pwc-archive/papers-with-abstracts huggingface dataset:
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition
Authors: Zi-Qiang Zhang, Jie Zhang, Jian-Shu Zhang, Ming-Hui Wu, Xin Fang, Li-Rong Dai
Date: 2022-02-15 00:00:00
Tasks: Audio-Visual Speech Recognition, Lipreading, Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition, Visual Speech Recognition

With the advance in self-supervised learning for audio and visual modalities, it has become possible to learn a robust audio-visual speech representation. This would be beneficial for improving the audio-visual speech recognition (AVSR) performance, as the multi-modal inputs contain more fruitful information in principle. In this paper, based on existing self-supervised representation learning methods for audio modality, we therefore propose an audio-visual representation learning approach. The proposed approach explores both the complementarity of audio-visual modalities and long-term context dependency using a transformer-based fusion module and a flexible masking strategy. After pre-training, the model is able to extract fused representations required by AVSR. Without loss of generality, it can be applied to single-modal tasks, e.g. audio/visual speech recognition by simply masking out one modality in the fusion module. The proposed pre-trained model is evaluated on speech recognition and lipreading tasks using one or two modalities, where the superiority is revealed.
On the design of scalable networks rejecting first order disturbances
Authors: Shihao Xie, Giovanni Russo
Date: 2022-02-15 00:00:00

This paper is concerned with the problem of designing distributed control protocols for network systems affected by delays and disturbances consisting of a first-order polynomial component and a residual signal. Specifically, we propose the use of a multiplex architecture to design distributed control protocols to reject polynomial disturbances up to ramps and guarantee a scalability property that prohibits the amplification of residual disturbances. For this architecture, we give a sufficient condition on the control protocols to guarantee scalability and ramps rejection. The effectiveness of the result, which can be used to study networks of nonlinearly coupled nonlinear agents, is illustrated via a robot formation control problem.
Autonomous Vehicle-to-Grid Design for Provision of Frequency Control Ancillary Service and Distribution Voltage Regulation
Authors: Shota Yumiki, Yoshihiko Susuki, Yuta Oshikubo, Yutaka Ota, Ryo Masegi, Akihiko Kawashima, Atsushi Ishigame, Shinkichi Inagaki, Tatsuya Suzuki
Date: 2021-01-26 00:00:00
Tasks: energy management, Management

We develop a system-level design for the provision of Ancillary Service (AS) for control of electric power grids by in-vehicle batteries, suitably applied to Electric Vehicles (EVs) operated in a sharing service. An architecture for cooperation between transportation and energy management systems is introduced that enables us to design an autonomous Vehicle-to-Grid (V2G) for the provision of multi-objective AS: primary frequency control in a transmission grid and voltage amplitude regulation in a distribution grid connected to EVs. The design is based on the ordinary differential equation model of distribution voltage, which has been recently introduced as a new physics-based model, and is utilized in this paper for assessing and regulating the impact of spatiotemporal charging/charging of a large population of EVs to a distribution grid. Effectiveness of the autonomous V2G design is evaluated with numerical simulations of realistic models for transmission and distribution grids with synthetic operation data on EVs in a sharing service. In addition, we present a hardware-in-the-loop test for evaluating its feasibility in a situation where inevitable latency is involved due to power, control, and communication equipments.
Time Domain Simulation of DFIG-Based Wind Power System using Differential Transform Method
Authors: Pradeep Singh, Upasana Buragohain, Nilanjan Senroy
Date: 2022-02-15 00:00:00

