pwc-archive/papers-with-abstracts huggingface dataset:
Structural reduction of chemical reaction networks based on topology
Authors: Yuji Hirono, Takashi Okada, Hiroyasu Miyazaki, Yoshimasa Hidaka
Date: 2021-02-15 00:00:00

We develop a model-independent reduction method of chemical reaction systems based on the stoichiometry, which determines their network topology. A subnetwork can be eliminated systematically to give a reduced system with fewer degrees of freedom. This subnetwork removal is accompanied by rewiring of the network, which is prescribed by the Schur complement of the stoichiometric matrix. Using homology and cohomology groups to characterize the topology of chemical reaction networks, we can track the changes of the network topology induced by the reduction through the changes in those groups. We prove that, when certain topological conditions are met, the steady-state chemical concentrations and reaction rates of the reduced system are ensured to be the same as those of the original system. This result holds regardless of the modeling of the reactions, namely chemical kinetics, since the conditions only involve topological information. This is advantageous because the details of reaction kinetics and parameter values are difficult to identify in many practical situations. The method allows us to reduce a reaction network while preserving its original steady-state properties, thereby complex reaction systems can be studied efficiently. We demonstrate the reduction method in hypothetical networks and the central carbon metabolism of Escherichia coli.
Pricing FX Options under Intermediate Currency
Authors: S. Maurer, T. E. Sharp, M. V. Tretyakov
Date: 2019-12-03 00:00:00

We suggest an intermediate currency approach that allows us to price options on all FX markets simultaneously under the same risk-neutral measure which ensures consistency of FX option prices across all markets. In particular, it is sufficient to calibrate a model to the volatility smile on the domestic market as, due to the consistency of pricing formulas, the model automatically reproduces the correct smile for the inverse pair (the foreign market). We first consider the case of two currencies and then the multi-currency setting. We illustrate the intermediate currency approach by applying it to the Heston and SABR stochastic volatility models, to the model in which exchange rates are described by an extended skewed normal distribution, and also to the model-free approach of option pricing
Multistep Frequency Response Optimized Integrators and Their Application to Accelerating a Power System Transient Simulation Scheme
Authors: Sheng Lei, Alexander Flueck
Date: 2020-11-02 00:00:00
Tasks: Prediction

This paper proposes several explicit and implicit multistep frequency response optimized integrators considering first or second order derivative. A prediction-based method aiming at accelerating a novel power system transient simulation scheme without impacting its accuracy is further put forward utilizing the proposed numerical integrators and some others available in the literature. Case studies verify the effectiveness of the proposed prediction method. Although they are utilized to accelerate the simulation scheme in this paper, the proposed numerical integrators are in fact general-purpose and can be applied to other areas.
Livelayer: A Semi-Automatic Software Program for Segmentation of Layers and Diabetic Macular Edema in Optical Coherence Tomography Images
Authors: Mansooreh Montazerin, Zahra Sajjadifar, Elias Khalili Pour, Hamid Riazi-Esfahani, Tahereh Mahmoudi, Hossein Rabbani, Hossein Movahedian, Alireza Dehghani, Mohammadreza Akhlaghi, Rahele Kafieh
Date: 2020-03-12 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Given the capacity of Optical Coherence Tomography (OCT) imaging to display symptoms of a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called ''Livelayer'' which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra's Shortest Path (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of ILM, IPL-INL, OPL-ONL was much less than that for the manual segmentation, 5s for the ILM (minimum) and 15.57s for the OPL-ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL-INL, OPL-ONL, respectively. The Bland-Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL-ONL and < 1 for the other two. The dice scores for comparing the two algorithms and for obtaining the repeatability on segmentation of fluid objects were at acceptable levels.
Assessment of a failure prediction model in the energy sector: a multicriteria discrimination approach with Promethee based classification
Authors: Silvia Angilella, Maria Rosaria Pappalardo
Date: 2021-02-15 00:00:00
Tasks: General Classification

