pwc-archive/papers-with-abstracts huggingface dataset:
Reservoir Computers Modal Decomposition and Optimization
Authors: Chad Nathe, Enrico Del Frate, Thomas Carroll, Louis Pecora, Afroza Shirin, Francesco Sorrentino
Date: 2021-01-13 00:00:00

The topology of a network associated with a reservoir computer is often taken so that the connectivity and the weights are chosen randomly. Optimization is hardly considered as the parameter space is typically too large. Here we investigate this problem for a class of reservoir computers for which we obtain a decomposition of the reservoir dynamics into modes, which can be computed independently of one another. Each mode depends on an eigenvalue of the network adjacency matrix. We then take a parametric approach in which the eigenvalues are parameters that can be appropriately designed and optimized. In addition, we introduce the application of a time shift to each individual mode. We show that manipulations of the individual modes, either in terms of the eigenvalues or the time shifts, can lead to dramatic reductions in the training error.
Holonic Multiagent Simulation of Complex Adaptive Systems
Authors: Rafik Hadfi, Takayuki Ito
Date: 2016-06-01 00:00:00

We propose a holonic multiagent simulator that can simulate any complex urban environment. We focus on traffic simulation within any geographic area on earth, subject to any weather conditions. We adopt an agent-based approach for the different beahviors of the vehicles, drivers, and pedestrians. The proposed driving behavioral models can realistically emulate driving behaviors of humans. The resulting simulator can handle all the complexities of such environments in accordance with the laws of physics.
Traffic Simulation in Urban Networks Using Stochastic Cell Transmission Model
Authors: Rafik Hadfi, Sho Tokuda, Takayuki Ito
Date: 2015-10-15 00:00:00

The stochastic cell transmission model (SCTM) is a macroscopic traffic simulation model of high accuracy. One of its advantages is that it can represent the uncertainty of the traffic states and the changing travel demand and supply conditions. However, it has been applied to freeways and simple networks having only one origin‐destination pair. In this article, we propose a modified stochastic cell transmission model (M‐SCTM) that applies the conventional SCTM to urban networks. In M‐SCTM, we introduce vehicle agents as well as their route choice behavior on an urban network, which is more applicable to complex urban networks. Additionally, M‐SCTM was applied to networks in which the turning ratio is not priorly set through a route search algorithm. The results show that M‐SCTM can conduct simulations with as much accuracy as SCTM. Furthermore, we verified the appropriate reproducibility of the simulations based on M‐SCTM and compared the estimated value and the measured value in terms of the travel time of each vehicle.
Complex Multi-Issue Negotiation Using Utility Hyper-Graphs
Authors: Rafik Hadfi, Takayuki Ito
Date: 2015-07-01 00:00:00

We propose to handle the complexity of utility spaces used in multi-issue negotiation by adopting a new representation that allows a modular decomposition of the issues and the constraints. This is based on the idea that a constraint-based utility space is nonlinear with respect to issues, but linear with respect to the constraints. This allows us to rigorously map the utility space into an issue-constraint hyper-graph. Exploring the utility space reduces then to a message passing mechanism along the hyper-edges of the hyper-graph by means of utility propagation. Optimal contracts are found efficiently using a variation of the Max-Sum algorithm. We evaluate the model experimentally using parameterized nonlinear utility spaces, showing that it can handle a large family of complex utility spaces by finding optimal contracts, outperforming previous sampling-based approaches. We also evaluate the model in a negotiation setting. We show that under high complexity, social welfare could be greater than the sum of the individual agents' best utilities.
Approximating Constraint-Based Utility Spaces Using Generalized Gaussian Mixture Models
Authors: Rafik Hadfi, Takayuki Ito
Date: 2014-12-01 00:00:00
Tasks: Form

Complex negotiations are characterized by a particular type of utility spaces that is usually non-linear and non-monotonic. An example of such utility spaces are constraint-based utility spaces. The multitude of constraints’ shapes that could potentially be used by the negotiating agents makes any opponent modeling attempt more challenging. The same problem persists even when the agent is exploring her own utility space as to find her optimal contracts. Seeking a unified form for constraint-based utility representation might shed some light on how to tackle these problems. In this paper, we propose to find an approximation for constraint-based preferences, used mainly in complex negotiation with non-linear utility spaces. The proposed approximation yields a compact form that unifies a whole family of constraints (Cubic, Bell, Conic, etc.). Results show that the new canonical form can in fact be an alternative representation for all known constraint-based utility functions. Additionally, it leads us to a potential parametric model that could be used for opponent modeling in complex non-linear negotiations.
A Zonal Volt/VAR Control Mechanism for High PV Penetration Distribution Systems
Authors: Asmaa Alrushoud, Catie McEntee, Ning Lu
Date: 2020-12-31 00:00:00
Tasks: Clustering

