pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic controller that operates over homomorphically encrypted data for infinite time horizon
Authors: Junsoo Kim, Hyungbo Shim, Kyoohyung Han
Date: 2019-12-09 00:00:00

In this paper, we present a dynamic feedback controller that computes the next state and the control signal over encrypted data using homomorphic properties of cryptosystems, whose performance is equivalent to the linear dynamic controllers over real-valued data. Assuming that the input as well as the output of the plant is encrypted and transmitted back to the controller, it is shown that the state matrix of any linear time-invariant controller can be always converted to a matrix of integer components. This allows the dynamic feedback controller to operate for infinite time horizon without decryption or reset of its internal state. For implementation in practice, we illustrate the use of a cryptosystem that is based on the Learning With Errors problem, which allows both multiplication and addition over encrypted data. It is also shown that the effect of injected random numbers during encryption for security can be maintained within a small bound by way of the closed-loop stability.
Improving gearshift controllers for electric vehicles with reinforcement learning
Authors: Marc-Antoine Beaudoin, Benoit Boulet
Date: 2021-12-01 00:00:00
Tasks: Experimental Design, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench. Engineers typically treat the mapping from the controller parameters to the gearshift quality as a black-box, and use methods rooted in experimental design -- a purely statistical approach -- to infer the parameter combination that will maximize a chosen gearshift performance indicator. This approach unfortunately requires thousands of gearshift trials, ultimately discouraging the exploration of different control strategies. In this work, we calibrate the feedforward and feedback parameters of a gearshift controller using a model-based reinforcement learning algorithm adapted from Pilco. Experimental results show that the method optimizes the controller parameters with few gearshift trials. This approach can accelerate the exploration of gearshift control strategies, which is especially important for the emerging technology of multi-speed transmissions for electric vehicles.
Simultaneous Controller and Lyapunov Function Design for Constrained Nonlinear Systems
Authors: Reza Lavaei, Leila Bridgeman
Date: 2021-12-01 00:00:00

This paper presents a method to stabilize state and input constrained nonlinear systems using an offline optimization on variable triangulations of the set of admissible states. For control-affine systems, by choosing a continuous piecewise affine (CPA) controller structure, the non-convex optimization is formulated as iterative semi-definite programming (SDP), which can be solved efficiently using available software. The method has very general assumptions on the system's dynamics and constraints. Unlike similar existing methods, it avoids finding terminal invariant sets, solving non-convex optimizations, and does not rely on knowing a control Lyapunov function (CLF), as it finds a CPA Lyapunov function explicitly. The method enforces a desired upper-bound on the decay rate of the state norm and finds the exact region of attraction. Thus, it can be also viewed as a systematic approach for finding Lipschitz CLFs in state and input constrained control-affine systems. Using the CLF, a minimum norm controller is also formulated by quadratic programming for online application.
Energy Management of a Multi-Battery System for Renewable-Based High Power EV Charging
Authors: Jan Engelhardt, Jan Martin Zepter, Tatiana Gabderakhmanova, Mattia Marinelli
Date: 2021-12-01 00:00:00
Tasks: Decision Making, energy management, Management

Hybrid fast-charging stations with battery storage and local renewable generation can facilitate low-carbon electric vehicle (EV) charging, while reducing the stress on the distribution grid. This paper proposes energy management strategies for a novel multi-battery design that directly connects its strings to other DC components through a busbar matrix without the need for interfacing power converters. Hence, the energy management system has two degrees of control: (i) allocating strings to other DC microgrid components, in this case a photovoltaic system, two EV fast chargers, and a grid-tie inverter, and (ii) managing the energy exchange with the local distribution grid. For the grid exchange, a basic droop control is compared to an enhanced control including forecasts in the decision making. To this end, this paper evaluates results from multiple Monte Carlo simulations capturing the uncertainty of EV charging. For a realistic charging behaviour in each simulation run, random fast-charging profiles were created based on probability distributions of actual fast-charging data for arrival time, charging duration, and requested energy. The impact of different utilisation levels of the chargers was assessed by varying the average charging instances from 1 to 30 EVs per day. Using actual photovoltaic measurements from different months, the numerical analyses show that the enhanced control increases self-sufficiency by reducing grid exchange, and decreases the number of battery cycles. However, the enhanced control operates the battery closer to its charge limits, which may accelerate calendar ageing.
An Open Source Software Stack for Tuning the Dynamical Behavior of Complex Power Systems
Authors: Anna Büttner, Hans Würfel, Anton Plietzsch, Michael Lindner, Frank Hellmann
Date: 2021-12-01 00:00:00
Tasks: BIG-bench Machine Learning