This study presents the implementation of a non-parametric multiple criteria decision aiding (MCDA) model, the Multi-group Hierarchy Discrimination (M.H.DIS) model, with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), on a dataset of 114 European unlisted companies operating in the energy sector. Firstly, the M.H.DIS model has been developed following a five-fold cross validation procedure to analyze whether the model explains and replicates a two-group pre-defined classification of companies in the considered sample, provided by Bureau van Dijk's Amadeus database. Since the M.H.DIS method achieves a quite limited satisfactory accuracy in predicting the considered Amadeus classification in the holdout sample, the PROMETHEE method has been performed then to provide a benchmark sorting procedure useful for comparison purposes.
Subspace Decomposition for Graphon LQR: Applications to VLSNs of Harmonic Oscillators
Authors: Shuang Gao, Peter E. Caines
Date: 2020-04-01 00:00:00

Graphon control has been proposed and developed in [1]-[3] to approximately solve control problems for very large-scale networks (VLSNs) of linear dynamical systems based on graphon limits. This paper provides a solution method based on invariant subspace decompositions for a class of graphon linear quadratic regulation (LQR) problems where the local dynamics share homogeneous parameters but the graphon couplings may be heterogeneous among the coupled agents. Graphon couplings in this paper appear in states, controls and costs, and these couplings may be represented by different graphons. By exploring a common invariant subspace of the couplings, the original problem is decomposed into a network coupled LQR problem of finite dimension and a decoupled infinite dimensional LQR problem. A centralized optimal solution, and a nodal collaborative optimal control solution where each agent computes its part of the optimal solution locally, are established. The application of these solutions to finite network LQR problems may be via (i) the graphon control methodology [3], or (ii) the representation of finite LQR problems as special cases of graphon LQR problems. The complexity of these solutions involves solving one nd X nd dimensional Riccati equation and one n X n Riccati equation, where n is the dimension of each nodal agent state and d is the dimension of the nontrivial common invariant subspace of the coupling operators, whereas a direct approach involves solving an nN X nN dimensional Riccati equation, where N is the size of the network. For situations where the graphon couplings do not admit exact low-rank representations, approximate control is developed based on low-rank approximations. Finally, an application to the regulation of harmonic oscillators coupled over large networks with uncertainties is demonstrated.
Impact of EV Charging Stations in Power Grids in Italy and its Mitigation Mechanisms
Authors: A. Samson Mogos, Samuele Grillo
Date: 2021-02-15 00:00:00

Global warming leads the world to think of a different way of transportation: avoiding internal combustion engines and electrifying the transportation sector. With a high penetration of electric vehicle (EV) charging stations on an existing power distribution network, the impact may be consistent. The loads of the fast-charging stations would potentially result in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. The degrading performance of the power system due to the negative impact of the EV charging stations can even lead to penalties to be paid by the distribution system operator (DSO). This paper: i) investigates the impact of the \ac{ev} charging station on the distribution network for what concerns voltage drop on MV feeders and loading of transformers in primary substations, and ii) proposes a mitigation mechanism. A realistic typical Italian grid has been used to assess the impact of EV charging stations and to validate the mitigation mechanism.
Application of Quantitative Systems Pharmacology to guide the optimal dosing of COVID-19 vaccines
Authors: Mario Giorgi, Rajat Desikan, Piet H. van der Graaf, Andrzej M. Kierzek
Date: 2021-02-15 00:00:00

