pwc-archive/papers-with-abstracts huggingface dataset:
Reactive and Risk-Aware Control for Signal Temporal Logic
Authors: Lars Lindemann, George J. Pappas, Dimos V. Dimarogonas
Date: 2021-08-30 00:00:00

The deployment of autonomous systems in uncertain and dynamic environments has raised fundamental questions. Addressing these is pivotal to build fully autonomous systems and requires a systematic integration of planning and control. We first propose reactive risk signal interval temporal logic (ReRiSITL) as an extension of signal temporal logic (STL) to formulate complex spatiotemporal specifications. Unlike STL, ReRiSITL allows to consider uncontrollable propositions that may model humans as well as random environmental events such as sensor failures. Additionally, ReRiSITL allows to incorporate risk measures, such as (but not limited to) the Conditional Value-at-Risk, to measure the risk of violating certain spatial specifications. Second, we propose an algorithm to check if an ReRiSITL specification is satisfiable. For this purpose, we abstract the ReRiSITL specification into a timed signal transducer and devise a game-based approach. Third, we propose a reactive planning and control framework for dynamical control systems under ReRiSITL specifications.
LSD-StructureNet: Modeling Levels of Structural Detail in 3D Part Hierarchies
Authors: Dominic Roberts, Ara Danielyan, Hang Chu, Mani Golparvar-Fard, David Forsyth
Date: 2021-08-18 00:00:00
Proceeding: ICCV 2021 10
Tasks: Diversity

Generative models for 3D shapes represented by hierarchies of parts can generate realistic and diverse sets of outputs. However, existing models suffer from the key practical limitation of modelling shapes holistically and thus cannot perform conditional sampling, i.e. they are not able to generate variants on individual parts of generated shapes without modifying the rest of the shape. This is limiting for applications such as 3D CAD design that involve adjusting created shapes at multiple levels of detail. To address this, we introduce LSD-StructureNet, an augmentation to the StructureNet architecture that enables re-generation of parts situated at arbitrary positions in the hierarchies of its outputs. We achieve this by learning individual, probabilistic conditional decoders for each hierarchy depth. We evaluate LSD-StructureNet on the PartNet dataset, the largest dataset of 3D shapes represented by hierarchies of parts. Our results show that contrarily to existing methods, LSD-StructureNet can perform conditional sampling without impacting inference speed or the realism and diversity of its outputs.
Development of User-friendly Smart Grid Architecture
Authors: Swaroop Mishra
Date: 2021-08-31 00:00:00

As systems like smart grid continue to become complex on a daily basis, emerging issues demand complex solutions that can deal with parameters in multiple domains of engineering. The complex solutions further demand a friendly interface for the users to express their requirements. Cyber-Physical systems deals with the study of techniques that are committed to modeling, simulating and solving the problems that emerge from a multi-disciplinary outlook towards futuristic systems. This thesis is mainly concerned with the development of user-friendly cyber-physical frameworks that can tackle various issues faced by the utilities and users of smart grid through a suitable choice of smart microgrid architecture.
A nonlinear cross-diffusion epidemic with time-dependent SIRD system: Multiscale derivation and computational analysis
Authors: Mohamed Zagour
Date: 2021-08-28 00:00:00

A nonlinear cross-diffusion epidemic with a time-dependent Susceptible-Infected-Recovered-Died system is proposed in this paper. This system is derived from kinetic theory model by multiscale approach, which leads to an equivalent system coupled the microscopic and macroscopic equations. Subsequently, numerical investigations to design asymptotic preserving scheme property is developed and validated by various numerical tests. Finally, the numerical computational results of the proposed system are discussed in two dimensional space using the finite volume method.
Music Demixing Challenge 2021
Authors: Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich, Fabian-Robert Stöter, Alexandre Défossez, Minseok Kim, Woosung Choi, Chin-Yun Yu, Kin-Wai Cheuk
Date: 2021-08-31 00:00:00
Tasks: Music Source Separation

Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIREX or SiSEC connected state-of-the-art models and corresponding papers, which can help researchers integrate the best practices into their models. In recent years, the widely used MUSDB18 dataset played an important role in measuring the performance of music source separation. While the dataset made a considerable contribution to the advancement of the field, it is also subject to several biases resulting from a focus on Western pop music and a limited number of mixing engineers being involved. To address these issues, we designed the Music Demixing (MDX) Challenge on a crowd-based machine learning competition platform where the task is to separate stereo songs into four instrument stems (Vocals, Drums, Bass, Other). The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i.e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers. In this paper, we provide the details of the datasets, baselines, evaluation metrics, evaluation results, and technical challenges for future competitions.
Addressing the IEEE AV Test Challenge with Scenic and VerifAI
Authors: Kesav Viswanadha, Francis Indaheng, Justin Wong, Edward Kim, Ellen Kalvan, Yash Pant, Daniel J. Fremont, Sanjit A. Seshia
Date: 2021-08-20 00:00:00
Tasks: Autonomous Vehicles, Probabilistic Programming