BlockSystems.jl and NetworkDynamics.jl are two novel software packages which facilitate highly efficient transient stability simulations of power networks. Users may specify inputs and power system design in a convenient modular and equation-based manner without compromising on speed or model detail. Written in the high-level, high-performance programming language Julia a rich open-source package ecosystem is available, which provides state-of-the-art solvers and machine learning algorithms. Motivated by the recent interest in the Nordic inertia challenge we have implemented the Nordic5 test case and tuned its control parameters by making use of the machine learning and automatic differentiation capabilities of our software stack.
Experimental Validation of Multi-lane Formation Control for Connected and Automated Vehicles in Multiple Scenarios
Authors: Mengchi Cai, Qing Xu, Chunying Yang, Jianghong Dong, Chaoyi Chen, Jiawei Wang, Jianqiang Wang, Keqiang Li
Date: 2021-12-01 00:00:00

Formation control methods of connected and automated vehicles have been proposed to smoothly switch the structure of vehicular formations in different scenarios. In the previous research, simulations are often conducted to verify the performance of formation control methods. This paper presents the experimental results of multi-lane formation control for connected and automated vehicles. The coordinated formation control framework and specific methods utilized for different scenarios are introduced. The details of experimental platform and vehicle control strategy is provided. Simulations and experiments are conducted in different scenarios, and the results indicate that the formation control method is applicable to multiple traffic scenarios and able to improve formation-structure-switching efficiency compared with benchmark methods.
Forgetting leads to chaos in attractor networks
Authors: Ulises Pereira-Obilinovic, Johnatan Aljadeff, Nicolas Brunel
Date: 2021-11-30 00:00:00
Tasks: Retrieval

Attractor networks are an influential theory for memory storage in brain systems. This theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. In this work, we study a sparsely connected attractor network where memories are learned according to a Hebbian synaptic plasticity rule. After recapitulating known results for the continuous, sparsely connected Hopfield model, we investigate a model in which new memories are learned continuously and old memories are forgotten, using an online synaptic plasticity rule. We show that for a forgetting time scale that optimizes storage capacity, the qualitative features of the network's memory retrieval dynamics are age-dependent: most recent memories are retrieved as fixed-point attractors while older memories are retrieved as chaotic attractors characterized by strong heterogeneity and temporal fluctuations. Therefore, fixed-point and chaotic attractors co-exist in the network phase space. The network presents a continuum of statistically distinguishable memory states, where chaotic fluctuations appear abruptly above a critical age and then increase gradually until the memory disappears. We develop a dynamical mean field theory (DMFT) to analyze the age-dependent dynamics and compare the theory with simulations of large networks. Our numerical simulations show that a high-degree of sparsity is necessary for the DMFT to accurately predict the network capacity. Finally, our theory provides specific predictions for delay response tasks with aging memoranda. Our theory of attractor networks that continuously learn new information at the price of forgetting old memories can account for the observed diversity of retrieval states in the cortex, and in particular the strong temporal fluctuations of cortical activity.
Free energy analyses of cell-penetrating peptides using the weighted ensemble method
Authors: Seungho Choe
Date: 2021-12-01 00:00:00

Cell-penetrating peptides (CPPs) have been widely used for drug-delivery agents; however, it has not been fully understood how they translocate across cell membranes. The Weighted Ensemble (WE) method, one of powerful and flexible path sampling techniques, can be helpful to reveal translocation paths and free energy barriers along those paths. Within the WE approach we show how Arg9 (nona-arginine) and Tat interact with a DOPC/DOPG (4:1) model membrane, and we present free energy (or potential mean of forces, PMFs) profiles of penetration, although a translocation across the membrane has not been observed in the current simulations. Two different compositions of lipid molecules were also tried and compared. Our approach can be applied to any CPPs interacting with various model membranes, and it will provide useful information regarding the transport mechanisms of CPPs.
Analyzing the effect of cell rearrangement on Delta-Notch pattern formation
Authors: Toshiki Oguma, Hisako Takigawa-Imamura, Tomoyasu Shinoda, Shuntaro Ogura, Akiyoshi Uemura, Takaki Miyata, Philip K. Maini, Takashi Miura
Date: 2021-12-01 00:00:00

