pwc-archive/papers-with-abstracts huggingface dataset:
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Authors: Yuhan Helena Liu, Stephen Smith, Stefan Mihalas, Eric Shea-Brown, Uygar Sümbül
Date: 2022-06-02 00:00:00

The spectacular successes of recurrent neural network models where key parameters are adjusted via backpropagation-based gradient descent have inspired much thought as to how biological neuronal networks might solve the corresponding synaptic credit assignment problem. There is so far little agreement, however, as to how biological networks could implement the necessary backpropagation through time, given widely recognized constraints of biological synaptic network signaling architectures. Here, we propose that extra-synaptic diffusion of local neuromodulators such as neuropeptides may afford an effective mode of backpropagation lying within the bounds of biological plausibility. Going beyond existing temporal truncation-based gradient approximations, our approximate gradient-based update rule, ModProp, propagates credit information through arbitrary time steps. ModProp suggests that modulatory signals can act on receiving cells by convolving their eligibility traces via causal, time-invariant and synapse-type-specific filter taps. Our mathematical analysis of ModProp learning, together with simulation results on benchmark temporal tasks, demonstrate the advantage of ModProp over existing biologically-plausible temporal credit assignment rules. These results suggest a potential neuronal mechanism for signaling credit information related to recurrent interactions over a longer time horizon. Finally, we derive an in-silico implementation of ModProp that could serve as a low-complexity and causal alternative to backpropagation through time.
Hypothesis testing for matched pairs with missing data by maximum mean discrepancy: An application to continuous glucose monitoring
Authors: Marcos Matabuena, Paulo Félix, Marc Ditzhaus, Juan Vidal, Francisco Gude
Date: 2022-06-03 00:00:00

A frequent problem in statistical science is how to properly handle missing data in matched paired observations. There is a large body of literature coping with the univariate case. Yet, the ongoing technological progress in measuring biological systems raises the need for addressing more complex data, e.g., graphs, strings and probability distributions, among others. In order to fill this gap, this paper proposes new estimators of the maximum mean discrepancy (MMD) to handle complex matched pairs with missing data. These estimators can detect differences in data distributions under different missingness mechanisms. The validity of this approach is proven and further studied in an extensive simulation study, and results of statistical consistency are provided. Data from continuous glucose monitoring in a longitudinal population-based diabetes study are used to illustrate the application of this approach. By employing the new distributional representations together with cluster analysis, new clinical criteria on how glucose changes vary at the distributional level over five years can be explored.
Recommending what video to watch next: a multitask ranking system
Authors: Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi
Date: 2019-09-10 00:00:00
Proceeding: RecSys 2019 9
Tasks: Mixture-of-Experts

In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform. The system faces many real-world challenges, including the presence of multiple competing ranking objectives, as well as implicit selection biases in user feedback. To tackle these challenges, we explored a variety of soft-parameter sharing techniques such as Multi-gate Mixture-of-Experts so as to efficiently optimize for multiple ranking objectives. Additionally, we mitigated the selection biases by adopting a Wide & Deep framework. We demonstrated that our proposed techniques can lead to substantial improvements on recommendation quality on one of the world's largest video sharing platforms.
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning
Authors: Rocco Palmitessa, Morten Grum, Allan Peter Engsig-Karup, Roland Löwe
Date: 2022-05-24 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis, Transfer Learning

We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynamic (HiFi) model. Our approach reduces simulation times by one to two orders of magnitude compared to a HiFi model. It is thus slower than e.g. conceptual hydrological models, but it enables simulations of water levels, flows and surcharges in all nodes and links of a drainage network and thus largely preserves the level of detail provided by HiFi models. Comparing time series simulated by the surrogate and the HiFi model, R2 values in the order of 0.9 are achieved. Surrogate training times are currently in the order of one hour. However, they can likely be reduced through the application of transfer learning and graph neural networks. Our surrogate approach will be useful for interactive workshops in initial design phases of urban drainage systems, as well as for real time applications. In addition, our model formulation is generic and future research should investigate its application for simulating other water systems.
A Comparative Study on Energy Consumption Models for Drones
Authors: Carlos Muli, Sangyoung Park, Mingming Liu
Date: 2022-05-30 00:00:00

