA)-aided MEC system can further enhance the spectral-efficiency with massive tasks offloading. However, with more dynamic devices brought online and the uncontrollable stochastic channel environment, it is even desirable to deploy appealing technique, i.e., intelligent reflecting surfaces (IRS), in the MEC system to flexibly tune the communication environment and improve the system energy efficiency. In this paper, we investigate the joint offloading, communication and computation resource allocation for IRS-assisted NOMA MEC system. We firstly formulate a mixed integer energy efficiency maximization problem with system queue stability constraint. We then propose the Lyapunov-function-based Mixed Integer Deep Deterministic Policy Gradient (LMIDDPG) algorithm which is based on the centralized reinforcement learning (RL) framework. To be specific, we design the mixed integer action space mapping which contains both continuous mapping and integer mapping. Moreover, the award function is defined as the upper-bound of the Lyapunov drift-plus-penalty function. To enable end devices (EDs) to choose actions independently at the execution stage, we further propose the Heterogeneous Multi-agent LMIDDPG (HMA-LMIDDPG) algorithm based on distributed RL framework with homogeneous EDs and heterogeneous base station (BS) as heterogeneous multi-agent. Numerical results show that our proposed algorithms can achieve superior energy efficiency performance to the benchmark algorithms while maintaining the queue stability. Specially, the distributed structure HMA-LMIDDPG can acquire more energy efficiency gain than centralized structure LMIDDPG.
Two-Stage Submodular Optimization of Dynamic Thermal Rating for Risk Mitigation Considering Placement and Operation Schedule
Authors: Qinfei Long, Junhong Liu, Chenhao Ren, Wenqian Yin, Feng Liu, Yunhe Hou
Date: 2022-09-20 00:00:00

Cascading failure causes a major risk to society currently. To effectively mitigate the risk, dynamic thermal rating (DTR) technique can be applied as a cost-effective strategy to exploit potential transmission capability. From the perspectives of service life and Braess paradox, it is important and challenging to jointly optimize the DTR placement and operation schedule for changing system state, which is a two-stage combinatorial problem with only discrete variables, suffering from no approximation guarantee and dimension curse only based on traditional models. Thus, the present work proposes a novel two-stage submodular optimization (TSSO) of DTR for risk mitigation considering placement and operation schedule. Specifically, it optimizes DTR placement with proper redundancy in first stage, and then determines the corresponding DTR operation for each system state in second stage. Under the condition of the Markov and submodular features in sub-function of risk mitigation, the submodularity of total objective function of TSSO can be proven for the first time. Based on this, a state-of-the-art efficient solving algorithm is developed that can provide a better approximation guarantee than previous studies by coordinating the separate curvature and error form. The performance of the proposed algorithms is verified by case results.
Superheterodyne Microwave System for the Detection of Bioparticles with Coplanar Electrodes on a Microfluidic Platform
Authors: Cesar Palacios, Marc Jofre, Lluis Jofre, Jordi Romeu, Luis Jofre-Roca
Date: 2022-09-20 00:00:00

The combination of microwave and microfluidic technologies has the potential to enable wireless monitoring and interaction with bioparticles, facilitating in this way the exploration of a still largely uncharted territory at the intersection of biology, communication engineering and microscale physics. Opportunely, the scientific and technical requirements of microfluidics and microwave techniques converge to the need of system miniaturization to achieve the required sensitivity levels. This work, therefore, presents the design and optimization of a measurement system for the detection of bioparticles over the frequency range $0.01$ to $10$ $\mathrm{GHz}$, with different coplanar electrodes configurations on a microfluidic platform. The design of the measurement signal-chain setup is optimized for a novel real-time superheterodyne microwave detection system. In particular, signal integrity is achieved by means of a microwave-shielded chamber, which is protected from external electromagnetic interference that may potentially impact the coplanar electrodes mounted on the microfluidic device. Additionally, analytical expressions and experimental validation of the system-level performance are provided and discussed for the different designs of the coplanar electrodes. This technique is applied to measure the electrical field perturbation produced by $10$ $\mathrm{\mu m}$ polystyrene beads with a concentration of $10^5$ $\mathrm{beads/mL}$, and flowing at a rate of $10$ $\mathrm{\mu L/m}$. The achieved SNR is in the order of $40$ $\mathrm{dB}$ for the three coplanar electrodes considered.
A Unified Analytical Method to Quantify Three Types of Fast Frequency Response from Inverter-based Resources
Authors: Shuan Dong, Xin Fang, Jin Tan, Ningchao Gao, Xiaofan Cui, Anderson Hoke
Date: 2022-09-20 00:00:00

