pwc-archive/papers-with-abstracts huggingface dataset:
A Wearable EEG System for Closed-Loop Neuromodulation of High-Frequency Sleep-Related Oscillations
Authors: Scott Bressler, Ryan Neely, Heather Read, Ryan Yost, David Wang
Date: 2022-12-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Quality

In healthy sleepers, cortical alpha oscillations are present during the transition from wakefulness to sleep, and dissipate at sleep onset. For individuals with insomnia, alpha power is elevated during the wake-sleep transition and can persist throughout the night. Neuromodulation techniques using phase-locked stimulation have been put forth as alternatives to drugs for improving slow-wave sleep quality. Due to technical limitations, this approach has not been tested on faster frequency alpha oscillations. Here we examine the feasibility of using an endpoint-corrected version of the Hilbert Transform (ecHT) algorithm implemented on-device to measure alpha phase and deliver phase-locked auditory stimulation to modulate alpha and promote sleep initiation. First, the ecHT algorithm is implemented on a tabletop electroencephalogram (EEG) device and used to measure the timing of the auditory evoked response and its delivery at precise phases of the alpha oscillation. Secondly, a pilot at-home study tests feasibility to use a wearable version of the neuromodulation device for real-time phase-locked stimulation in the alpha (8-12 Hz) frequency range. Auditory stimulation was delivered at the intended phases of alpha with high precision, and alpha oscillations were affected differently by stimuli delivered at opposing phases. Our wearable system was capable of measuring sleep micro- and macro-events present in the EEG that were appropriate for clinical sleep scoring. Sleep onset latencies were reduced for a subset of subjects displaying sleep onset insomnia symptoms in the stimulation condition. This study demonstrates the feasibility of closed-loop tracking and neuromodulation of alpha oscillations using a wearable EEG device. Preliminary results suggest that this approach could be used to accelerate sleep initiation in individuals with objective insomnia symptoms.
Machine Learning Assisted Inertia Estimation using Ambient Measurements
Authors: Mingjian Tuo, Xingpeng Li
Date: 2022-12-21 00:00:00

With the increasing penetration of converter-based renewable resources, different types of dynamics have been introduced to the power system. Due to the complexity and high order of the modern power system, mathematical model-based inertia estimation method becomes more difficult. This paper proposes two novel machine learning assisted inertia estimation methods based on long-recurrent convolutional neural (LRCN) network and graph convolutional neural (GCN) network respectively. Informative features are extracted from ambient measurements collected through phasor measurement units (PMU). Spatial structure with high dimensional features and graphical information are then incorporated to improve the accuracy of the inertia estimation. Case studies are conducted on the IEEE 24-bus system. The proposed LRCN and GCN based inertia estimation models achieve an accuracy of 97.34% and 98.15% respectively. Furthermore, the proposed zero generation injection bus based optimal PMU placement (ZGIB-OPP) has been proved to be able to maximize the system observability, which subsequently improves the performance of all proposed inertia estimation models.
Standoff Tracking Using DNN-Based MPC with Implementation on FPGA
Authors: Fei Dong, Xingchen Li, Keyou You, Shiji Song
Date: 2022-12-21 00:00:00
Tasks: Model Predictive Control, Trajectory Planning, valid

This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height. We propose a novel Lyapunov guidance vector (LGV) field with tunable convergence rates for the UAV's trajectory planning and a deep neural network (DNN)-based model predictive control (MPC) scheme to track the reference trajectory. Then, we show how to collect samples for training the DNN offline and design an integral module (IM) to refine the tracking performance of our DNN-based MPC. Moreover, the hardware-in-the-loop (HIL) simulation with an FPGA@200MHz demonstrates that our method is a valid alternative to embedded implementations of MPC for addressing complex systems and applications which is impossible for directly solving the MPC optimization problems.
Driving Style Recognition at First Impression for Online Trajectory Prediction
Authors: Tu Xu, Kan Wu, Yongdong Zhu, Wei Ji
Date: 2022-12-21 00:00:00
Tasks: Autonomous Vehicles, Trajectory Prediction

This paper proposes a new driving style recognition approach that allows autonomous vehicles (AVs) to perform trajectory predictions for surrounding vehicles with minimal data. Toward that end, we use a hybrid of offline and online methods in the proposed approach. We first learn typical driving styles with PCA and K-means algorithms in the offline part. After that, local Maximum-Likelihood techniques are used to perform online driving style recognition. We benchmarked our method on a real driving dataset against other methods in terms of the RMSE value of the predicted trajectory and the observed trajectory over a 5s duration. The proposed approach can reduce trajectory prediction error by up to 37.7\% compared to using the parameters from other literature and up to 24.4\% compared to not performing driving style recognition.
Real-time Path Planning of Driver-less Mining Trains with Time-dependent Physical Constraints
Authors: Xiaojiang Ren, Hui Guo, Sheng Kai, Guoqiang Mao
Date: 2022-12-21 00:00:00
Tasks: Scheduling