The Delta-Notch system plays a vital role in a number of areas in biology and typically forms a salt and pepper pattern in which cells strongly expressing Delta and cells strongly expressing Notch are alternately aligned via lateral inhibition. Although the spatial arrangement of the cells is important to the Delta-Notch pattern, the effect of cell rearrangement is not often considered. In this study, we provide a framework to analytically evaluate the effect of cell mixing and proliferation on Delta-Notch pattern formation in one spatial dimension. We model cell rearrangement events by a Poisson process and analyze the model while preserving the discrete properties of the spatial structure. We find that the homogeneous expression pattern is stabilized if the frequency of cell rearrangement events is sufficiently large. We analytically obtain the critical frequencies of the cell rearrangement events where the decrease of the pattern amplitude as a result of cell rearrangement is balanced by the increase in amplitude due to the Delta-Notch interaction dynamics. Our theoretical results are qualitatively consistent with experimental results, supporting the notion that the heterogeneity of expression patterns is inversely correlated with cell rearrangement \textit{in vivo}. Our framework, while applied here to the specific case of the Delta-Notch system, is applicable more widely to other pattern formation mechanisms.
Vicinity Effects of Field Free Point on the Relaxation Behavior of MNPs
Authors: Atakan Topcu, Asli Alpman, Mustafa Utkur, Emine Ulku Saritas
Date: 2021-11-30 00:00:00

In Magnetic Particle Imaging (MPI), the distribution of magnetic nanoparticles (MNPs) is imaged by moving a field free point (FFP) in space. All MNPs in close vicinity of the FFP contribute to the signal induced on the receive coil. The relaxation behavior of these MNPs are subject to a DC field due to the selection field (SF). In this work, we investigate the effects of the DC field on the relaxation behavior of the MNPs, with the goal of understanding the differences between the measured relaxations in Magnetic Particle Spectrometer (MPS) setups vs. MPI scanners.
Inequality in economic shock exposures across the global firm-level supply network
Authors: Abhijit Chakraborty, Tobias Reisch, Christian Diem, Stefan Thurner
Date: 2021-12-01 00:00:00

For centuries, national economies created wealth by engaging in international trade and production. The resulting international supply networks not only increase wealth for countries, but also create systemic risk: economic shocks, triggered by company failures in one country, may propagate to other countries. Using global supply network data on the firm-level, we present a method to estimate a country's exposure to direct and indirect economic losses caused by the failure of a company in another country. We show the network of systemic risk-flows across the world. We find that rich countries expose poor countries much more to systemic risk than the other way round. We demonstrate that higher systemic risk levels are not compensated with a risk premium in GDP, nor do they correlate with economic growth. Systemic risk around the globe appears to be distributed more unequally than wealth. These findings put the often praised benefits for developing countries from globalized production in a new light, since they relate them to the involved risks in the production processes. Exposure risks present a new dimension of global inequality, that most affects the poor in supply shock crises. It becomes fully quantifiable with the proposed method.
Geo-political conflicts, economic sanctions and international knowledge flows
Authors: Teemu Makkonen, Timo Mitze
Date: 2021-12-01 00:00:00

We address the question how sensitive international knowledge flows respond to geo-political conflicts taking the politico-economic tensions between EU-Russia since the Ukraine crisis 2014 as case study. We base our econometric analysis on comprehensive data covering more than 500 million scientific publications and 8 million international co-publications between 1995 and 2018. Our findings indicate that the imposition of EU sanctions and Russian counter-sanctions from 2014 onwards has significant negative effects on bilateral international scientific co-publication rates between EU countries and Russia. Depending on the chosen control group and sectors considered, effect size ranges from 15% to 70%. Effects are also observed to grow over time.
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition
Authors: Sundararajan Srinivasan, Zhaocheng Huang, Katrin Kirchhoff
Date: 2021-11-30 00:00:00
Tasks: Emotion Classification, Emotion Recognition, Representation Learning, Speech Emotion Recognition

Generic pre-trained speech and text representations promise to reduce the need for large labeled datasets on specific speech and language tasks. However, it is not clear how to effectively adapt these representations for speech emotion recognition. Recent public benchmarks show the efficacy of several popular self-supervised speech representations for emotion classification. In this study, we show that the primary difference between the top-performing representations is in predicting valence while the differences in predicting activation and dominance dimensions are less pronounced. However, we show that even the best-performing HuBERT representation underperforms on valence prediction compared to a multimodal model that also incorporates text representation. We address this shortcoming by injecting lexical information into the speech representation using the multimodal model as a teacher. To improve the efficacy of our approach, we propose a novel estimate of the quality of the emotion predictions, to condition teacher-student training. We report new audio-only state-of-the-art concordance correlation coefficient (CCC) values of 0.757, 0.627, 0.671 for activation, valence and dominance predictions, respectively, on the MSP-Podcast corpus, and also state-of-the-art values of 0.667, 0.582, 0.545 on the IEMOCAP corpus.
Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields
Authors: Renhao Liu, Yu Sun, Jiabei Zhu, Lei Tian, Ulugbek Kamilov
Date: 2021-11-27 00:00:00
Tasks: Zero-Shot Learning

