pwc-archive/papers-with-abstracts huggingface dataset:
On Distribution Grid Optimal Power Flow Development and Integration
Authors: Sarmad Hanif, Rabayet Sadnan, Tylor E. Slay, Nawaf Nazir, Shiva Poudel, Bilal Bhatti, Andy Reiman, Jim Follum, Joseph McKinsey, Tarek Elgindy, Rui Yang
Date: 2022-12-09 00:00:00
Tasks: Benchmarking

Due to changes in electric distribution grid operation, new operation regimes have been recommended. Distribution grid optimal power flow (DOPF) has received tremendous attention in the research community, yet it has not been fully adopted across the utility industry. Our paper recognizes this problem and suggests a development and integration procedure for DOPF. We propose development of DOPF as a three step procedure of 1) processing the grid, 2) obtaining a tractable solution, and 3) implementing multiple solution algorithms and benchmarking them to improve application reliability. For the integration of DOPF, we demonstrate how a DOPF federate may be developed that can be integrated in a co-simulation environment to mimic the real-world conditions and hence improve its practicality to be deployed in the field. To demonstrate the efficacy of the proposed methods, tests on IEEE 123-bus system are performed where the usage of tractable formulation in DOPF algorithm development and its comparison to the benchmark solution are demonstrated.
Simulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems
Authors: Rabayet Sadnan, Nathan Gray, Anjan Bose, Anamika Dubey
Date: 2022-12-09 00:00:00
Tasks: Distributed Optimization

Distributed optimization methods have been extensively applied for the optimization of electric power distribution systems, especially for grid-edge coordination. Existing distributed optimization algorithms applied to power distribution systems require many communication rounds among the distributed agents and may pose convergence challenges in difficult nonlinear settings. The communication network parameters also significantly impact the algorithm's performance. In this paper, we propose a scalable, equivalent network approximation-based, distributed optimization algorithm that employs simulation within optimization using the system's digital twin (DT) to solve the optimal power problems (OPF) for a three-phase unbalanced distribution system. The proposed approach is implemented using a cyber-physical co-simulation platform to validate the robustness of the proposed distributed algorithm under stressed communication. The proposed approach is thoroughly validated using the IEEE 123-bus test system.
Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes
Authors: Shiva Moshtagh, Anwarul Islam Sifat, Behrouz Azimian, Anamitra Pal
Date: 2022-12-08 00:00:00
Tasks: Graph Neural Network, State Estimation

Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper circumvents this challenge by formulating a graph neural network (GNN)-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The results obtained using the IEEE 118-bus system indicate that the GNN-based state estimator outperforms both the model-based linear state estimator and a data-driven deep neural network-based state estimator in the presence of non-Gaussian measurement noise and topology changes, respectively.
Data-Driven Flow and Injection Estimation in PMU-Unobservable Transmission Systems
Authors: Satyaprajna Sahoo, Anwarul Islam Sifat, Anamitra Pal
Date: 2022-12-08 00:00:00

Fast and accurate knowledge of power flows and power injections is needed for a variety of applications in the electric grid. Phasor measurement units (PMUs) can be used to directly compute them at high speeds; however, a large number of PMUs will be needed for computing all the flows and injections. Similarly, if they are calculated from the outputs of a linear state estimator, then their accuracy will deteriorate due to the quadratic relationship between voltage and power. This paper employs machine learning to perform fast and accurate flow and injection estimation in power systems that are sparsely observed by PMUs. We train a deep neural network (DNN) to learn the mapping function between PMU measurements and power flows/injections. The relation between power flows and injections is incorporated into the DNN by adding a linear constraint to its loss function. The results obtained using the IEEE 118-bus system indicate that the proposed approach performs more accurate flow/injection estimation in severely unobservable power systems compared to other data-driven methods.
Sampling effects and measurement overlap can bias the inference of neuronal avalanches
Authors: Joao Pinheiro Neto, Franz Paul Spitzner, Viola Priesemann
Date: 2019-10-22 00:00:00

To date, it is still impossible to sample the entire mammalian brain with single-neuron precision. This forces one to either use spikes (focusing on few neurons) or to use coarse-sampled activity (averaging over many neurons, e.g. LFP). Naturally, the sampling technique impacts inference about collective properties. Here, we emulate both sampling techniques on a simple spiking model to quantify how they alter observed correlations and signatures of criticality. We describe a general effect: when the inter-electrode distance is small, electrodes sample overlapping regions in space, which increases the correlation between the signals. For coarse-sampled activity, this can produce power-law distributions even for non-critical systems. In contrast, spike recordings do not suffer this particular bias and underlying dynamics can be identified. This may resolve why coarse measures and spikes have produced contradicting results in the past.
Remote estimation of geologic composition using interferometric synthetic-aperture radar in California's Central Valley
Authors: Kyongsik Yun, Kyra Adams, John Reager, Zhen Liu, Caitlyn Chavez, Michael Turmon, Thomas Lu
Date: 2022-12-04 00:00:00

