pwc-archive/papers-with-abstracts huggingface dataset:
Comparison of inverse problem linear and non-linear methods for localization source: a combined TMS-EEG study
Authors: Ridha jarray, Abir Hadriche, Cokri ben Amar, Nawel Jmail
Date: 2021-11-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The Electro-Encephalo-Graphy (EEG) technique consists of estimating the cortical distribution of signals over time of electrical activity and also of locating the zones of primary sensory projection. Moreover, it is able to record respectively the variations of potential and field magnetic waves generated by electrical activity in the brain every millisecond. Concerning, the study of the localization source, the brain localizationactivity requires the solution of a inverse problem. Many different imaging methods are used to solve the inverse problem.The aim of the presentstudy is to provide comparison criteria for choosing the least bad method. Hence, the transcranial magnetic stimulation (TMS) and electroencephalography (EEG) technique are combined for the sake of studying the dynamics of the brain at rest following a disturbance. The study focuses in the comparison of the following methods for EEG following stimulation by TMS: sLORETA (standardized Low Resolution Electromagnetic Tomography), MNE (Minimum Estimate of the standard), dSPM (dynamic Statistical Parametric Mapping) and wMEM (wavelet based on the Maximum Entropy on the Mean)in order to study the impact of TMS towards rest and to study inter and intra zone connectivity.The contribution of the comparison is demonstrated via the stages of the simulations.
A Fault Location Method Using Direct Convolution: Electromagnetic Time Reversal or Not Reversal
Authors: Guanbo Wang, Chijie Zhuang
Date: 2021-11-27 00:00:00

Electromagnetic time reversal (EMTR) is drawing increasing interest in short-circuit fault location. In this letter, we investigate the classic EMTR fault location methods and find that it is not necessary to reverse the obtained signal in time which is a standard operation in these methods before injecting it into the network. The effectiveness of EMTR fault location method results from the specific similarity of the transfer functions in the forward and reverse processes. Therefore, we can inject an arbitrary type and length of source in the reverse process to locate the fault. Based on this observation, we propose a new EMTR fault location method using direct convolution. This method is different from the traditional methods, and it only needs to pre-calculate the assumed fault transients for a given network, which can be stored in embedded hardware. The faults can be located efficiently via direct convolution of the signal collected from a fault and the pre-stored calculated transients, even using a fraction of the fault signal.
Algebra, Geometry and Topology of ERK Kinetics
Authors: Lewis Marsh, Emilie Dufresne, Helen M. Byrne, Heather A. Harrington
Date: 2021-12-01 00:00:00

The MEK/ERK signalling pathway is involved in cell division, cell specialisation, survival and cell death. Here we study a polynomial dynamical system describing the dynamics of MEK/ERK proposed by Yeung et al. with their experimental setup, data and known biological information. The experimental dataset is a time-course of ERK measurements in different phosphorylation states following activation of either wild-type MEK or MEK mutations associated with cancer or developmental defects. We demonstrate how methods from computational algebraic geometry, differential algebra, Bayesian statistics and computational algebraic topology can inform the model reduction, identification and parameter inference of MEK variants, respectively. Throughout, we show how this algebraic viewpoint offers a rigorous and systematic analysis of such models.
A multi-stage model of cell proliferation and death: tracking cell divisions with Erlang distributions
Authors: Giulia Belluccini, Martín López-García, Grant Lythe, Carmen Molina-París
Date: 2021-12-01 00:00:00

Lymphocyte populations, stimulated in vitro or in vivo, grow as cells divide. Stochastic models are appropriate because some cells undergo multiple rounds of division, some die, and others of the same type in the same conditions do not divide at all. If individual cells behave independently, each can be imagined as sampling from a probability density of times to division. The most convenient choice of density in mathematical and computational work, the exponential density, overestimates the probability of short division times. We consider a multi-stage model that produces an Erlang distribution of times to division, and an exponential distribution of times to die. The resulting dynamics of competing fates is a type of cyton model. Using Approximate Bayesian Computation, we compare our model to published cell counts, obtained after CFSE-labelled OT-I and F5 T cells were transferred to lymphopenic mice. The death rate is assumed to scale linearly with the generation (number of divisions) and the number of stages of undivided cells (generation $0$) is allowed to differ from that of cells that have divided at least once (generation greater than zero). Multiple stages are preferred in posterior distributions, and the mean time to first division is longer than the mean time to subsequent divisions.
Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach
Authors: Huyen Pham, Xiaoli Wei, Chao Zhou
Date: 2018-09-05 00:00:00