This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge. We demonstrate a systematic testing framework leveraging our previous work on formally-driven simulation for intelligent cyber-physical systems. First, to model and generate interactive scenarios involving multiple agents, we used Scenic, a probabilistic programming language for specifying scenarios. A Scenic program defines an abstract scenario as a distribution over configurations of physical objects and their behaviors over time. Sampling from an abstract scenario yields many different concrete scenarios which can be run as test cases for the AV. Starting from a Scenic program encoding an abstract driving scenario, we can use the VerifAI toolkit to search within the scenario for failure cases with respect to multiple AV evaluation metrics. We demonstrate the effectiveness of our testing framework by identifying concrete failure scenarios for an open-source autopilot, Apollo, starting from a variety of realistic traffic scenarios.
Solver-Free Heuristics to Retrieve Feasible Points for Offshore Wind Farm Collection System
Authors: Juan-Andrés Pérez-Rúa
Date: 2021-08-31 00:00:00

A set of solver-free heuristics for the offshore wind collection system problem are presented. Currently, methods of this type are not able to cope with typical constraints, and most of their variations minimize only for accumulated cable length. The first algorithm is a two-steps decision process, where the output is the design of a tree network satisfying cable thermal limits constraints, but vulnerable to violate planarity constraints. Subsequently, a cable crossings repair heuristic is introduced in order to fix infeasible points from the first heuristic. Finally, a refining heuristic (negative cycle cancelling refining heuristic) takes over to improve feasible points. The latter iteratively swaps cables, intending to find cycles with negative costs that will lead to investment savings. The sequence of heuristics supports the most important restrictions of the problem. The applicability of the workflow is empirically demonstrated by means of a set of large-scale real-world offshore wind farms. The numerical results indicate that: (i) feasible points can be retrieved in computing times in order of seconds, and (ii) warm-starting can help solvers to converge significantly faster for problems with solution time in order of several hours.
Harmonic Radar with Adaptively Phase-Coherent Auxiliary Transmitters
Authors: Anastasia Lavrenko, James K. Cavers, Graeme K. Woodward
Date: 2021-08-31 00:00:00
Tasks: TAG

In harmonic radar (HR), the radio frequency transmitter illuminates a nonlinear target (the tag), causing the return signal to consist of harmonics at multiples of the transmitted carrier frequency. Of them, the second harmonic is usually the strongest and the one to which the receiver is tuned. This frequency difference distinguishes the tag reflection from environmental clutter, which remains at the uplink (transmitter to tag) frequency. However, the passive nature of HR tags severely limits the reflected power, and therefore the range of the downlink (tag to receiver) path. We propose to increase the range and/or signal to noise ratio (SNR) by novel restructuring at the physical and signal levels. For this, we accompany the original transmitter with auxiliary transmitters able to send simple tones that are synchronized to arrive at the tag in phase, and we design the receiver to detect an intermodulation component. The resulting range and SNR are much greater than those of the original, conventional HR system, and greater even than if the original system were to transmit with power equal to the aggregate power of our new system. Achieving mutually coherent, i.e., in phase, arrival of the tones at the tag is the focus of the present paper. We provide a system framework that models the tag and the uplink and downlink, then present the adaptive phase coherence algorithm and analyze the probabilistic growth of the output signal power. We also account for the effects of frequency shifts due to transmitter mobility and the frequency offset errors in the transmitter local oscillators.
A Novel Robust Extended Dissipativity State Feedback Control system design for Interval Type-2 Fuzzy Takagi-Sugeno Large-Scale Systems
Authors: Mojtaba Asadi Jokar, Iman Zamani, Mohamad Manthouri, Mohammad Sarbaz
Date: 2021-08-31 00:00:00

In this paper, we use the advantage of large-scale systems modeling based on the type-2 fuzzy Takagi-Sugeno model to cover the uncertainties caused by large-scale systems modeling. The advantage of using membership function information is the reduction of conservatism resulting from stability analysis. Also, this paper uses the extended dissipativity robust control performance index to reduce the effect of external perturbations on the large-scale system, which is a generalization of H, L, passive, and dissipativity performance indexes and control gains can be achieved through solving linear matrix inequalities (LMIs), so the whole closed-loop fuzzy large-scale system is asymptotically stable.. Finally, the effectiveness of the proposed method is demonstrated by a numerical example of a double inverted pendulum system.
Decentralized Robust Interval Type-2 Fuzzy Model Predictive Control for Takagi-Sugeno Large-Scale Systems
Authors: Mohammad Sarbaz, Iman Zamani, Mohammad Manthouri, Asier Ibeas
Date: 2021-08-31 00:00:00
Tasks: Model Predictive Control