Creating an appropriate energy consumption prediction model is becoming an important topic for drone-related research in the literature. However, a general consensus on the energy consumption model is yet to be reached at present. As a result, there are many variations that attempt to create models that range in complexity with a focus on different aspects. In this paper, we benchmark the five most popular energy consumption models for drones derived from their physical behaviours and point to the difficulties in matching with a realistic energy dataset collected from a delivery drone in flight under different testing conditions. Moreover, we propose a novel data-driven energy model using the Long Short-Term Memory (LSTM) based deep learning architecture and the accuracy is compared based on the dataset. Our experimental results have shown that the LSTM based approach can easily outperform other mathematical models for the dataset under study. Finally, sensitivity analysis has been carried out in order to interpret the model.
Composite Adaptive Control for Time-varying Systems with Dual Adaptation
Authors: Raghavv Goel, Sayan Basu Roy
Date: 2022-06-03 00:00:00
Tasks: parameter estimation

This paper proposes a composite adaptive control architecture using dual adaptation scheme for dynamical systems comprising time-varying uncertain parameters. While majority of the adaptive control schemes in literature address the case of constant parameters, recent research has conceptualized improved adaptive control techniques for time-varying systems with rigorous stability proofs. The proposed work is an effort towards a similar direction, where a novel dual adaptation mechanism is introduced to efficiently tackle the time-varying nature of the parameters. Projection and $\sigma$-modification algorithms are strategically combined using congelation of variables to claim a global result for the tracking error space. While the classical adaptive systems demand a restrictive condition of persistence of excitation (PE) for accurate parameter estimation, the proposed work relies on a milder condition, called initial excitation (IE) for the same. A rigorous Lyapunov stability analysis is carried out to establish uniformly ultimately bounded (UUB) stability of the closed-loop system. Further it is analytically shown that the proposed work can recover the performance of previously designed IE-based adaptive controller in case of time invariant systems.
An adaptive fuzzy sliding mode controller applied to a chaotic pendulum
Authors: Wallace Moreira Bessa, Aline Souza de Paula, Marcelo Amorim Savi
Date: 2022-06-03 00:00:00
Tasks: LEMMA

In this work, an intelligent controller is employed to the chaos control problem in a nonlinear pendulum. The adopted approach is based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm to cope with modeling inaccuracies. The convergence properties of the closed-loop system are analytically proven using Lyapunov's direct method and Barbalat's lemma. Numerical results are also presented in order to demonstrate the control system performance.
Optimal Control for Unmanned Systems with One-way Broadcast Communication
Authors: Chao Ge, Ge Chen
Date: 2022-06-03 00:00:00

Unmanned systems (USs) including unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles have great application prospects in military and civil fields, among which the process of finding feasible and optimal paths for the agents in USs is a kernel problem. Traditional path finding algorithms are hard to adequately obtain optimal paths in real-time under fast time-varying and poor communication environments. We propose an online optimal control algorithm for USs based on a one-way broadcast communication mode under the assumption of a poor communication environment, mobile targets, radars (or sonar), and missiles (or torpedoes). With the principle of receding horizon control, optimal (or suboptimal) paths are then generated by the approximation theory of neural networks and gradient optimization techniques, with low computation requirements. Also, we give a convergence analysis for our algorithm, and show that each agent can reach its target in finite time under some conditions on agents, targets and radar-missiles. Moreover, simulations demonstrate that the agents in USs can generate optimal (or suboptimal) paths in real time using our algorithm while effectively avoiding collision with other agents or detection by enemy radars.
Dual-Port Dynamically Reconfigurable Battery with Semi-Controlled and Fully-Controlled Outputs
Authors: N. Tashakor, J. Kacetl, J. Fang, Z. Li, S. Goetz
Date: 2022-06-03 00:00:00

Modular multilevel converters (MMC) and cascaded H-bridge (CHB) converters are an established concept in ultra-high voltage systems. In combination with batteries, these circuits allow dynamically changing the series or parallel configuration of subportions of the battery as so-called modular battery integrated converters or reconfigurable batteries, and are being discussed for grid-storage and electromobility applications. A large body of research focuses on such circuits for supplying a single load, such as a motor for electric drives. Modularity, failure tolerance, less dependence on the weakest element of a battery pack, higher controllability, and better efficiency are the main incentives behind this pursuit. However, most studies neglect the auxiliary loads which require isolation from the high-voltage battery. This paper proposes a simple topology and controller that can fork off a second (galvanically isolated) output of a reconfigurable dc battery. The proposed system provides a nonisolated semicontrolled port for the dc link to maintain the operating point of the main inverter(s) close to optimal, while fully controlling an isolated output for the auxiliaries per the safety regulations. The proposed system does not require additional active switches for the auxiliary port and can operate with a wide range of voltages. Simulation and experiments verify the developed analysis.
Receding Horizon Games with Coupling Constraints for Demand-Side Management
Authors: Sophie Hall, Giuseppe Belgioioso, Dominic Liao-McPherson, Florian Dörfler
Date: 2022-06-03 00:00:00
Tasks: Management, Model Predictive Control