This paper proposes a new non-iterative time-domain simulation approach using Differential Transform Method (DTM) to solve the set of non-linear Differential-Algebraic Equations (DAEs) involved in a DFIG-based wind power system. The DTM is an analytical as well as numerical approach applied to solve high dimensional non-linear dynamical systems and the solution can be expressed in the form of a series. In this approach, there is no need to compute higher-order derivatives as DAEs are converted into a set of linear equations after applying transformation rules so that the power series coefficients can be computed directly. The transformation rules are used to transform power system models of various devices, such as induction generator, wind turbine, rotor and grid side converter, which includes trigonometric, square root, exponential functions etc. Further, to increase the interval of convergence for the series solutions, the multi-step DTM (MsDTM) approach is used. The numerical performance of the proposed approach is compared with the traditional numerical RK-4 method to demonstrate the potential of the proposed approach in solving power system non-linear DAEs
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
Authors: Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone
Date: 2022-02-15 00:00:00
Tasks: Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs. By centrally controlling a fleet of self-driving vehicles, these systems provide mobility service to customers and are currently starting to be deployed in a number of cities around the world. Current learning-based approaches for controlling AMoD systems are limited to the single-city scenario, whereby the service operator is allowed to take an unlimited amount of operational decisions within the same transportation system. However, real-world system operators can hardly afford to fully re-train AMoD controllers for every city they operate in, as this could result in a high number of poor-quality decisions during training, making the single-city strategy a potentially impractical solution. To address these limitations, we propose to formalize the multi-city AMoD problem through the lens of meta-reinforcement learning (meta-RL) and devise an actor-critic algorithm based on recurrent graph neural networks. In our approach, AMoD controllers are explicitly trained such that a small amount of experience within a new city will produce good system performance. Empirically, we show how control policies learned through meta-RL are able to achieve near-optimal performance on unseen cities by learning rapidly adaptable policies, thus making them more robust not only to novel environments, but also to distribution shifts common in real-world operations, such as special events, unexpected congestion, and dynamic pricing schemes.
Graph Neural Network-Based Scheduling for Multi-UAV-Enabled Communications in D2D Networks
Authors: Pei Li, Lingyi Wang, Wei Wu, Fuhui Zhou, Baoyun Wang, Qihui Wu
Date: 2022-02-15 00:00:00
Tasks: Graph Neural Network, Scheduling

In this paper, we jointly design the power control and position dispatch for Multi-unmanned aerial vehicle (UAV)-enabled communication in device-to-device (D2D) networks. Our objective is to maximize the total transmission rate of downlink users (DUs). Meanwhile, the quality of service (QoS) of all D2D users must be satisfied. We comprehensively considered the interference among D2D communications and downlink transmissions. The original problem is strongly non-convex, which requires high computational complexity for traditional optimization methods. And to make matters worse, the results are not necessarily globally optimal. In this paper, we propose a novel graph neural networks (GNN) based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner. Particularly, we first construct a GNN-based model for the proposed network, in which the transmission links and interference links are formulated as vertexes and edges, respectively. Then, by taking the channel state information and the coordinates of ground users as the inputs, as well as the location of UAVs and the transmission power of all transmitters as outputs, we obtain the mapping from inputs to outputs through training the parameters of GNN. Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples. Moreover, it also shows that the performance of proposed GNN-based method is better than that of traditional means.
Dynamic optical contrast imaging for real-time delineation of tumor resection margins using head and neck cancer as a model
Authors: Yong Hu, Shan Huang, Albert Y. Han, Seong Moon, Jeffrey F. Krane, Oscar Stafsudd, Warren Grundfest, Maie A. St. John
Date: 2022-02-15 00:00:00
Tasks: Specificity

Complete surgical resection of the tumor for Head and neck squamous cell carcinoma (HNSCC) remains challenging, given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures. To address the clinical challenges, we introduce a wide-field, label-free imaging tool that can assist surgeons delineate tumor margins real-time. We assume that autofluorescence lifetime is a natural indicator of the health level of tissues, and ratio-metric measurement of the emission-decay state to the emission-peak state of excited fluorophores will enable rapid lifetime mapping of tissues. Here, we describe the principle, instrumentation, characterization of the imager and the intraoperative imaging of resected tissues from 13 patients undergoing head and neck cancer resection. 20 x 20 mm2 imaging takes 2 second/frame with a working distance of 50 mm, and characterization shows that the spatial resolution reached 70 {\mu}m and the least distinguishable fluorescence lifetime difference is 0.14 ns. Tissue imaging and Hematoxylin-Eosin stain slides comparison reveals its capability of delineating cancerous boundaries with submillimeter accuracy and a sensitivity of 91.86% and specificity of 84.38%.
Features of Linear Models that May Compromise Model-Based, Plant-Wide Control Techniques. The Case of the Tennessee Eastman Plant
Authors: Sergio F. Yapur
Date: 2022-02-14 00:00:00

This work examines a set of features that impact the reliability of linear models within the context of plant-wide control design (PWC). The study case is the Tennessee-Eastman (TE) plant. This benchmark problem is well-known for challenging many control design approaches. Analyses involve eigenvalues, average errors between simulations, condition numbers, and loss of rank across frequencies. These studies offer guidance for designing an effective plant-wide control system based on linear models.
The Economics of Orbit Use: Open Access, External Costs, and Runaway Debris Growth
Authors: Akhil Rao, Giacomo Rondina
Date: 2022-02-12 00:00:00
Tasks: Playing the Game of 2048