Optimal use and distribution of Covid-19 vaccines involves adjustments of dosing. Due to the rapidly-evolving pandemic, such adjustments often need to be introduced before full efficacy data are available. As demonstrated in other areas of drug development, quantitative systems pharmacology (QSP) is well placed to guide such extrapolation in a rational and timely manner. Here we propose for the first time how QSP can be applied real time in the context of COVID-19 vaccine development.
Detect caterpillar, grasshopper, aphid and simulation program for neutralizing them by laser Ildar Rakhmatulin DOI: 10.21203/rs.3.rs-242641/v1 Download PDF LICENSE: This work is licensed under a CC BY 4.0 License. Read Full License DECLARATIONS: View author declarations. Abstract The protection of crops from pests is relevant for any cultivated crop. But modern methods of pest control by pesticides carry many dangers for humans. Therefore, research into the development of safe and effective pest control methods is promising. This manuscript presents a new method of pest control. We used neural networks for pest detection and developed a powerful laser device (5 W) for their neutralization. In the manuscript methods of processing images with pests to extract the most useful feature are described in detail. Using the following pets as an example: aphids, grasshopper, cabbage caterpillar, we analyzed various neural network models and selected the optimal models and characteristics for each insect. In the paper
Authors: Ildar Rakhmatulin
Date: 2021-02-16 00:00:00

The protection of crops from pests is relevant for any cultivated crop. But modern methods of pest control by pesticides carry many dangers for humans. Therefore, research into the development of safe and effective pest control methods is promising. This manuscript presents a new method of pest control. We used neural networks for pest detection and developed a powerful laser device (5 W) for their neutralization. In the manuscript methods of processing images with pests to extract the most useful feature are described in detail. Using the following pets as an example: aphids, grasshopper, cabbage caterpillar, we analyzed various neural network models and selected the optimal models and characteristics for each insect. In the paper the principle of operation of the developed laser device is described in detail. We created the program to search a pest in the video stream calculation of their coordinates and transmission data with coordinates to the device with the laser.
Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing
Date: 2020-06-05 00:00:00
Tasks: Drug Discovery

Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Authors: Yundong Zhang, Huiye Liu, Qiang Hu
Date: 2021-02-16 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Medical image segmentation - the prerequisite of numerous clinical needs - has been significantly prospered by recent advances in convolutional neural networks (CNNs). However, it exhibits general limitations on modeling explicit long-range relation, and existing cures, resorting to building deep encoders along with aggressive downsampling operations, leads to redundant deepened networks and loss of localized details. Hence, the segmentation task awaits a better solution to improve the efficiency of modeling global contexts while maintaining a strong grasp of low-level details. In this paper, we propose a novel parallel-in-branch architecture, TransFuse, to address this challenge. TransFuse combines Transformers and CNNs in a parallel style, where both global dependency and low-level spatial details can be efficiently captured in a much shallower manner. Besides, a novel fusion technique - BiFusion module is created to efficiently fuse the multi-level features from both branches. Extensive experiments demonstrate that TransFuse achieves the newest state-of-the-art results on both 2D and 3D medical image sets including polyp, skin lesion, hip, and prostate segmentation, with significant parameter decrease and inference speed improvement.
GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
Authors: Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, Xiao Liu
Date: 2021-02-16 00:00:00
Tasks: Benchmarking

Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software engineering including program analysis, type inference, and code representation. In this paper, we introduce GraphGallery, a platform for fast benchmarking and easy development of GNNs based software. GraphGallery is an easy-to-use platform that allows developers to automatically deploy GNNs even with less domain-specific knowledge. It offers a set of implementations of common GNN models based on mainstream deep learning frameworks. In addition, existing GNNs toolboxes such as PyG and DGL can be easily incorporated into the platform. Experiments demonstrate the reliability of implementations and superiority in fast coding. The official source code of GraphGallery is available at https://github.com/EdisonLeeeee/GraphGallery and a demo video can be found at https://youtu.be/mv7Zs1YeaYo.
Supervised learning is an accurate method for network-based gene classification
Authors: Renming Liu, Christopher A Mancuso, Anna Yannakopoulos, Kayla A Johnson, Arjun Krishnan
Date: 2020-06-01 00:00:00
Tasks: Benchmarking, General Classification

Background: Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem.