California's Central Valley is the national agricultural center, producing 1/4 of the nation's food. However, land in the Central Valley is sinking at a rapid rate (as much as 20 cm per year) due to continued groundwater pumping. Land subsidence has a significant impact on infrastructure resilience and groundwater sustainability. In this study, we aim to identify specific regions with different temporal dynamics of land displacement and find relationships with underlying geological composition. Then, we aim to remotely estimate geologic composition using interferometric synthetic aperture radar (InSAR)-based land deformation temporal changes using machine learning techniques. We identified regions with different temporal characteristics of land displacement in that some areas (e.g., Helm) with coarser grain geologic compositions exhibited potentially reversible land deformation (elastic land compaction). We found a significant correlation between InSAR-based land deformation and geologic composition using random forest and deep neural network regression models. We also achieved significant accuracy with 1/4 sparse sampling to reduce any spatial correlations among data, suggesting that the model has the potential to be generalized to other regions for indirect estimation of geologic composition. Our results indicate that geologic composition can be estimated using InSAR-based land deformation data. In-situ measurements of geologic composition can be expensive and time consuming and may be impractical in some areas. The generalizability of the model sheds light on high spatial resolution geologic composition estimation utilizing existing measurements.
The Falsification Adaptive Set in Linear Models with Instrumental Variables that Violate the Exclusion or Conditional Exogeneity Restriction
Authors: Nicolas Apfel, Frank Windmeijer
Date: 2022-12-09 00:00:00
Tasks: valid

Masten and Poirier (2021) introduced the falsification adaptive set (FAS) in linear models with a single endogenous variable estimated with multiple correlated instrumental variables (IVs). The FAS reflects the model uncertainty that arises from falsification of the baseline model. We show that it applies to cases where a conditional exogeneity assumption holds and invalid instruments violate the exclusion assumption only. We propose a generalized FAS that reflects the model uncertainty when some instruments violate the exclusion assumption and/or some instruments violate the conditional exogeneity assumption. Under the assumption that invalid instruments are not themselves endogenous explanatory variables, if there is at least one relevant instrument that satisfies both the exclusion and conditional exogeneity assumptions then this generalized FAS is guaranteed to contain the parameter of interest.
On the Non-Identification of Revenue Production Functions
Authors: David Van Dijcke
Date: 2022-12-09 00:00:00

Production functions are potentially misspecified when revenue is used as a proxy for output. I formalize and strengthen this common knowledge by showing that neither the production function nor Hicks-neutral productivity can be identified with such a revenue proxy. This result obtains when relaxing the standard assumptions used in the literature to allow for imperfect competition. It holds for a large class of production functions, including all commonly used parametric forms. Among the prevalent approaches to address this issue, only those that impose assumptions on the underlying demand system can possibly identify the production function.
$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle
Authors: Jean-Philippe Boussemart, Walter Briec, Raluca Parvulescu, Paola Ravelojaona
Date: 2022-12-09 00:00:00

This paper proposes to estimate the returns-to-scale of production sets by considering the individual return of each observed firm through the notion of $\Lambda$-returns to scale assumption. Along this line, the global technology is then constructed as the intersection of all the individual technologies. Hence, an axiomatic foundation is proposed to present the notion of $\Lambda$-returns to scale. This new characterization of the returns-to-scale encompasses the definition of $\alpha$-returns to scale, as a special case as well as the standard non-increasing and non-decreasing returns-to-scale models. A non-parametric procedure based upon the goodness of fit approach is proposed to assess these individual returns-to-scale. To illustrate this notion of $\Lambda$-returns to scale assumption, an empirical illustration is provided based upon a dataset involving 63 industries constituting the whole American economy over the period 1987-2018.
Monetary Uncertainty as a Determinant of the Response of Stock Market to Macroeconomic News
Authors: Mykola Pinchuk
Date: 2022-12-08 00:00:00

This paper examines the effect of macroeconomic news announcements (MNA) on the stock market. Stocks exhibit a strong positive response to major MNA: 1 standard deviation of MNA surprise causes 11-25 bps higher returns. This response is highly time-varying and is weaker during periods of high monetary uncertainty. I decompose this response into cash flow and risk-free rate channels. 1 standard deviation of good MNA surprise leads to plus 30 bps returns from the cash flow channel and minus 23 bps per 1\% of monetary uncertainty from the risk-free rate channel. Risk-free rate channel is time-varying and is stronger when monetary uncertainty is high. High levels of monetary uncertainty mask the strong positive response of stocks to MNA, which explains why past research failed to detect this relation.
Geometry-aware DoA Estimation using a Deep Neural Network with mixed-data input features
Authors: Ulrik Kowalk, Simon Doclo, Joerg Bitzer
Date: 2022-12-09 00:00:00