While the increased automation levels of production and operation equipment have led to improved productivity of mining activity in open pit mines, the capacity of mine transport system become a bottleneck. The optimization of mine transport system is of great practical significance to reduce the production and operation cost and improve the production and organizational efficiency of mines. In this paper we first formulate a multi-objective optimisation problem for mine railway scheduling by introducing a set of mathematical constraints. As the problem is NP-hard, we then devise a Mixed Integer Programming based solution to solve this problem, and develop an online framework accordingly. We finally conduct test cases to evaluate the performance of the proposed solution. Experimental results demonstrate that the proposed solution is efficient and able to generate train schedule in a real-time manner.
Towards data-driven stochastic predictive control
Authors: Guanru Pan, Ruchuan Ou, Timm Faulwasser
Date: 2022-12-20 00:00:00
Tasks: LEMMA

Data-driven predictive control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, little has been done on data-driven stochastic control. In this paper, we propose a data-driven stochastic predictive control scheme for LTI systems subject to possibly unbounded additive process disturbances. Based on a stochastic extension of the fundamental lemma and leveraging polynomial chaos expansions, we construct a data-driven surrogate Optimal Control Problem (OCP). Moreover, combined with an online selection strategy of the initial condition of the OCP, we provide sufficient conditions for recursive feasibility and for stability of the proposed data-driven predictive control scheme. Finally, two numerical examples illustrate the efficacy and closed-loop properties of the proposed scheme for process disturbances governed by different distributions.
Page time and the order parameter for a consciousness state
Authors: Alexander Gorsky
Date: 2022-12-18 00:00:00
Tasks: Time Series, Time Series Analysis

In this short note using the analogy with the recent resolution of the black hole information paradox we conjecture the order parameter for the state of consciousness based on the notion of the Page curve and the Page time. The entanglement between the state of the brain and time series of neuronal firing as well as the non-orthogonality of the functional connectomes play a key role.
Characterization of micro pore optics for full-field X-ray fluorescence imaging
Authors: Siwen An, David Krapohl, Benny Thörnberg, Romain Roudot, Emile Schyns, Börje Norlin
Date: 2022-12-21 00:00:00

Elemental mapping images can be achieved through step scanning imaging using pinhole optics or micro pore optics (MPO), or alternatively by full-field X-ray fluorescence imaging (FF-XRF). X-ray optics for FF-XRF can be manufactured with different micro-channel geometries such as square, hexagonal or circular channels. Each optic geometry creates different imaging artefacts. Square-channel MPOs generate a high intensity central spot due to two reflections via orthogonal channel walls inside a single channel, which is the desirable part for image formation, and two perpendicular lines forming a cross due to reflections in one plane only. Thus, we have studied the performance of a square-channel MPO in an FF-XRF imaging system. The setup consists of a commercially available MPO provided by Photonis and a Timepix3 readout chip with a silicon detector. Imaging of fluorescence from small metal particles has been used to obtain the point spread function (PSF) characteristics. The transmission through MPO channels and variation of the critical reflection angle are characterized by measurements of fluorescence from Copper and Titanium metal fragments. Since the critical angle of reflection is energy dependent, the cross-arm artefacts will affect the resolution differently for different fluorescence energies. It is possible to identify metal fragments due to the form of the PSF function. The PSF function can be further characterized using a Fourier transform to suppress diffuse background signals in the image.
The Inverse of Exact Renormalization Group Flows as Statistical Inference
Authors: David S. Berman, Marc S. Klinger
Date: 2022-12-21 00:00:00
Tasks: Bayesian Inference

We build on the view of the Exact Renormalization Group (ERG) as an instantiation of Optimal Transport described by a functional convection-diffusion equation. We provide a new information theoretic perspective for understanding the ERG through the intermediary of Bayesian Statistical Inference. This connection is facilitated by the Dynamical Bayesian Inference scheme, which encodes Bayesian inference in the form of a one parameter family of probability distributions solving an integro-differential equation derived from Bayes' law. In this note, we demonstrate how the Dynamical Bayesian Inference equation is, itself, equivalent to a diffusion equation which we dub Bayesian Diffusion. Identifying the features that define Bayesian Diffusion, and mapping them onto the features that define the ERG, we obtain a dictionary outlining how renormalization can be understood as the inverse of statistical inference.
Impulse Pattern Formulation (IPF) Brain Model
Authors: Rolf Bader
Date: 2022-12-21 00:00:00
Tasks: ERP, model, Rhythm