Intensity diffraction tomography (IDT) refers to a class of optical microscopy techniques for imaging the 3D refractive index (RI) distribution of a sample from a set of 2D intensity-only measurements. The reconstruction of artifact-free RI maps is a fundamental challenge in IDT due to the loss of phase information and the missing cone problem. Neural fields (NF) has recently emerged as a new deep learning (DL) approach for learning continuous representations of physical fields. NF uses a coordinate-based neural network to represent the field by mapping the spatial coordinates to the corresponding physical quantities, in our case the complex-valued refractive index values. We present DeCAF as the first NF-based IDT method that can learn a high-quality continuous representation of a RI volume from its intensity-only and limited-angle measurements. The representation in DeCAF is learned directly from the measurements of the test sample by using the IDT forward model, without any ground-truth RI maps. We qualitatively and quantitatively evaluate DeCAF on the simulated and experimental biological samples. Our results show that DeCAF can generate high-contrast and artifact-free RI maps and lead to up to 2.1 times reduction in MSE over existing methods.
An improved bearing fault detection strategy based on artificial bee colony algorithm
Authors: Haiquan Wang, Wenxuan Yue, Shengjun Wen, Xiaobin Xu, Menghao Su, Shanshan Zhang, Panpan Du
Date: 2021-12-01 00:00:00
Tasks: Fault Detection, feature selection

The operating state of bearing directly affects the performance of rotating machinery and how to accurately and decisively extract features from the original vibration signal and recognize the faulty parts as early as possible is very critical. In this study, the one-dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelets transformation is proposed to calculate the parameter of it which is also the standard deviation of the transformed shaplets that is usually selected by trial and error. Moreover, XGBoost is used to recognize the faults from the obtained features, and an improved artificial bee colony algorithm(ABC) where the evolution is guided by the importance indices of different search space is proposed to optimize the parameters of XGBoost. Here the value of importance index is related to the probability of optimal solutions in certain space, thus the problem of easily falling into local optimality in traditional ABC could be avoided.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97% which is much higher than the ones corresponding to other extraction strategies, thus the ability of extraction could be improved. And with the improved artificial bee colony algorithm which is used to optimize the parameters of XGBoost, the classification accuracy could be improved from 97.02% to about 98.60% compared with the traditional classification strategy
Using Reconfigurable Intelligent Surfaces for UE Positioning in mmWave MIMO Systems
Authors: Wei zhang, Wee Peng Tay
Date: 2021-12-01 00:00:00

A reconfigurable intelligent surface (RIS) consists of massive meta elements, which results in a reflection path between a base station (BS) and user equipment (UE). In wireless localization, this reflection path aids in positioning accuracy, especially when the line-of-sight (LOS) path is subject to severe blockage and fading. We develop a RIS-aided positioning framework to locate a UE in environments where the LOS path may or may not be available. We first estimate the RIS-aided channel parameters from the received signals at the UE. To reduce algorithmic complexity, we propose a linear combination of the estimated UE positions from the direct and reflection paths, which is shown to be approximately the maximum likelihood estimator under the large-sample regime when the estimates from different paths are independent. We optimize the RIS phase shifts to improve the positioning accuracy, and extend the proposed approach to the case with multiple BSs and UEs. We derive the Cramer-Rao bound (CRB) and demonstrate numerically that our proposed method approaches the CRB.
High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces
Authors: Ying He, Dongheng Zhang, Yan Chen
Date: 2021-12-01 00:00:00
Tasks: Privacy Preserving, Quantization, SSIM, Super-Resolution, Vocal Bursts Intensity Prediction

WiFi-based imaging enables pervasive sensing in a privacy-preserving and cost-effective way. However, most of existing methods either require specialized hardware modification or suffer from poor imaging performance due to the fundamental limit of off-the-shelf commodity WiFi devices in spatial resolution. We observe that the recently developed reconfigurable intelligent surface (RIS) could be a promising solution to overcome these challenges. Thus, in this paper, we propose a RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices. Specifically, we first design a beamforming method to achieve the first-stage imaging by separating the signals from different spatial locations with the aid of the RIS. Then, we propose an optimization-based super-resolution imaging algorithm by leveraging the low rank nature of the reconstructed object. During the optimization, we also explicitly take into account the effect of finite phase quantization in RIS to avoid the resolution degradation due to quantization errors. Simulation results demonstrate that our framework achieves median root mean square error (RMSE) of 0.03 and median structural similarity (SSIM) of 0.52. The visual results show that high-resolution imaging results are achieved with simulation signals at 5 GHz that are matched with commercial WiFi 802.11n/ac protocols.