Unlike model-based direction of arrival (DoA) estimation algorithms, supervised learning-based DoA estimation algorithms based on deep neural networks (DNNs) are usually trained for one specific microphone array geometry, resulting in poor performance when applied to a different array geometry. In this paper we illustrate the fundamental difference between supervised learning-based and model-based algorithms leading to this sensitivity. Aiming at designing a supervised learning-based DoA estimation algorithm that generalizes well to different array geometries, in this paper we propose a geometry-aware DoA estimation algorithm. The algorithm uses a fully connected DNN and takes mixed data as input features, namely the time lags maximizing the generalized cross-correlation with phase transform and the microphone coordinates, which are assumed to be known. Experimental results for a reverberant scenario demonstrate the flexibility of the proposed algorithm towards different array geometries and show that the proposed algorithm outperforms model-based algorithms such as steered response power with phase transform.
FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications
Authors: Zhe Chen, Garrett J. Blair, Chengdi Cao, Jim Zhou, Daniel Aharoni, Peyman Golshani, Hugh T. Blair, Jason Cong
Date: 2022-12-09 00:00:00

Miniaturized calcium imaging is an emerging neural recording technique that has been widely used for monitoring neural activity on a large scale at a specific brain region of rats or mice. Most existing calcium-image analysis pipelines operate offline. This results in long processing latency, making it difficult to realize closed-loop feedback stimulation for brain research. In recent work, we have proposed an FPGA-based real-time calcium image processing pipeline for closed-loop feedback applications. It can perform real-time calcium image motion correction, enhancement, fast trace extraction, and real-time decoding from extracted traces. Here, we extend this work by proposing a variety of neural network based methods for real-time decoding and evaluate the tradeoff among these decoding methods and accelerator designs. We introduced the implementation of the neural network based decoders on the FPGA, and showed their speedup against the implementation on the ARM processor. Our FPGA implementation enables the real-time calcium image decoding with sub-ms processing latency for closed-loop feedback applications.
RSMA for Dual-Polarized Massive MIMO Networks: A SIC-Free Approach
Authors: Arthur S. de Sena, Pedro H. J. Nardelli, Daniel B. da Costa, Petar Popovski, Constantinos B. Papadias, Merouane Debbah
Date: 2022-12-09 00:00:00

Aiming at overcoming practical issues of successive interference cancellation (SIC), this paper proposes a dual-polarized rate-splitting multiple access (RSMA) technique for a downlink massive multiple-input multiple-output (MIMO) network. By modeling the effects of polarization interference, an in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities and ergodic sum-rates. Simulation results validate the accuracy of the theoretical analysis and confirm the effectiveness of the proposed approach. For instance, under low to moderate cross-polar interference, our results show that the proposed dual-polarized MIMO-RSMA strategy outperforms the single-polarized MIMO-RSMA counterpart for all considered levels of residual SIC error.
Effective, Practical PON Monitoring Beyond the Splitter
Authors: Neil Parkin, Sophie Minoughan, Md Asif Iqbal
Date: 2022-12-09 00:00:00

Monitoring beyond the splitter in a PON is costly due to the need for additional hardware. A non-standard monitoring wavelength can reduce cost and increase the visibility of customers to 97% on a C+ GPON
How Can Optical Communications Shape the Future of Deep Space Communications? A Survey
Authors: Sarah Karmous, Nadia Adem, Mohammed Atiquzzaman, Sumudu Samarakoon
Date: 2022-12-07 00:00:00
Tasks: Survey

With a large number of deep space (DS) missions anticipated by the end of this decade, reliable and high-capacity DS communications are needed more than ever. Nevertheless, existing technologies are far from meeting such a goal. Improving current systems does not only require engineering leadership, but also, very crucially, investigating potential technologies that overcome the unique challenges of ultra-long DS links. To the best of our knowledge, there has not been any comprehenive surveys of DS communications technologies over the last decade. Free space optical (FSO) is an emerging DS technology, proven to acquire lower communications systems size weight and power (SWaP) and achieve a very high capacity compared to its counterpart radio frequency (RF), the currently used DS technology. In this survey, we discuss the pros and cons of deep space optical communications (DSOC) and review their physical and networking characteristics. Furthermore, we provide, for the first time, thoughtful discussions about implementing orbital angular momentum (OAM) and quantum communications (QC) for DS. We elaborate on how these technologies among other field advances including interplanetary network (IPN) and RF/FSO systems improve reliability, capacity, and security. This paper provides a holistic survey of DSOC technologies gathering 247 fragmented pieces of literature and including novel perspectives aiming to set the stage for more developments in the field.
Arbitrage theory in a market of stochastic dimension
Authors: Erhan Bayraktar, Donghan Kim, Abhishek Tilva
Date: 2022-12-09 00:00:00