This paper focuses on a dynamic multi-asset mean-variance portfolio selection problem under model uncertainty. We develop a continuous time framework for taking into account ambiguity aversion about both expected return rates and correlation matrix of the assets, and for studying the join effects on portfolio diversification. The dynamic setting allows us to consider time varying ambiguity sets, which include the cases where the drift and correlation are estimated on a rolling window of historical data or when the investor takes into account learning on the ambiguity. In this context, we prove a general separation principle for the associated robust control problem, which allows us to reduce the determination of the optimal dynamic strategy to the parametric computation of the minimal risk premium function. Our results provide a justification for under-diversification, as documented in empirical studies and in the static models [16], [34]. Furthermore, we explicitly quantify the degree of under-diversification in termsof correlation bounds and Sharpe ratios proximities, and emphasize the different features induced by drift and correlation ambiguity. In particular, we show that an investor with a poor confidence in the expected return estimation does not hold any risky asset, and on the other hand, trades only one risky asset when the level of ambiguity on correlation matrix is large. We also provide a complete picture of the diversification for the optimal robust portfolio in the three-asset case JEL Classification: G11, C61 MSC Classification: 91G10, 91G80, 60H30
Evolutionary paths to lateralization of complex brain functions
Authors: Luís F Seoane
Date: 2021-12-01 00:00:00

At large, most animal brains present two mirror-symmetric sides; but closer inspection reveals a range of asymmetries (in shape and function), that seem more salient in more cognitively complex species. Sustaining symmetric, redundant neural circuitry has associated metabolic costs, but it might aid in implementing computations within noisy environments or with faulty pieces. It has been suggested that the complexity of a computational task might play a role in breaking bilaterally symmetric circuits into fully lateralized ones; yet a rigorous, mathematically grounded theory of how this mechanism might work is missing. Here we provide such a mathematical framework, starting with the simplest assumptions, but extending our results to a comprehensive range of biologically and computationally relevant scenarios. We show mathematically that only fully lateralized or bilateral solutions are relevant within our framework (dismissing configurations in which circuits are only partially engaged). We provide maps that show when each of these configurations is preferred depending on costs, contributed fitness, circuit reliability, and task complexity. We discuss evolutionary paths leading from bilateral to lateralized configurations and other possible outcomes. The implications of these results for evolution, development, and rehabilitation of damaged or aged brains is discussed. Our work constitutes a limit case that should constrain and underlie similar mappings when other aspects (aside task complexity and circuit reliability) are considered.
Generalized Modeling: A survey and guide
Authors: Jana C. Massing, Thilo Gross
Date: 2021-12-01 00:00:00
Tasks: Survey

Many current challenges involve understanding the complex dynamical interplay between the constituents of systems. Typically, the number of such constituents is high, but only limited data sources on them are available. Conventional dynamical models of complex systems are rarely mathematically tractable and their numerical exploration suffers both from computational and data limitations. Here we review generalized modeling, an alternative approach to formulating dynamical models. We argue that this approach deals elegantly with the uncertainties that exist in real world data and enables analytical insight or highly efficient numerical investigation. We provide a survey of recent successes of generalized modeling and a guide to the application of this modeling approach in future studies such as complex integrative ecological models.
Backward bifurcation and saddle-node bifurcation in virus-immune dynamics
Authors: Tengfei Wang, Shaoli Wang, Fei Xu
Date: 2021-12-01 00:00:00
Tasks: Math

Recently, Wang and Xu [ Appl. Math. Lett. 78 (2018) 105-111] studied thresholds and bi-stability in virus-immune dynamics. In this paper, we show there also exist backward bifurcation and saddle node bifurcation in this model. Our investigation demonstrates the existence of post-bifurcation phenomenon in the system when the immune strength was selected as the bifurcation parameter. The bifurcation of saddle knot is then analyzed. The values of backward bifurcation point and saddle node bifurcation point are the elite control threshold and post-treatment control threshold respectively. The time interval between the two thresholds is the bi-stable interval.
Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1
Authors: Colin LaMont, Jakub Otwinowski, Kanika Vanshylla, Henning Gruell, Florian Klein, Armita Nourmohammad
Date: 2021-11-30 00:00:00