Distributed energy storage and flexible loads are essential tools for ensuring stable and robust operation of the power grid in spite of the challenges arising from the integration of volatile renewable energy generation and increasing peak loads due to widespread electrification. This paper proposes a demand-side management policy to coordinate self-interested energy prosumers based on receding horizon games, i.e., a closed-loop receding-horizon implementation of game-theoretic day-ahead planning. Practical stability and recursive constraint satisfaction of the proposed feedback control policy is proven under symmetric pricing assumptions using tools from game theory and economic model predictive control. Our numerical studies show that the proposed approach is superior to standard open-loop day-head implementations in terms of peak-shaving, disturbance rejection, and control performance.
Data Encryption based on 9D Complex Chaotic System with Quaternion for Smart Grid
Authors: Fangfang Zhang, Zhe Huang, Lei Kou, Yang Li, Maoyong Cao, Fengying Ma
Date: 2022-06-03 00:00:00
Tasks: Management

With the development of smart grid, the operation and control of power system is realized through power communication network, especially the power production and enterprise management business involve a large amount of sensitive information, and the requirements for data security and real-time transmission are gradually improved. In this paper, a new 9D complex chaotic system with quaternion is proposed for the encryption of smart grid data. Firstly, a new 9D complex chaotic system with quaternion is proposed, and its attractors, bifurcation diagram, complexity, and 0-1 test are analyzed. Secondly, the pseudo-random sequences are generated by the new chaotic system to encrypt power data. Finally, the proposed encryption algorithm is verifed with power data and images in the smart grid, which can ensure the encryption security and real-time. The verifcation results show that the proposed encryption scheme is technically feasible and available for power data and image encryption in smart grid.
Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers
Authors: Kendric R. Ortiz, Adam J. Thorpe, AnaMaria Perez, Maya Luster, Brandon J. Pitts, Meeko Oishi
Date: 2022-06-02 00:00:00
Tasks: Collision Avoidance

Variability in human response creates non-trivial challenges for modeling and control of human-automation systems. As autonomy becomes pervasive, methods that can accommodate human variability will become paramount, to ensure efficiency, safety, and high levels of performance. We propose an easily computable modeling framework which takes advantage of a metric to assess variability in individual human response in a dynamic task that subjects repeat over several trials. Our approach is based in a transformation of observed trajectories to a reproducing kernel Hilbert space, which captures variability in human response as a distribution embedded within the Hilbert space. We evaluate the similarity across responses via the maximum mean discrepancy, which measures the distance between distributions within the Hilbert space. We apply this metric to a difficult driving task designed to elucidate differences across subjects. We conducted a pilot study with 6 subjects in an advanced driving simulator, in which subjects were tasked with collision avoidance of an obstacle in the middle of the road, around a blind corner, in a nighttime scenario, while steering only with the non-dominant hand.
Impact of Frequency Support by Wind Turbines on Small-Signal Stability of Power Systems
Authors: Antonio Pepiciello, José Luis Domínguez-García, Alfredo Vaccaro
Date: 2022-06-02 00:00:00

Rising wind energy integration, accompanied by a decreasing level of system inertia, requires additional sources of ancillary services. Wind turbines based on doubly fed induction generators (DFIG) can provide inertial and primary frequency support, when equipped with specific controls. This paper investigates the effect of frequency support provision by DFIGs on the small-signal stability of power systems. To this end, a modified version of the Kundur two-area test system is employed to analyze different scenarios. Wind energy generation is either added to the existing system or displaces part of the synchronous generation. Simulations show that primary frequency support tends to improve the damping of electromechanical oscillations and deteriorate it for converter control-based ones. On the other hand, inertial response may be either beneficial, detrimental or negligible to damping, depending on the tuning of control parameters.
Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
Authors: Alexander Henkes, Henning Wessels
Date: 2022-05-31 00:00:00
Tasks: Generative Adversarial Network, Uncertainty Quantification