This paper studies an equity market of stochastic dimension, where the number of assets fluctuates over time. In such a market, we develop the fundamental theorem of asset pricing, which provides the equivalence of the following statements: (i) there exists a supermartingale num\'eraire portfolio; (ii) each dissected market, which is of a fixed dimension between dimensional jumps, has locally finite growth; (iii) there is no arbitrage of the first kind; (iv) there exists a local martingale deflator; (v) the market is viable. We also present the optional decomposition theorem, which characterizes a given nonnegative process as the wealth process of some investment-consumption strategy. Furthermore, similar results still hold in an open market embedded in the entire market of stochastic dimension, where investors can only invest in a fixed number of large capitalization stocks. These results are developed in an equity market model where the price process is given by a piecewise continuous semimartingale of stochastic dimension. Without the continuity assumption on the price process, we present similar results but without explicit characterization of the num\'eraire portfolio.
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
Authors: Yizi Zhang, Meimei Liu, Zhengwu Zhang, David Dunson
Date: 2022-12-08 00:00:00
Tasks: Diffusion  MRI

Mapping of human brain structural connectomes via diffusion MRI offers a unique opportunity to understand brain structural connectivity and relate it to various human traits, such as cognition. However, head displacement during image acquisition can compromise the accuracy of connectome reconstructions and subsequent inference results. We develop a generative model to learn low-dimensional representations of structural connectomes invariant to motion-induced artifacts, so that we can link brain networks and human traits more accurately, and generate motion-adjusted connectomes. We apply the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition. Empirical results demonstrate that the proposed motion-invariant variational auto-encoder (inv-VAE) outperforms its competitors in various aspects. In particular, motion-adjusted structural connectomes are more strongly associated with a wide array of cognition-related traits than other approaches without motion adjustment.
Epidemiological Agent-Based Modelling Software (Epiabm)
Authors: Kit Gallagher, Ioana Bouros, Nicholas Fan, Elizabeth Hayman, Luke Heirene, Patricia Lamirande, Annabelle Lemenuel-Diot, Ben Lambert, David Gavaghan, Richard Creswell
Date: 2022-12-07 00:00:00
Tasks: Epidemiology

Epiabm is a fully tested, open-source software package for epidemiological agent-based modelling, re-implementing the well-known CovidSim model from the MRC Centre for Global Infectious Disease Analysis at Imperial College London. It has been developed as part of the first-year training programme in the EPSRC SABS:R3 Centre for Doctoral Training at the University of Oxford. The model builds an age-stratified, spatially heterogeneous population and offers a modular approach to configure and run epidemic scenarios, allowing for a broad scope of investigative and comparative studies. Two simulation backends are provided: a pedagogical Python backend (with full functionality) and a high performance C++ backend for use with larger population simulations. Both are highly modular, with comprehensive testing and documentation for ease of understanding and extensibility. Epiabm is publicly available through GitHub at https://github.com/SABS-R3-Epidemiology/epiabm.
Robust asymptotic insurance-finance arbitrage
Authors: Katharina Oberpriller, Moritz Ritter, Thorsten Schmidt
Date: 2022-12-09 00:00:00

In most cases, insurance contracts are linked to the financial markets, such as through interest rates or equity-linked insurance products. To motivate an evaluation rule in these hybrid markets, Artzner et al. (2022) introduced the notion of insurance-finance arbitrage. In this paper we extend their setting by incorporating model uncertainty. To this end, we allow statistical uncertainty in the underlying dynamics to be represented by a set of priors $\mathscr{P}$. Within this framework we introduce the notion of robust asymptotic insurance-finance arbitrage and characterize the absence of such strategies in terms of the concept of ${Q}\mathscr{P}$-evaluations. This is a nonlinear two-step evaluation which guarantees no robust asymptotic insurance-finance arbitrage. Moreover, the ${Q}\mathscr{P}$-evaluation dominates all two-step evaluations as long as we agree on the set of priors $\mathscr{P}$ which shows that those two-step evaluations do not allow for robust asymptotic insurance-finance arbitrages. Furthermore, we introduce a doubly stochastic model under uncertainty for surrender and survival. In this setting, we describe conditional dependence by means of copulas and illustrate how the ${Q}\mathscr{P}$-evaluation can be used for the pricing of hybrid insurance products.