Broadly neutralizing antibodies (bNAbs) are promising targets for vaccination and therapy against HIV. Passive infusions of bNAbs have shown promise in clinical trials as a potential alternative for anti-retroviral therapy. A key challenge for the potential clinical application of bnAbs is the suppression of viral escape, which is more effectively achieved with a combination of bNAbs. However, identifying an optimal bNAb cocktail is combinatorially complex. Here, we propose a computational approach to predict the efficacy of a bNAb therapy trial based on the population genetics of HIV escape, which we parametrize using high-throughput HIV sequence data from a cohort of untreated bNAb-naive patients. By quantifying the mutational target size and the fitness cost of HIV-1 escape from bNAbs, we reliably predict the distribution of rebound times in three clinical trials. Importantly, we show that early rebounds are dominated by the pre-treatment standing variation of HIV-1 populations, rather than spontaneous mutations during treatment. Lastly, we show that a cocktail of three bNAbs is necessary to suppress the chances of viral escape below 1%, and we predict the optimal composition of such a bNAb cocktail. Our results offer a rational design for bNAb therapy against HIV-1, and more generally show how genetic data could be used to predict treatment outcomes and design new approaches to pathogenic control.
A General Approach for Lookback Option Pricing under Markov Models
Authors: Gongqiu Zhang, Lingfei Li
Date: 2021-12-01 00:00:00

We propose a very efficient method for pricing various types of lookback options under Markov models. We utilize the model-free representations of lookback option prices as integrals of first passage probabilities. We combine efficient numerical quadrature with continuous-time Markov chain approximation for the first passage problem to price lookbacks. Our method is applicable to a variety of models, including one-dimensional time-homogeneous and time-inhomogeneous Markov processes, regime-switching models and stochastic local volatility models. We demonstrate the efficiency of our method through various numerical examples.
Extremal Analysis of Flooding Risk and Management
Authors: Chengxiu Ling, Jiayi Li, Yixuan Liu, Zhiyan Cai
Date: 2021-12-01 00:00:00
Tasks: Management

Catastrophic losses caused by natural disasters receive a growing concern about the severe rise in magnitude and frequency. The constructions of insurance and financial management scheme become increasingly necessary to diversify the disaster risks. Given the frequency and severity of floods in China, this paper investigates the extreme analysis of flood-related huge losses and extreme precipitations using Peaks-Over-Threshold method and Point Process (PP) model. These findings are further utilized for both designs of flood zoning insurance and flooding catastrophic bond: (1) Using the extrapolation approach in Extreme Value Theory (EVT), the estimated Value-at-Risk (VaR) and conditional VaR (CVaR) are given to determine the cross-regional insurance premium together with the Grey Relational Analysis (GRA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The flood risk vulnerability and threat are analyzed with both the geography and economic factors into considerations, leading to the three layered premium levels of the 19 flood-prone provinces. (2) To hedge the risk for insurers and reinsurers to the financial market, we design a flooding catastrophe bond with considerate trigger choices and the pricing mechanism to balance the benefits of both reinsurers and investors. To reflect both the market price of catastrophe risk and the low-correlated financial interest risk, we utilize the pricing mechanism of Tang and Yuan (2021) to analyze the pricing sensitivity against the tail risk of the flooding disaster and the distortion magnitude and the market risk through the distortion magnitude involved in Wang's transform. Finally, constructive suggestions and policies are proposed concerning the flood risk warning and prevention.
Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices
Authors: Saideep Tiku, Sudeep Pasricha
Date: 2021-11-28 00:00:00
Tasks: Indoor Localization

Fingerprinting-based indoor localization is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. The superior pairing of ubiquitously available WiFi signals with computationally capable smartphones is set to revolutionize the area of indoor localization. However, the observed signal characteristics from independently maintained WiFi access points vary greatly over time. Moreover, some of the WiFi access points visible at the initial deployment phase may be replaced or removed over time. These factors are often ignored in indoor localization frameworks and cause gradual and catastrophic degradation of localization accuracy post-deployment (over weeks and months). To overcome these challenges, we propose a Siamese neural encoder-based framework that offers up to 40% reduction in degradation of localization accuracy over time compared to the state-of-the-art in the area, without requiring any retraining.
GPU-Accelerated Forward-Backward algorithm with Application to Lattice-Free MMI
Authors: Lucas Ondel, Léa-Marie Lam-Yee-Mui, Martin Kocour, Caio Filippo Corro, Lukáš Burget
Date: 2021-10-22 00:00:00
Tasks: GPU