We present a dynamic physico-economic model of Earth orbit use with endogenous satellite collision risk to study conditions under which debris-producing collisions between orbiting bodies result in debris growth that may render Earth's orbits unusable, an outcome known as Kessler Syndrome. We characterize the dynamics of objects in orbit under open access as well as when external costs -- the impact of an additional satellite launch on the collision risk faced by all satellites -- are internalized, and we show that Kessler Syndrome can emerge in both cases. Finally, we show that once the economic incentives of satellite launching are modeled, for Kessler Syndrome to emerge, autocatalytic debris growth is essential. In our main calibration, Kessler Syndrome can emerge anytime between the year 2040 and the year 2184, with the precise date being very sensitive to the calibration of autocatalytic debris growth parameters.
Limits on the Evolutionary Rates of Biological Traits
Authors: Luis Pedro García-Pintos
Date: 2022-02-15 00:00:00

This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These \emph{rate limits} link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait's average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher's fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-off relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species' weights to the lengths of DNA strands.
An Equilibrium Model of the First-Price Auction with Strategic Uncertainty: Theory and Empirics
Authors: Bernhard Kasberger
Date: 2022-02-15 00:00:00

In many first-price auctions, bidders face considerable strategic uncertainty: They cannot perfectly anticipate the other bidders' bidding behavior. We propose a model in which bidders do not know the entire distribution of opponent bids but only the expected (winning) bid and lower and upper bounds on the opponent bids. We characterize the optimal bidding strategies and prove the existence of equilibrium beliefs. Finally, we apply the model to estimate the cost distribution in highway procurement auctions and find good performance out-of-sample.
Asymptotics of Cointegration Tests for High-Dimensional VAR($k$)
Authors: Anna Bykhovskaya, Vadim Gorin
Date: 2022-02-15 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The paper studies nonstationary high-dimensional vector autoregressions of order $k$, VAR($k$). Additional deterministic terms such as trend or seasonality are allowed. The number of time periods, $T$, and the number of coordinates, $N$, are assumed to be large and of the same order. Under this regime the first-order asymptotics of the Johansen likelihood ratio (LR), Pillai-Bartlett, and Hotelling-Lawley tests for cointegration are derived: the test statistics converge to nonrandom integrals. For more refined analysis, the paper proposes and analyzes a modification of the Johansen test. The new test for the absence of cointegration converges to the partial sum of the Airy$_1$ point process. Supporting Monte Carlo simulations indicate that the same behavior persists universally in many situations beyond those considered in our theorems. The paper presents empirical implementations of the approach for the analysis of S$\&$P$100$ stocks and of cryptocurrencies. The latter example has a strong presence of multiple cointegrating relationships, while the results for the former are consistent with the null of no cointegration.
Sequential Monte Carlo With Model Tempering
Authors: Marko Mlikota, Frank Schorfheide
Date: 2022-02-14 00:00:00
Tasks: model, Time Series, Time Series Analysis

Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and a nonlinear dynamic stochastic general equilibrium model. The runtime reductions we obtain range from 27% to 88%.
The Evolution of U.S. Retail Concentration
Authors: Dominic A. Smith, Sergio Ocampo
Date: 2022-02-15 00:00:00

Increases in national concentration have been a salient feature of industry dynamics in the U.S. and have contributed to concerns about increasing market power. Yet, local trends may be more informative about market power, particularly in the retail sector where consumers have traditionally shopped at nearby stores. We find that local concentration has increased almost in parallel with national concentration using novel Census data on product-level revenue for all U.S. retail stores between 1992 and 2012. The increases in concentration are broad based, affecting most markets, products, and retail industries. We show that the expansion of multi-market firms into new markets explains most of the increase in national retail concentration, with consolidation via increases in local market shares increasing in importance between 1997 and 2007, and single-market firms playing a negligible role. Finally, we find that increases in local concentration can explain one-quarter to one-third of the observed rise in retail gross margins.
Stablecoins and Central Bank Digital Currencies: Policy and Regulatory Challenges
Authors: Barry Eichengreen, Ganesh Viswanath-Natraj
Date: 2022-02-15 00:00:00