A new brain model is introduced, based on the Impulse Pattern Formulation (IPF) already established for modeling and understanding musical instrument and rhythm perception and production. It assumes the brain works with impulses, neural bursts, ejected from an arbitrary reference point in the brain, arriving at other reflecting brain regions, and returning to the reference point delayed and damped. A plasticity model is suggested to adjust reflection strength in time. The model is systematically studied with 50 reflection points by varying the amount of excitatory vs. inhibitory neurons, the presence or absence of plasticity or external sensory input, and the strength of the input and plasticity in terms of system adaptation to an input or to the system itself. The Brain IPF shows adaptation to an external stimulus, which is stronger without plasticity, showing the active brain not being a simple passive \emph{tabula rasa}. A relation of 10-20\% of inhibitory vs. excitatory neurons, as found in the brain, shows a maximum adaptation to an external stimulus compared to all other relations, pointing to an optimum of this relation concerning adaptation. When assuming strong brain periodicities only up to about 100 Hz, the reflection strength of the model is highest for delays of around 300 ms, corresponding to Event-Related Potential (ERP) timescales of brain potentials most often found roughly between 100 - 400 ms. The mean convergence times of the model correspond to short-time memory time scales with a mean of five seconds for converging IPFs. The Brain IPF is computationally very cheap, highly flexible, and with musical instruments already found to be of high predictive precision. Therefore, in future studies, the Brain IPF might be a model able to understand very large systems composed of an ensemble of brains as well as cultural artifacts and ecological entities.
Satellite-derived solar radiation for intra-hour and intra-day applications: Biases and uncertainties by season and altitude
Authors: Alberto Carpentieri, Doris Folini, Martin Wild, Laurent Vuilleumier, Angela Meyer
Date: 2022-10-05 00:00:00

Accurate estimates of the surface solar radiation (SSR) are a prerequisite for intra-day forecasts of solar resources and photovoltaic power generation. Intra-day SSR forecasts are of interest to power traders and to operators of solar plants and power grids who seek to optimize their revenues and maintain the grid stability by matching power supply and demand. Our study analyzes systematic biases and the uncertainty of SSR estimates derived from Meteosat with the SARAH-2 and HelioMont algorithms at intra-hour and intra-day time scales. The satellite SSR estimates are analyzed based on 136 ground stations across altitudes from 200 m to 3570 m Switzerland in 2018. We find major biases and uncertainties in the instantaneous, hourly and daily-mean SSR. In peak daytime periods, the instantaneous satellite SSR deviates from the ground-measured SSR by a mean absolute deviation (MAD) of 110.4 and 99.6 W/m2 for SARAH-2 and HelioMont, respectively. For the daytime SSR, the instantaneous, hourly and daily-mean MADs amount to 91.7, 81.1, 50.8 and 82.5, 66.7, 42.9 W/m2 for SARAH-2 and HelioMont, respectively. Further, the SARAH-2 instantaneous SSR drastically underestimates the solar resources at altitudes above 1000 m in the winter half year. A possible explanation in line with the seasonality of the bias is that snow cover may be misinterpreted as clouds at higher altitudes.
SiteFerret: beyond simple pocket identification in proteins
Authors: Luca Gagliardi, Walter Rocchia
Date: 2022-12-22 00:00:00
Tasks: Anomaly Detection, Clustering

We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual SES probe spheres obtained from the geometrical primitives generated by the NanoShaper software. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight on which geometrical (clustering) and chemical (residues) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a reliable representation that pinpoint the binding site with greater precision. Site Ferret is outstanding in its versatility, accurately predicting a wide range of binding sites, from small molecules to peptides and difficult shallow sites.
Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction
Authors: Alan Kai Hassen, Paula Torren-Peraire, Samuel Genheden, Jonas Verhoeven, Mike Preuss, Igor Tetko
Date: 2022-12-12 00:00:00
Tasks: Benchmarking, Multi-step retrosynthesis, Retrosynthesis, Single-step retrosynthesis, valid

Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequently, the goal is to provide a valid synthesis route for a molecule. As more single-step models develop, we see increasing accuracy in the prediction of molecular disconnections, potentially improving the creation of synthetic paths. Multi-step approaches repeatedly apply the chemical information stored in single-step retrosynthesis models. However, this connection is not reflected in contemporary research, fixing either the single-step model or the multi-step algorithm in the process. In this work, we establish a bridge between both tasks by benchmarking the performance and transfer of different single-step retrosynthesis models to the multi-step domain by leveraging two common search algorithms, Monte Carlo Tree Search and Retro*. We show that models designed for single-step retrosynthesis, when extended to multi-step, can have a tremendous impact on the route finding capabilities of current multi-step methods, improving performance by up to +30% compared to the most widely used model. Furthermore, we observe no clear link between contemporary single-step and multi-step evaluation metrics, showing that single-step models need to be developed and tested for the multi-step domain and not as an isolated task to find synthesis routes for molecules of interest.
Role of predator-prey reversal in Rock-Paper-Scissors models
Authors: P. P. Avelino, B. F. de Oliveira, R. S. Trintin
Date: 2022-12-22 00:00:00