This paper presents a zonal Volt/VAR control scheme that coordinates Photovoltaic (PV) inverters for providing voltage regulation on 3-phase unbalanced distribution feeders. Voltage sensitivity studies are conducted to uncover the dependency between nodal voltage changes and the reactive power injections at nodes with smart PV inverters. A fast incremental clustering method is developed to divide the distribution feeder into weakly coupled zones based on correlations of nodal voltage sensitivities. Because each zone is weakly coupled, voltage of each zone can be controlled independently. Thus, in each zone, a rule-based voltage controller will dispatch PV smart inverters to provide reactive power control for correcting the over/under voltages. An actual distribution feeder in North Carolina is used as a test bed. Simulation results show that the proposed zonal based Volt/VAR control mechanism can maintain the voltage in the distribution system within limits and solves faster than the centralized controller.
A tool for user friendly, cloud based, whole slide image segmentation
Authors: Brendon Lutnick, David Manthey, Pinaki Sarder
Date: 2021-01-18 00:00:00
Tasks: Image Segmentation, Management, Segmentation, Semantic Segmentation, whole slide images

Convolutional neural networks, the state of the art for image segmentation, have been successfully applied to histology images by many computational researchers. However, the translatability of this technology to clinicians and biological researchers is limited due to the complex and undeveloped user interface of the code, as well as the extensive computer setup required. As an extension of our previous work (arXiv:1812.07509), we have developed a tool for segmentation of whole slide images (WSIs) with an easy to use graphical user interface. Our tool runs a state-of-the-art convolutional neural network for segmentation of WSIs in the cloud. Our plugin is built on the open source tool HistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data management and viewing abilities for WSI datasets. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Users can easily upload slides to a server where our plugin is installed and perform human in the loop segmentation analysis remotely. This tool is open source, and has the ability to be adapted to segment of any pathological structure. For a proof of concept, we have trained it to segment glomeruli from renal tissue images, achieving an F-score > 0.97 on holdout tissue slides.
Scalability in nonlinear network systems affected by delays and disturbances
Authors: Shihao Xie, Giovanni Russo, Richard Middleton
Date: 2020-06-12 00:00:00

This paper is concerned with the study of scalability in nonlinear heterogeneous networks affected by communication delays and disturbances. After formalizing the notion of scalability, we give two sufficient conditions to assess this property. Our results can be used to study leader-follower and leaderless networks and also allow to consider the case when the desired configuration of the system changes over time. We show how our conditions can be turned into design guidelines to guarantee scalability and illustrate their effectiveness via numerical examples.
Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications
Authors: Max Cohen, Calin Belta
Date: 2021-01-18 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this paper we study the problem of synthesizing optimal control policies for uncertain continuous-time nonlinear systems from syntactically co-safe linear temporal logic (scLTL) formulas. We formulate this problem as a sequence of reach-avoid optimal control sub-problems. We show that the resulting hybrid optimal control policy guarantees the satisfaction of a given scLTL formula by constructing a barrier certificate. Since solving each optimal control problem may be computationally intractable, we take a learning-based approach to approximately solve this sequence of optimal control problems online without requiring full knowledge of the system dynamics. Using Lyapunov-based tools, we develop sufficient conditions under which our approximate solution maintains correctness. Finally, we demonstrate the efficacy of the developed method with a numerical example.
Graph-based Matched Field Localization for an Underwater Source
Authors: Peng Xiao, Lingji Xu, Liya Xu, Jianmin Yang, Qing Hu
Date: 2021-01-18 00:00:00

Matched Field Processing (MFP) locates the underwater sources by matching the received data with the replica vectors, which could be regarded as a generalized beamformer. In this paper, the MFP method is combined with a recently developed framework -- Graph Signal Processing (GSP) method. Following the paradigm of GSP, a spatial adjacency matrix is constructed for the arbitrary distributed sensors based on the Green's function, then the source is located by utilizing the graph Fourier transform. The simulation results illustrate that the Graph-based MFP outperforms the the conventional MFP processors -- the Bartlett processor and the Minimum Variance processor -- for its good accuracy and robustness.
Microfluidic production of porous polymer cell-mimics capable of gene expression
Authors: Imre Banlaki, Francois-Xavier Lehr, Henrike Niederholtmeyer
Date: 2021-01-18 00:00:00

Engineering simple, artificial models of living cells allows synthetic biologists to study cellular functions under well-controlled conditions. Reconstituting multicellular behaviors with synthetic cell-mimics is still a challenge because it requires efficient communication between individual compartments in large populations. This protocol presents a microfluidic method to produce large quantities of cell-mimics with highly porous, stable and chemically modifiable polymer membranes that can be programmed on demand with nucleus-like DNA-hydrogel compartments for gene expression. We describe expression of genes encoded in the hydrogel compartment and communication between neighboring cell-mimics through diffusive protein signals.
SPRISS: Approximating Frequent $k$-mers by Sampling Reads, and Applications
Authors: Diego Santoro, Leonardo Pellegrina, Fabio Vandin
Date: 2021-01-18 00:00:00