With more inverter-based resources (IBRs), our power systems have lower frequency nadirs following N-1 contingencies, and undesired under-frequency load shedding (UFLS) can occur. To address this challenge, IBRs can be programmed to provide at least three types of fast frequency response (FFR), e.g., step response, proportional response (P/f droop response), and derivative response (synthetic inertia). However, these heterogeneous FFR challenge the study of power system frequency dynamics. Thus, this paper develops an analytical frequency nadir prediction method that allows for the consideration of all three potential forms of FFR provided by IBRs. The proposed method provides fast and accurate frequency nadir estimation after N-1 generation tripping contingencies. Our method is grounded on the closed-form solution for the frequency nadir, which is solved from the second-order system frequency response model considering the governor dynamics and three types of FFR. The simulation results in the IEEE 39-bus system with different types of FFR demonstrate that the proposed method provides an accurate and fast prediction of the frequency nadir under various disturbances.
Shepherding Control for Separating a Single Agent from a Swarm
Authors: Yaosheng Deng, Masaki Ogura, Aiyi Li, Naoki Wakamiya
Date: 2022-09-20 00:00:00

In this paper, we consider the swarm-control problem of spatially separating a specified target agent within the swarm from all the other agents, while maintaining the connectivity among the other agents. We specifically aim to achieve the separation by designing the movement algorithm of an external agent, called a shepherd, which exerts repulsive forces on the agents in the swarm. This problem has potential applications in the context of the manipulation of the swarm of micro- and nano-particles. We first formulate the separation problem, where the swarm agents (called sheep) are modeled by the Boid model. We then analytically study the special case of two-sheep swarms. By leveraging the analysis, we then propose a potential function-based movement algorithm of the shepherd to achieve separation while maintaining the connectivity within the remaining swarm. We demonstrate the effectiveness of the proposed algorithm with numerical simulations.
A Wireless-Assisted Hierarchical Framework to Accommodate Mobile Energy Resources
Authors: Pudong Ge, Cesare Caputo, Michel-Alexandre Cardin, Anna Korre, Fei Teng
Date: 2022-09-19 00:00:00

The societal decarbonisation fosters the installation of massive renewable inverter-based resources (IBRs) in replacing fossil fuel based traditional energy supply. The efficient and reliable operation of distributed IBRs requires advanced Information and Communication Technologies (ICT) , which may lead to a huge infrastructure investment and long construction time for remote communities. Therefore, to efficiently coordinate IBRs, we propose a low-cost hierarchical structure, especially for remote communities without existing strong ICT connections, that combines the advantages of centralised and distributed frameworks via advanced wireless communication technologies. More specifically, in each region covered by a single cellular network, dispatchable resources are controlled via a regional aggregated controller, and the corresponding regional information flow is enabled by a device-to-device (D2D) communication assisted wireless network. The wireless network can fully reuse the bandwidth to improve data flow efficiency, leading to a flexible information structure that can accommodate the plug-and-play operation of mobile IBRs. Simulation results demonstrate that the proposed wireless communication scheme significantly improves the utilization of existing bandwidth, and the dynamically allocated wireless system ensures the flexible operation of mobile IBRs.
Safety-Critical Controller Verification via Sim2Real Gap Quantification
Authors: Prithvi Akella, Wyatt Ubellacker, Aaron D. Ames
Date: 2022-09-19 00:00:00