In this manuscript, decentralized robust interval type-2 fuzzy model predictive control for Takagi-Sugeno large-scale systems is studied. The mentioned large-scale system consists a number of interval type-2 (IT2) fuzzy Takagi-Sugeno (T-S) subsystems. An important matter and necessities that limit the practical application of model predictive control are the online computational cost and burden of the existence frameworks. For model predictive control of T-S fuzzy large-scale systems, the online computational burden is even worse and in some cases, they cannot be solved in an expected time. Especially for severe large-scale systems with disturbances, existing model predictive control of T-S fuzzy large-scale systems usually leads to a very conservative solution. So, researchers have many challenges and difficulties in finding a reasonable solution in a short time. Although, more relaxed results can be achieved by the proposed fuzzy model predictive control approach which adopts T-S large-scale systems with nonlinear subsystems, many restrictions are not considered in these approaches. In this paper, challenges are solved and the MPC is designed for a nonlinear IT2 fuzzy large-scale system with uncertainties and disturbances. Besides, online optimization problem is solved and results are proposed. Consequently, online computational cost of the optimization problem is reduced considerably. At the end, by two practical examples, the effectiveness of the proposed algorithm is illustrated.
Open-stub based spurious harmonic suppression method for Microstrip Coupled Line Filter
Authors: Mohammad Hossein Koohi Ghamsari, Erfan Azizkhani, Javad Ghalibafan
Date: 2021-08-31 00:00:00

In this paper, first, we review a straightforward analytical technique based on image impedance concept for designing traditional microwave microstrip coupled line filters using distributed elements. In the introduced approach, we characterize a quarter-wave coupled line section, and then these discrete sections can be connected in series to synthesis the final desired frequency response. Next, we use a novel open-stub based technique to suppress spurious harmonic frequencies. Finally, using proposed technique, we design and simulate a band pass filter (BPF). The simulation results prove the usefulness of the proposed technique.
Suboptimal nonlinear moving horizon estimation
Authors: Julian D. Schiller, Matthias A. Müller
Date: 2021-08-31 00:00:00
Tasks: Model Predictive Control

In this paper, we propose a suboptimal moving horizon estimator for a general class of nonlinear systems. For the stability analysis, we transfer the "feasibility-implies-stability/robustness" paradigm from model predictive control to the context of moving horizon estimation in the following sense: Using a suitably defined, feasible candidate solution based on an auxiliary observer, robust stability of the proposed suboptimal estimator is inherited independently of the horizon length and even if no optimization is performed. Moreover, the proposed design allows for the choice between two cost functions different in structure: the former in the manner of a standard least squares approach, which is typically used in practice, and the latter following a time-discounted modification, resulting in better theoretical guarantees. We apply the proposed suboptimal estimator to a nonlinear chemical reactor process, verify the theoretical assumptions, and show that even a few iterations of the optimizer are sufficient to significantly improve the estimation results of the auxiliary observer. Furthermore, we illustrate the flexibility of the proposed design by employing different solvers and compare the performance with two state-of-the-art fast MHE schemes from the literature.
In-body Bionanosensor Localization for Anomaly Detection via Inertial Positioning and THz Backscattering Communication
Authors: Jennifer Simonjan, Bige D. Unluturk, Ian F. Akyildiz
Date: 2021-08-31 00:00:00
Tasks: Anomaly Detection

Nanotechnology is enabling the development of a new generation of devices which are able to sense, process and communicate, while being in the scale of tens to hundreds of cubic nanometers. Such small, imperceptible devices enhance not only current applications but enable entirely new paradigms especially for in-body environments. This paper introduces a localization and tracking concept for bionanosensors floating in the human bloodstream to detect anomalies in the body. Besides the nanoscale sensors, the proposed system also comprises macroscale anchor nodes attached to the skin of the monitored person. To realize autonomous localization and resource-efficient wireless communication between sensors and anchors, we propose to exploit inertial positioning and sub-terahertz backscattering. The proposed system is a first step towards early disease detection as it aims at localizing body regions which show anomalies. Simulations are conducted to enable a systematical evaluation on the feasibility of the approach.
Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints
Authors: Yiyue Chen, Abolfazl Hashemi, Haris Vikalo
Date: 2020-05-27 00:00:00