Multiscale simulations are demanding in terms of computational resources. In the context of continuum micromechanics, the multiscale problem arises from the need of inferring macroscopic material parameters from the microscale. If the underlying microstructure is explicitly given by means of microCT-scans, convolutional neural networks can be used to learn the microstructure-property mapping, which is usually obtained from computational homogenization. The CNN approach provides a significant speedup, especially in the context of heterogeneous or functionally graded materials. Another application is uncertainty quantification, where many expansive evaluations are required. However, one bottleneck of this approach is the large number of training microstructures needed. This work closes this gap by proposing a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors. During prediction time, the network can produce unique three-dimensional microstructures with the same properties of the original data in a fraction of seconds and at consistently high quality.
Enhanced precision of circadian rhythm by output system
Authors: Hotaka Kaji, Fumito Mori, Hiroshi Ito
Date: 2022-06-02 00:00:00
Tasks: Denoising, Rhythm

Circadian rhythms are biological rhythms of approximately 24 h that persist even under constant conditions without environmental daily cues. The molecular circadian clock machinery generates the physiological rhythms, which can be transmitted into the downstream output system. Owing to the stochastic nature of the biochemical reactions, the oscillation period of circadian rhythms exhibited by individual organisms or cells is not constant on a daily basis with variations as high as 10 % in terms of the coefficient of variation. Although the fluctuations in circadian rhythm is measured through a reporter such as bioluminescence or fluorescence, experimentally confirming whether the fluctuations found in the reporter system are the same as those in the clock itself is challenging. This study numerically and analytically investigated a coupled system of a circadian clock and its output system, and then compared the fluctuations in the oscillation period of the two systems. We found that the amount of fluctuation in the output system is smaller than that in the circadian clock when the degradation rate of the molecules responsible for the output system was at typical values. The results obtained imply that the output system improves the accuracy of the circadian rhythm without the need for any special denoising processes.
Non-Identifiability in Network Autoregressions
Authors: Federico Martellosio
Date: 2020-11-22 00:00:00

We study identifiability of the parameters in autoregressions defined on a network. Most identification conditions that are available for these models either rely on the network being observed repeatedly, are only sufficient, or require strong distributional assumptions. This paper derives conditions that apply even when the individuals composing the network are observed only once, are necessary and sufficient for identification, and require weak distributional assumptions. We find that the model parameters are generically, in the measure theoretic sense, identified even without repeated observations, and analyze the combinations of the interaction matrix and the regressor matrix causing identification failures. This is done both in the original model and after certain transformations in the sample space, the latter case being relevant, for example, in some fixed effects specifications.
Multi-Asset Bubbles Equilibrium Price Dynamics
Authors: Francesco Cordoni
Date: 2022-06-03 00:00:00

The price-bubble and crash process formation is theoretically investigated in a two-asset equilibrium model. Sufficient and necessary conditions are derived for the existence of average equilibrium price dynamics of different agent-based models, where agents are distinguished in terms of factor and investment trading strategies. In line with experimental results, we show that assets with a positive average dividend, i.e., with a strictly declining fundamental value, display at the equilibrium price the typical hump-shaped bubble observed in experimental asset markets. Moreover, a misvaluation effect is observed in the asset with a constant fundamental value, triggered by the other asset that displays the price bubble shape when a sharp price decline is exhibited at the end of the market.
Fundamentals of RIS-Aided Localization in the Far-Field
Authors: Don-Roberts Emenonye, Harpreet S. Dhillon, R. Michael Buehrer
Date: 2022-06-03 00:00:00

This paper develops fundamental bounds for localization in orthogonal frequency division multiplexing (OFDM) systems aided by reconfigurable intelligent surfaces (RISs). Specifically, we start from the assumption that the position and orientation of a RIS can be viewed as prior information for RIS-aided localization in wireless systems and derive Bayesian bounds for the localization of a user equipment (UE). To do this, we first derive the Bayesian Fisher information matrix (FIM) for channel parameters to derive the Bayesian localization bounds. Then, to focus on the geometric channel parameters, we derive the equivalent Fisher information matrix (EFIM) and show that it has a definite structure. Subsequently, we show through the information loss associated with the EFIM that when the RIS reflection coefficients remain constant across all OFDM symbols, and there is no prior information about the nuisance parameters, the corresponding submatrix in the EFIM related to the RIS angle parameters is a zero matrix. As a result of the EFIM being a zero matrix, estimating the RIS-related angle channel parameters is not possible when the RIS reflection coefficients remain constant across all OFDM symbols. This observation is crucial for the estimation of the RIS-related angle parameters. It dictates that to estimate the RIS-related angle parameters, there must be more than one OFDM transmission with differing RIS reflection coefficients.