The well-known quote from George Box states that: "All models are wrong, but some are useful." To develop more useful models, we quantify the inaccuracy with which a given model represents a system of interest, so that we may leverage this quantity to facilitate controller synthesis and verification. Specifically, we develop a procedure that identifies a sim2real gap that holds with a minimum probability. Augmenting the nominal model with our identified sim2real gap produces an uncertain model which we prove is an accurate representor of system behavior. We leverage this uncertain model to synthesize and verify a controller in simulation using a probabilistic verification approach. This pipeline produces controllers with an arbitrarily high probability of realizing desired safe behavior on system hardware without requiring hardware testing except for those required for sim2real gap identification. We also showcase our procedure working on two hardware platforms - the Robotarium and a quadruped.
Guarding a Non-Maneuverable Translating Line with an Attached Defender
Authors: Goutam Das, Michael Dorothy, Zachary I. Bell, Daigo Shishika
Date: 2022-09-19 00:00:00

In this paper we consider a target-guarding differential game where the defender must protect a linearly translating line-segment by intercepting an attacker who tries to reach it. In contrast to common target-guarding problems, we assume that the defender is attached to the target and moves along with it. This assumption affects the defenders' maximum speed in inertial frame, which depends on the target's direction of motion. Zero-sum differential game of degree for both the attacker-win and defender-win scenarios are studied, where the payoff is defined to be the distance between the two agents at the time of game termination. We derive the equilibrium strategies and the Value function by leveraging the solution for the infinite-length target scenario. The zero-level set of this Value function provides the barrier surface that divides the state space into defender-win and attacker-win regions. We present simulation results to demonstrate the theoretical results.
Thermodynamic force thresholds biomolecular behavior
Authors: Milo M. Lin
Date: 2022-09-19 00:00:00

In living systems, collective molecular behavior is driven by thermodynamic forces in the form of chemical gradients. Leveraging recent advances in the field of nonequilibrium physics, I show that increasing the thermodynamic force alone can induce qualitatively new behavior. To demonstrate this principle, general equations governing kinetic proofreading and microtubule assembly are derived. These equations show that new capabilities, including catalytic regulation of steady-state behavior and exponential enhancement of molecular discrimination, are only possible if the system is driven sufficiently far from equilibrium, and can emerge sharply at a threshold force. Regardless of design parameters, these results reveal that the thermodynamic force sets fundamental performance limits on tuning sensitivity, error, and waste. Experimental data show that these biomolecular processes operate at the limits allowed by theory.
Non-local interaction effects in models of interacting populations
Authors: Mario I. Simoy, Marcelo N. Kuperman
Date: 2022-09-20 00:00:00

We consider a couple of models for the dynamics of the populations of two interacting species, inspired by Lotka-Volterra's classical equations. The novelty of this work is that the interaction terms are non local and the interaction occurs within a bounded range. These terms include the competitive intraspecific interaction among individuals and the interspecific terms for which we consider two cases: Competition and predation. The results show that not only the non-locality induces spatial structures but also allows for the survival of the species when due to predation or the competitive exclusion extinction was expected, and even promotes spatio-temporal patterns not linked to eventual temporal oscillations in the local case. In this work we also explore some interesting details about the behavior of the population dynamics that shows spatial patterns that interfere in a way that leads to non-trivial results.
Weak Identification with Bounds in a Class of Minimum Distance Models
Authors: Gregory Cox
Date: 2020-12-21 00:00:00