The extraction of $k$-mers is a fundamental component in many complex analyses of large next-generation sequencing datasets, including reads classification in genomics and the characterization of RNA-seq datasets. The extraction of all $k$-mers and their frequencies is extremely demanding in terms of running time and memory, owing to the size of the data and to the exponential number of $k$-mers to be considered. However, in several applications, only frequent $k$-mers, which are $k$-mers appearing in a relatively high proportion of the data, are required by the analysis. In this work we present SPRISS, a new efficient algorithm to approximate frequent $k$-mers and their frequencies in next-generation sequencing data. SPRISS employs a simple yet powerful reads sampling scheme, which allows to extract a representative subset of the dataset that can be used, in combination with any $k$-mer counting algorithm, to perform downstream analyses in a fraction of the time required by the analysis of the whole data, while obtaining comparable answers. Our extensive experimental evaluation demonstrates the efficiency and accuracy of SPRISS in approximating frequent $k$-mers, and shows that it can be used in various scenarios, such as the comparison of metagenomic datasets and the identification of discriminative $k$-mers, to extract insights in a fraction of the time required by the analysis of the whole dataset.
Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION
Authors: Martina Mammarella, Victor Mirasierra, Matthias Lorenzen, Teodoro Alamo, Fabrizio Dabbene
Date: 2021-01-15 00:00:00
Tasks: Model Predictive Control

In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of the approximating set, by defining families of simple-approximating sets of given complexity. A probabilistic scaling procedure then allows to rescale these sets to obtain the desired probabilistic guarantees. The proposed approach is shown to be applicable in several problem in systems and control, such as the design of Stochastic Model Predictive Control schemes or the solution of probabilistic set membership estimation problems.
Beating the Market with Generalized Generating Portfolios
Authors: Patrick Mijatovic
Date: 2021-01-18 00:00:00

Stochastic portfolio theory aims at finding relative arbitrages, i.e. trading strategies which outperform the market with probability one. Functionally generated portfolios, which are deterministic functions of the market weights, are an invaluable tool in doing so. Driven by a practitioner point of view, where investment decisions are based upon consideration of various financial variables, we generalize functionally generated portfolios and allow them to depend on continuous-path semimartingales, in addition to the market weights. By means of examples we demonstrate how the inclusion of additional processes can reduce time horizons beyond which relative arbitrage is possible, boost performance of generated portfolios, and how investor preferences and specific investment views can be included in the context of stochastic portfolio theory. Striking is also the construction of a relative arbitrage opportunity which is generated by the volatility of the additional semimartingale. An in-depth empirical analysis of the performance of the proposed strategies confirms our theoretical findings and demonstrates that our portfolios represent profitable investment opportunities even in the presence of transaction costs.
Some paradoxes in the Tilman model, how to avoid or accept them
Authors: Joost H. J. van Opheusden, Lia Hemerik
Date: 2021-01-18 00:00:00

In making models for biological systems one expects to grasp the biology and be able to use ones intuition to predict the outcome. This paper is about the discrepancy between what is expected and what is the outcome of the analysis of the model, a paradox. The Tilman model of consumers competing for resources has some aspects that may appear counterintuitive. Within the standard Tilman model for a single consumer and a single resource, when a very efficient consumer rapidly eats all available food, the resource density becomes zero, but if there is no food, how can the consumer survive? The paradox can be resolved by realising that in the short term indeed rapid consumption may lead to starvation and a decline in the consumer population, but in the long term a small resource and consumer density remain. A single consumer living on two essential nutrients leaves the density of the non-limiting nutrient above its critical level, so what is done with the extra food? We explain that the extra food is not consumed, just because it is available, but that in fact the additional food is not consumed at all, because the consumer has no use for it. For a model with two consumer species competing for a single nutrient one of the consumers will eventually disappear, even if the food economics of the surviving one is worse and there is still much food wasted. Both are inherent aspects of the model, and the paradox can be avoided if the difference between the consumer species is small, in which case it will take very long to reach equilibrium. We argue that in general an extended stability analysis, in which not only the asymptotically stable state is considered, but also the unstable steady states and all time scales involved in the transient dynamics, gives more insight in the dynamics involved and thus can help in avoiding apparent contradictions in ecological models, or accepting them.
Automated Diagnosis of Intestinal Parasites: A new hybrid approach and its benefits
Authors: D. Osaku, C. F. Cuba, Celso T. N. Suzuki, J. F. Gomes, A. X. Falcão
Date: 2021-01-07 00:00:00
Tasks: Decision Making