In this letter we consider a single parameter generalization of the standard three species Rock-Paper-Scissors (RPS) model allowing for predator-prey reversal. This model, which shall be referred to as $\kappa$RPS model, incorporates bidirectional predator-prey interactions between all the species in addition to the unidirectional predator-prey interactions of the standard RPS model. We study the dynamics of a May-Leonard formulation of the $\kappa$RPS model using lattice based spatial stochastic simulations with random initial conditions. We find that if the simulation lattices are sufficiently large for the coexistence of all three species to be maintained, the model asymptotically leads to the formation of spiral patterns whose evolution is qualitatively similar to that of the standard RPS model, albeit with larger characteristic length and time scales. We show that there are in general two distinct scaling regimes: one transient curvature dominated regime in which the characteristic length of the population network grows with time and another where it becomes a constant. We also estimate the dependence of the asymptotic value of the characteristic length of the population network on the likelihood of predator-prey reversal and show that if the simulation lattices are not sufficiently large then predator-prey reversal can have a significant negative impact on coexistence. Finally, we interpret these results by considering the much simpler dynamics of circular domains.
Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models
Authors: Timo Dimitriadis, Roxana Halbleib, Jeannine Polivka, Jasper Rennspies, Sina Streicher, Axel Friedrich Wolter
Date: 2022-12-22 00:00:00

This paper analyzes the benefits of sampling intraday returns in intrinsic time for the realized variance (RV) estimator. We theoretically show in finite samples that depending on the permitted sampling information, the RV estimator is most efficient under either hitting time sampling that samples whenever the price changes by a pre-determined threshold, or under the new concept of realized business time that samples according to a combination of observed trades and estimated tick variance. The analysis builds on the assumption that asset prices follow a diffusion that is time-changed with a jump process that separately models the transaction times. This provides a flexible model that allows for leverage specifications and Hawkes-type jump processes and separately captures the empirically varying trading intensity and tick variance processes, which are particularly relevant for disentangling the driving forces of the sampling schemes. Extensive simulations confirm our theoretical results and show that for low levels of noise, hitting time sampling remains superior while for increasing noise levels, realized business time becomes the empirically most efficient sampling scheme. An application to stock data provides empirical evidence for the benefits of using these intrinsic sampling schemes to construct more efficient RV estimators as well as for an improved forecast performance.
A Bootstrap Specification Test for Semiparametric Models with Generated Regressors
Authors: Elia Lapenta
Date: 2022-12-21 00:00:00
Tasks: valid

This paper provides a specification test for semiparametric models with nonparametrically generated regressors. Such variables are not observed by the researcher but are nonparametrically identified and estimable. Applications of the test include models with endogenous regressors identified by control functions, semiparametric sample selection models, or binary games with incomplete information. The statistic is built from the residuals of the semiparametric model. A novel wild bootstrap procedure is shown to provide valid critical values. We consider nonparametric estimators with an automatic bias correction that makes the test implementable without undersmoothing. In simulations the test exhibits good small sample performances, and an application to women's labor force participation decisions shows its implementation in a real data context.
Partly Linear Instrumental Variables Regressions without Smoothing on the Instruments
Authors: Jean-Pierre Florens, Elia Lapenta
Date: 2022-12-21 00:00:00

We consider a semiparametric partly linear model identified by instrumental variables. We propose an estimation method that does not smooth on the instruments and we extend the Landweber-Fridman regularization scheme to the estimation of this semiparametric model. We then show the asymptotic normality of the parametric estimator and obtain the convergence rate for the nonparametric estimator. Our estimator that does not smooth on the instruments coincides with a typical estimator that does smooth on the instruments but keeps the respective bandwidth fixed as the sample size increases. We propose a data driven method for the selection of the regularization parameter, and in a simulation study we show the attractive performance of our estimators.
Inference for Model Misspecification in Interest Rate Term Structure using Functional Principal Component Analysis
Authors: Kaiwen Hou
Date: 2022-12-21 00:00:00

Level, slope, and curvature are three commonly-believed principal components in interest rate term structure and are thus widely used in modeling. This paper characterizes the heterogeneity of how misspecified such models are through time. Presenting the orthonormal basis in the Nelson-Siegel model interpretable as the three factors, we design two nonparametric tests for whether the basis is equivalent to the data-driven functional principal component basis underlying the yield curve dynamics, considering the ordering of eigenfunctions or not, respectively. Eventually, we discover high dispersion between the two bases when rare events occur, suggesting occasional misspecification even if the model is overall expressive.