When parameters are weakly identified, bounds on the parameters may provide a valuable source of information. Existing weak identification estimation and inference results are unable to combine weak identification with bounds. Within a class of minimum distance models, this paper proposes identification-robust inference that incorporates information from bounds when parameters are weakly identified. The inference is based on limit theory that combines weak identification theory with parameter-on-the-boundary theory. This paper demonstrates the role of the bounds and identification-robust inference in two example factor models. This paper also demonstrates the identification-robust inference in an empirical application, a factor model for parental investments in children.
Network analysis and Eurozone trade imbalances
Authors: Giovanni Carnazza, Pierluigi Vellucci
Date: 2022-09-20 00:00:00

European Monetary Union continues to be characterised by significant macroeconomic imbalances. Germany has shown increasing current account surpluses at the expense of the other member states (especially the European periphery). Since the creation of a single currency has implied the impossibility of implementing competitive devaluations, trade imbalances within a monetary union can be considered unfair behaviour. We have modelled Eurozone trade flows in goods through a weighted network from 1995 to 2019. To the best of our knowledge, this is the first work that applies this methodology to this kind of data. Network analysis has allowed us to estimate a series of important centrality measures. A polarisation phenomenon emerges in relation to the growth of German dominance. The common currency has then not been capable to remove trade asymmetry, increasing the distance between surplus and deficit countries. This situation should be addressed with expansionary policies on the demand side at national and supranational level.
Lexicographic Composition of Choice Functions
Authors: Sean Horan, Vikram Manjunath
Date: 2022-09-19 00:00:00

Lexicographic composition is a natural way to build an aggregate choice function from component choice functions. As the name suggests, the components are ordered and choose sequentially. The sets that subsequent components select from are constrained by the choices made by earlier choice functions. The specific constraints affect whether properties like path independence are preserved. For several domains of inputs, we characterize the constraints that ensure such preservation.
ESPnet-ONNX: Bridging a Gap Between Research and Production
Authors: Masao Someki, Yosuke Higuchi, Tomoki Hayashi, Shinji Watanabe
Date: 2022-09-20 00:00:00
Tasks: Spoken Language Understanding

In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks. In contrast, application developers are interested in making models suitable for actual products, which involves optimizing a model for faster inference and adapting a model to various platforms (e.g., C++ and Python). In this work, to fill the gap between the two, we establish an effective procedure for optimizing a PyTorch-based research-oriented model for deployment, taking ESPnet, a widely used toolkit for speech processing, as an instance. We introduce different techniques to ESPnet, including converting a model into an ONNX format, fusing nodes in a graph, and quantizing parameters, which lead to approximately 1.3-2$\times$ speedup in various tasks (i.e., ASR, TTS, speech translation, and spoken language understanding) while keeping its performance without any additional training. Our ESPnet-ONNX will be publicly available at https://github.com/espnet/espnet_onnx
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model
Authors: Arka Saha, Sourya Sengupta
Date: 2022-09-20 00:00:00
Tasks: Denoising, Image Denoising

Optical Coherence Tomography (OCT) image denoising is a fundamental problem as OCT images suffer from multiplicative speckle noise, resulting in poor visibility of retinal layers. The traditional denoising methods consider specific statistical properties of the noise, which are not always known. Furthermore, recent deep learning-based denoising methods require paired noisy and clean images, which are often difficult to obtain, especially medical images. Noise2Noise family architectures are generally proposed to overcome this issue by learning without noisy-clean image pairs. However, for that, multiple noisy observations from a single image are typically needed. Also, sometimes the experiments are demonstrated by simulating noises on clean synthetic images, which is not a realistic scenario. This work shows how a single real-world noisy observation of each image can be used to train a denoising network. Along with a theoretical understanding, our algorithm is experimentally validated using a publicly available OCT image dataset. Our approach incorporates Anscombe transform to convert the multiplicative noise model to additive Gaussian noise to make it suitable for OCT images. The quantitative results show that this method can outperform several other methods where a single noisy observation of an image is needed for denoising. The code and implementation of this paper will be available publicly upon acceptance of this paper.
Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks
Authors: Marina Fernandez Garcia, Rodrigo Gonzalez Laiz, Hui Ji, Kelly Payette, Andras Jakab
Date: 2022-09-20 00:00:00
Tasks: SSIM, Super-Resolution