We consider the problem of decentralized optimization where a collection of agents, each having access to a local cost function, communicate over a time-varying directed network and aim to minimize the sum of those functions. In practice, the amount of information that can be exchanged between the agents is limited due to communication constraints. We propose a communication-efficient algorithm for decentralized convex optimization that rely on sparsification of local updates exchanged between neighboring agents in the network. In directed networks, message sparsification alters column-stochasticity -- a property that plays an important role in establishing convergence of decentralized learning tasks. We propose a decentralized optimization scheme that relies on local modification of mixing matrices, and show that it achieves $\mathcal{O}(\frac{\mathrm{ln}T}{\sqrt{T}})$ convergence rate in the considered settings. Experiments validate theoretical results and demonstrate efficacy of the proposed algorithm.
Asynchronous and coherent dynamics in balanced excitatory-inhibitory spiking networks
Authors: Hongjie Bi, Matteo di Volo, Alessandro Torcini
Date: 2021-08-31 00:00:00

Dynamic excitatory-inhibitory (E-I) balance is a paradigmatic mechanism invoked to explain the irregular low firing activity observed in the cortex. However, we will show that the E-I balance can be at the origin of other regimes observable in the brain. The analysis is performed by combining simulations of sparse E-I networks composed of N spiking neurons with analytical investigations of low dimensional neural mass models. The bifurcation diagrams, derived for the neural mass model, allow to classify the asynchronous and coherent behaviours emerging any finite in-degree K. In the limit N >> K >> 1 both supra and sub-threshold balanced asynchronous regimes can be observed. Due to structural heterogeneity the asynchronous states are characterized by the splitting of the neurons in three groups: silent, fluctuation and mean driven. The coherent rhythms are characterized by regular or irregular temporal fluctuations joined to spatial coherence similar to coherent fluctuations observed in the cortex over multiple spatial scales. Collective Oscillations (COs) can emerge due to two different mechanisms. A first mechanism similar to the pyramidal-interneuron gamma (PING) one. The second mechanism is intimately related to the presence of current fluctuations, which sustain COs characterized by an essentially simultaneous bursting of the two populations. We observe period-doubling cascades involving the PING-like COs finally leading to the appearance of coherent chaos. For sufficiently strong current fluctuations we report a novel mechanism of frequency locking among collective rhythms promoted by these intrinsic fluctuations. Our analysis suggest that despite PING-like or fluctuation driven COS are observable for any finite in-degree K, in the limit N >> K >> 1 these solutions result in two coexisting balanced regimes: an asynchronous and a fully synchronized one.
The End Restraint Method for Mechanically Perturbing Nucleic Acids in silico
Authors: Jack W Shepherd, Mark C Leake
Date: 2021-08-31 00:00:00
Tasks: Super-Resolution

Far from being a passive information store, the genome is a mechanically dynamic and diverse system in which torsion and tension fluctuate and combine to determine structure and help regulate gene expression. Much of this mechanical perturbation is due to molecular machines such as topoisomerases which must stretch and twist DNA as part of various functions including DNA repair and replication. While the broad-scale mechanical response of nucleic acids to tension and torsion is well characterized, detail at the single base pair level is beyond the limits of even super-resolution imaging. Here, we present a straightforward, flexible, and extensible umbrella-sampling protocol to twist and stretch nucleic acids in silico using the popular biomolecular simulation package Amber -- though the principles we describe are applicable also to other packages such as GROMACS. We discuss how to set up the simulation system, decide forcefields and solvation models, and equilibrate. We then introduce the torsionally-constrained stretching protocol, and finally we present some analysis techniques we have used to characterize structural motif formation. Rather than define forces or fictional pseudoatoms, we instead define a fixed translation of specified atoms between each umbrella sampling step, which allows comparison with experiment without needing to estimate applied forces by simply using the fractional end-to-end displacement as a comparison metric. We hope that this easy to implement solution will be valuable for interrogating optical and magnetic tweezers data on nucleic acids at base pair resolution.
Single-molecular quantification of flowering control proteins within nuclear condensates in live whole Arabidopsis root
Authors: Alex L. Payne-Dwyer, Mark C. Leake
Date: 2021-08-31 00:00:00

Here we describe the coupled standardisation of two complementary fluorescence imaging techniques and apply it to liquid-liquid phase separated condensates formed from an EGFP fluorescent reporter of Flowering Control Locus A (FCA), a protein that associates with chromosomal DNA in plants during epigenetic regulation of the flowering process. First, we use home-built single-molecule Slimfield microscopy to establish a fluorescent protein standard. This sample comprises live yeast cells expressing Mig1 protein, a metabolic regulator which localises to the nucleus under conditions of high glucose, fused to the same type of EGFP label as for the FCA fusion construct. Then we employ commercial confocal AiryScan microscopy to study the same standard. Finally, we demonstrate how to quantify FCA-EGFP nuclear condensates in intact root tips at rapid timescales and apply this calibration. This method is a valuable approach to obtaining single-molecule precise stoichiometry and copy number estimates of protein condensates that are integrated into the chromosome architecture of plants, using confocal instrumentation that lacks de facto single-molecule detection sensitivity.