Results: In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene's full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation's appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows.
PecanPy: A parallelized, efficient, and accelerated node2vec in Python
Authors: Renming Liu, Arjun Krishnan
Date: 2020-07-23 00:00:00
Tasks: BIG-bench Machine Learning

Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. Node2vec is the most widely used method for node embedding. However, its original Python and C++ implementations scale poorly with network density, failing for dense biological networks with hundreds of millions of edges. We have developed PecanPy, a new Python implementation of node2vec that uses cache-optimized compact graph data structures and precomputing/parallelization to result in fast, high-quality node embeddings for biological networks of all sizes and densities. PecanPy software and documentation are available at https://github.com/krishnanlab/pecanpy.
Design a Technology Based on the Fusion of Genetic Algorithm, Neural network and Fuzzy logic
Authors: Raid R. Al-Nima, Fawaz S. Abdullah, Ali N. Hamoodi
Date: 2021-02-16 00:00:00

This paper describes the design and development of a prototype technique for artificial intelligence based on the fusion of genetic algorithm, neural network and fuzzy logic. It starts by establishing a relationship between the neural network and fuzzy logic. Then, it combines the genetic algorithm with them. Information fusions are at the confidence level, where matching scores can be reported and discussed. The technique is called the Genetic Neuro-Fuzzy (GNF). It can be used for high accuracy real-time environments.
Machine learning for signature verification
Authors: Harish Srinivasan, Sargur N Srihari, Matthew J Beal
Date: 2006-12-15 00:00:00
Tasks: BIG-bench Machine Learning

Signature verification is a common task in forensic document analysis. It is one of determining whether a questioned signature matches known signature samples. From the viewpoint of automating the task it can be viewed as one that involves machine learning from a population of signatures. There are two types of learning to be accomplished. In the first, the training set consists of genuines and forgeries from a general population. In the second there are genuine signatures in a given case. The two learning tasks are called person-independent (or general) learning and person-dependent (or special) learning. General learning is from a population of genuine and forged signatures of several individuals, where the differences between genuines and forgeries across all individuals are learnt. The general learning model allows a questioned signature to be compared to a single genuine signature. In special learning, a person's signature is learnt from multiple samples of only that person's signature– where within-person similarities are learnt. When a sufficient number of samples are available, special learning performs better than general learning (5% higher accuracy). With special learning, verification accuracy increases with the number of samples.
Neural Posterior Regularization for Likelihood-Free Inference
Authors: Dongjun Kim, Kyungwoo Song, Seungjae Shin, Wanmo Kang, Il-Chul Moon, Weonyoung Joo
Date: 2021-02-15 00:00:00
Tasks: Bayesian Inference

A simulation is useful when the phenomenon of interest is either expensive to regenerate or irreproducible with the same context. Recently, Bayesian inference on the distribution of the simulation input parameter has been implemented sequentially to minimize the required simulation budget for the task of simulation validation to the real-world. However, the Bayesian inference is still challenging when the ground-truth posterior is multi-modal with a high-dimensional simulation output. This paper introduces a regularization technique, namely Neural Posterior Regularization (NPR), which enforces the model to explore the input parameter space effectively. Afterward, we provide the closed-form solution of the regularized optimization that enables analyzing the effect of the regularization. We empirically validate that NPR attains the statistically significant gain on benchmark performances for diverse simulation tasks.
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Authors: Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet
Date: 2021-02-15 00:00:00
Tasks: State Space Models, Variational Inference

Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models. Resampling is a key ingredient of PF, necessary to obtain low variance likelihood and states estimates. However, traditional resampling methods result in PF-based loss functions being non-differentiable with respect to model and PF parameters. In a variational inference context, resampling also yields high variance gradient estimates of the PF-based evidence lower bound. By leveraging optimal transport ideas, we introduce a principled differentiable particle filter and provide convergence results. We demonstrate this novel method on a variety of applications.