Fetal brain magnetic resonance imaging serves as an emerging modality for prenatal counseling and diagnosis in disorders affecting the brain. Machine learning based segmentation plays an important role in the quantification of brain development. However, a limiting factor is the lack of sufficiently large, labeled training data. Our study explored the application of SPADE, a conditional general adversarial network (cGAN), which learns the mapping from the label to the image space. The input to the network was super-resolution T2-weighted cerebral MRI data of 120 fetuses (gestational age range: 20-35 weeks, normal and pathological), which were annotated for 7 different tissue categories. SPADE networks were trained on 256*256 2D slices of the reconstructed volumes (image and label pairs) in each orthogonal orientation. To combine the generated volumes from each orientation into one image, a simple mean of the outputs of the three networks was taken. Based on the label maps only, we synthesized highly realistic images. However, some finer details, like small vessels were not synthesized. A structural similarity index (SSIM) of 0.972+-0.016 and correlation coefficient of 0.974+-0.008 were achieved. To demonstrate the capacity of the cGAN to create new anatomical variants, we artificially dilated the ventricles in the segmentation map and created synthetic MRI of different degrees of fetal hydrocephalus. cGANs, such as the SPADE algorithm, allow the generation of hypothetically unseen scenarios and anatomical configurations in the label space, which data in turn can be utilized for training various machine learning algorithms. In the future, this algorithm would be used for generating large, synthetic datasets representing fetal brain development. These datasets would potentially improve the performance of currently available segmentation networks.
Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization
Authors: Nicholas Dwork, Ethan M. I. Johnson, Daniel O'Connor, Jeremy W. Gordon, Adam B. Kerr, Corey A. Baron, John M. Pauly, Peder E. Z. Larson
Date: 2020-07-01 00:00:00
Tasks: compressed sensing

With the advent of multi-coil imaging and compressed sensing, a number of model based reconstruction algorithms have been created. They incorporate a multitude of different regularization functions based on physics, observed phenomenology, and heuristics. Moreover, several iterative methods exist that attempt to simultaneously estimate the sensitivity maps and the image. In this manuscript, we present a generalization of several existing iterative model based algorithms. We devise a calibrationless instance of this generalization that only incorporates regularization terms based on physics and the accepted compressed sensing phenomenology of sparsity in the wavelet domain. We compare the results of the new amalgamated optimization problem with existing methods on both simulated and real datasets. We show that the images reconstructed using the new method, entitled Multi-coil Compressed Sensing (MCCS), are of higher quality than existing methods in all cases studied.
Mirror Prox Algorithm for Large-Scale Cell-Free Massive MIMO Uplink Power Control
Authors: Muhammad Farooq, Hien Quoc Ngo, Le-Nam Tran
Date: 2022-09-20 00:00:00
Tasks: Fairness, Second-order methods, Unity

We consider the problem of max-min fairness for uplink cell-free massive multiple-input multiple-output (MIMO) subject to per-user power constraints. The standard framework for solving the considered problem is to separately solve two subproblems: the receiver filter coefficient design and the power control problem. While the former has a closed-form solution, the latter has been solved using either second-order methods of high computational complexity or a first-order method that provides an approximate solution. To deal with these drawbacks of the existing methods, we propose a mirror prox based method for the power control problem by equivalently reformulating it as a convex-concave problem and applying the mirror prox algorithm to find a saddle point. The simulation results establish the optimality of the proposed solution and demonstrate that it is more efficient than the known methods. We also conclude that for large-scale cell-free massive MIMO, joint optimization of linear receive combining and power control provides significantly better user fairness than the power control only scheme in which receiver coefficients are fixed to unity.