Stablecoins and central bank digital currencies are on the horizon in Asia, and in some cases have already arrived. This paper provides new analysis and a critique of the use case for both forms of digital currency. It provides time-varying estimates of devaluation risk for the leading stablecoin, Tether, using data from the futures market. It describes the formidable obstacles to widespread use of central bank digital currencies in cross-border transactions, the context in which their utility is arguably greatest. The bottom line is that significant uncertainties continue to dog the region's digital currency initiatives.
A Low-Parametric Model for Bit-Rate Estimation of VVC Residual Coding
Authors: Fabian Brand, Christian Herglotz, André Kaup
Date: 2022-02-15 00:00:00
Tasks: Video Compression

There are many tasks within video compression which require fast bit rate estimation. As an example, rate-control algorithms are only feasible because it is possible to estimate the required bit rate without needing to encode the entire block. With residual coding technology becoming more and more sophisticated, the corresponding bit rate models require more advanced features. In this work, we propose a set of four features together with a linear model, which is able to estimate the rate of arbitrary residual blocks which were compressed using the VVC standard. Our method outperforms other methods which were used for the same task both in terms of mean absolute error and mean relative error. Our model deviates by less than 4 bit on average over a large dataset of natural images.
Cerebrovascular morphology in aging and disease -- imaging biomarkers for ischemic stroke and Alzheimers disease
Authors: Aditi Deshpande, Nitya Kari, Jordan Elliott McKenzie, Bin Jiang, Patrik Michel, Nima Toosizadeh, Pouya Tahsili Fahadan, Chelsea Kidwell, Max Wintermark, Kaveh Laksari
Date: 2022-02-14 00:00:00

Background and Purpose: Altered brain vasculature is a key phenomenon in several neurologic disorders. This paper presents a quantitative assessment of vascular morphology in healthy and diseased adults including changes during aging and the anatomical variations in the Circle of Willis (CoW). Methods: We used our automatic method to segment and extract novel geometric features of the cerebral vasculature from MRA scans of 175 healthy subjects, 45 AIS, and 50 AD patients after spatial registration. This is followed by quantification and statistical analysis of vascular alterations in acute ischemic stroke (AIS) and Alzheimer's disease (AD), the biggest cerebrovascular and neurodegenerative disorders. Results: We determined that the CoW is fully formed in only 35 percent of healthy adults and found significantly increased tortuosity and fractality, with increasing age and with disease -- both AIS and AD. We also found significantly decreased vessel length, volume and number of branches in AIS patients. Lastly, we found that AD cerebral vessels exhibited significantly smaller diameter and more complex branching patterns, compared to age-matched healthy adults. These changes were significantly heightened with progression of AD from early onset to moderate-severe dementia. Conclusion: Altered vessel geometry in AIS patients shows that there is pathological morphology coupled with stroke. In AD due to pathological alterations in the endothelium or amyloid depositions leading to neuronal damage and hypoperfusion, vessel geometry is significantly altered even in mild or early dementia. The specific geometric features and quantitative comparisons demonstrate potential for using vascular morphology as a non-invasive imaging biomarker for neurologic disorders.
Alignment Performance of FSOC in Stratosphere and to the Ground Station
Authors: Bereket Kebede
Date: 2022-02-15 00:00:00

Airborne platforms, such as drones, balloons, and aerostats, have recently gained considerable interest in the communication sector. Free-space optical communication (FSOC) systems can deliver information wirelessly at high data rates (above 1 Gbps) using compact and lightweight optical terminals. Thus, airborne FSOC systems have been a recent subject of interest and activity, including emerging projects such as Google Loon and Facebook Aquila. One of major technical challenges of FSOC system is the stringent requirement of pointing, acquisition, and tracking (PAT) capability to ensure the reliable transmission between the transmitter and receiver, due to narrow beam width and movement of airborne platforms. By identifying and validating improvement techniques dealing with this issue, we developed simple models of propeller and wind induced vibrations and investigated the effect of those vibrations on the performance of FSOC systems operating in the stratosphere.
Virtualization of Electromagnetic Operations (VEMO)
Authors: Steven Jones, David Coleman, Robert Nichols
Date: 2022-02-15 00:00:00

Today's operations in the spectrum occur across many disparate and unique devices. For example, a military or commercial maritime platform can have dozens of apertures used for various functions. Utilizing software-defined radios and dynamic analog front ends, we propose that future systems involving multiple applications for RF, virtualize operations to gain performance efficiencies. The concept of virtualization of electromagnetic operations (VEMO) is considered a means to enable flexible use of hardware and spectrum resources to achieve diverse mission requirements in military and commercial settings. This will result in more efficient use of resources, including size/weight/power and integration of objectives across systems that employ the electromagnetic spectrum.