Intestinal parasites are responsible for several diseases in human beings. In order to eliminate the error-prone visual analysis of optical microscopy slides, we have investigated automated, fast, and low-cost systems for the diagnosis of human intestinal parasites. In this work, we present a hybrid approach that combines the opinion of two decision-making systems with complementary properties: ($DS_1$) a simpler system based on very fast handcrafted image feature extraction and support vector machine classification and ($DS_2$) a more complex system based on a deep neural network, Vgg-16, for image feature extraction and classification. $DS_1$ is much faster than $DS_2$, but it is less accurate than $DS_2$. Fortunately, the errors of $DS_1$ are not the same of $DS_2$. During training, we use a validation set to learn the probabilities of misclassification by $DS_1$ on each class based on its confidence values. When $DS_1$ quickly classifies all images from a microscopy slide, the method selects a number of images with higher chances of misclassification for characterization and reclassification by $DS_2$. Our hybrid system can improve the overall effectiveness without compromising efficiency, being suitable for the clinical routine -- a strategy that might be suitable for other real applications. As demonstrated on large datasets, the proposed system can achieve, on average, 94.9%, 87.8%, and 92.5% of Cohen's Kappa on helminth eggs, helminth larvae, and protozoa cysts, respectively.
DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems
Authors: Craig R. Gin, Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz
Date: 2020-12-31 00:00:00

Boundary value problems (BVPs) play a central role in the mathematical analysis of constrained physical systems subjected to external forces. Consequently, BVPs frequently emerge in nearly every engineering discipline and span problem domains including fluid mechanics, electromagnetics, quantum mechanics, and elasticity. The fundamental solution, or Green's function, is a leading method for solving linear BVPs that enables facile computation of new solutions to systems under any external forcing. However, fundamental Green's function solutions for nonlinear BVPs are not feasible since linear superposition no longer holds. In this work, we propose a flexible deep learning approach to solve nonlinear BVPs using a dual-autoencoder architecture. The autoencoders discover an invertible coordinate transform that linearizes the nonlinear BVP and identifies both a linear operator $L$ and Green's function $G$ which can be used to solve new nonlinear BVPs. We find that the method succeeds on a variety of nonlinear systems including nonlinear Helmholtz and Sturm--Liouville problems, nonlinear elasticity, and a 2D nonlinear Poisson equation. The method merges the strengths of the universal approximation capabilities of deep learning with the physics knowledge of Green's functions to yield a flexible tool for identifying fundamental solutions to a variety of nonlinear systems.
RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates
Authors: Avory Bryant, Krzysztof Cios
Date: 2017-12-27 00:00:00
Tasks: 3D Multi-Person Pose Estimation (absolute), Clustering

A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse nearest neighbor counts as an estimate of observation density. Clustering is performed using a DBSCAN-like approach based on k nearest neighbor graph traversals through dense observations. RNN-DBSCAN is preferable to the popular density-based clustering algorithm DBSCAN in two aspects. First, problem complexity is reduced to the use of a single parameter (choice of k nearest neighbors), and second, an improved ability for handling large variations in cluster density (heterogeneous density). The superiority of RNN-DBSCAN is demonstrated on several artificial and real-world datasets with respect to prior work on reverse nearest neighbor based clustering approaches (RECORD, IS-DBSCAN, and ISB-DBSCAN) along with DBSCAN and OPTICS. Each of these clustering approaches is described by a common graph-based interpretation wherein clusters of dense observations are defined as connected components, along with a discussion on their computational complexity. Heuristics for RNN-DBSCAN parameter selection are presented, and the effects of k on RNN-DBSCAN clusterings discussed. Additionally, with respect to scalability, an approximate version of RNN-DBSCAN is presented leveraging an existing approximate k nearest neighbor technique.
Analysis of misidentifications in TEM characterization of organic‐inorganic hybrid perovskite material
Authors: Yu‐Hao Deng, Leon Georg Nest
Date: 2021-01-13 00:00:00

Organic‐inorganic hybrid perovskites (OIHPs) have recently emerged as groundbreaking semiconductor materials owing to their remarkable properties. Transmission electron microscopy (TEM), as a very powerful characterization tool, has been widely used in perovskite materials for structural analysis and phase identification. However, the perovskites are highly sensitive to electron beams and easily decompose into PbX2 (X = I, Br, Cl) and metallic Pb. The electron dose of general high‐resolution TEM is much higher than the critical dose of MAPbI3, which results in universal misidentifications that PbI2 and Pb are incorrectly labeled as perovskite. The widely existed mistakes have negatively affected the development of perovskite research fields. Here misidentifications of the best‐known MAPbI3 perovskite are summarized and corrected, then the causes of mistakes are classified and ascertained. Above all, a solid method for phase identification and practical strategies to reduce the radiation damage for perovskite materials have also been proposed. This review aims to provide the causes of mistakes and avoid misinterpretations in perovskite research fields in the future.