We propose to express the forward-backward algorithm in terms of operations between sparse matrices in a specific semiring. This new perspective naturally leads to a GPU-friendly algorithm which is easy to implement in Julia or any programming languages with native support of semiring algebra. We use this new implementation to train a TDNN with the LF-MMI objective function and we compare the training time of our system with PyChain - a recently introduced C++/CUDA implementation of the LF-MMI loss. Our implementation is about two times faster while not having to use any approximation such as the "leaky-HMM".
Procedural modelling of terrains with constraints
Authors: Cristina Gasch, Miguel Chover, Inmaculada Remolar, Cristina Rebollo
Date: 2020-08-18 00:00:00
Proceeding: Multimedia Tools and Applications 2020 8

Terrain is an essential part of any outdoor environment and, consequently, many techniques have appeared that deal with the problem of its automatic generation, such as procedural modeling. One form to create terrains is using noise functions because its low computational cost and its random result. However, the randomness of these functions also makes it difficult to have any control over the result obtained. In order to solve the problem of lack of control, this paper presents a new method noise-based that allows procedural terrains creation with elevation constraints (GPS routes, points of interest and areas of interest). For this, the method establishes the restrictions as fixed values in the heightmap function and
creates a system of equations to obtain all points that they depend this restrictions. In this way, the terrain obtained maintains the random noise, but including the desired restrictions.
The paper also includes how we apply this method on large terrain models without losing resolution or increasing the computational cost excessively. The results show that our
method makes it possible to integrate this kind of constraints with high accuracy and realism while preserving the natural appearance of the procedural generation.
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Authors: Sara P. Oliveira, Pedro C. Neto, João Fraga, Diana Montezuma, Ana Monteiro, João Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto, Jaime S. Cardoso
Date: 2021-07-13 00:00:00
Proceeding: Scientific Reports 2021 7
Tasks: Colorectal Polyps Characterization, whole slide images

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.
Cost Functions over Feasible Power Transfer Regions of Virtual Power Plants
Authors: Wei Lin, Changhong Zhao
Date: 2021-12-02 00:00:00

A virtual power plant (VPP) facilitates the integration of distributed energy resources (DERs) for the transmission-level operation. A challenge in operating a VPP is to characterize the cost function over its feasible power transfer region under DERs' uncertainties. To address this challenge, a characterization method is presented in this paper for the intraday operation of a VPP based on the concepts of nonanticipativity and robustness to DERs' uncertainties. The characterization stems from designing a second-order cone programming (SOCP) problem, based on which a feasible power transfer region across all time periods is constructed by exploring boundary points at each time period and establishing time coupling constraints. Furthermore, a cost function over the feasible power transfer region is formulated as a convex piecewise surface whose breakpoints are obtained by solving SOCP problems, together with a constant compensation cost from a linear programming problem. Finally, to alleviate the heavy computational burden brought by numerous DERs, an approximation method is presented by identifying the critical DERs whose uncertainties have dominant impacts. The effectiveness of the presented methods is verified by the numerical experiments in a 3-bus system and the IEEE 136-bus system.
Masked-attention Mask Transformer for Universal Image Segmentation
Authors: Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar
Date: 2021-12-02 00:00:00
Proceeding: CVPR 2022 1
Tasks: 2D Semantic Segmentation, Image Segmentation, Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation, Universal Segmentation

Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).
TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning
Authors: Linhao Qu, Shaolei Liu, Manning Wang, Zhijian Song
Date: 2021-12-02 00:00:00
Tasks: Decoder, Multi-Exposure Image Fusion, Multi-Task Learning

In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning; through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features. In addition, to compensate for the defect in establishing long-range dependencies in CNN-based architectures, we design an encoder that combines a CNN module with a transformer module. This combination enables the network to focus on both local and global information. We evaluated our method and compared it to 11 competitive traditional and deep learning-based methods on the latest released multi-exposure image fusion benchmark dataset, and our method achieved the best performance in both subjective and objective evaluations.