pwc-archive/papers-with-abstracts huggingface dataset:
A GN-model closed-form formula supporting ultra-low fiber loss and short fiber spans
Authors: Mahdi Ranjbar Zefreh, Pierluigi Poggiolini
Date: 2021-11-04 00:00:00
Tasks: Form

We report on a fully closed-form approximation of the GN model for Nyquist WDM systems that extends the range of applicability of previously available formulas to values of fiber loss and span loss that were not previously covered. In particular, so far, a closed-form formula was available for zero loss, and another for span loss of at least about 7 dB. The new formula is accurate over any fiber loss and any span loss value. The interest for this new formula is varied, ranging from hollow-core fibers to improving modeling of standard systems that present lumped loss along installed spans (such as splices or connectors).
Compact Heterogeneous Integration for Next Generation High Frequency Scalable Array with Miniaturized and Efficient Power Delivery Network
Authors: Najme Ebrahimi
Date: 2021-10-29 00:00:00

Next generation communication and sensing require enabling technologies for miniaturized and efficient heterogeneous systems while integrating technologies ranging from silicon to compound semiconductors and from photonic chips to micro-sensors. To this end, high frequency and mm-wave (MMW) lossy parasitics and delay between modules need to be significantly reduced to minimize area, loss and thermal heating of inter-chip wiring and power delivery networks. In this work, we propose novel approaches to achieve an efficient wideband MMW array integrations. The proposed techniques are built upon the following: 1) fixed antenna package buildup for every element with differential excitation on two half sides of array to reduce the fabrication cost and the IC-to-antenna routing loss; 2) miniaturized aperture coupled local oscillator (LO) and intermediate frequency (IF) power delivery feed distribution to minimize the packaging stacked layers and their loss. The proposed 16-element antenna array is integrated which 4 dies in 2x2 configurations implemented in a 90-nm SiGe BiCMOS process using compact Weaver image-selection architecture (WISA). The proposed miniaturized and efficient architecture from circuit and chip level to package level results in 1.5 GHz modulation bandwidth for 64 QAM (9 Gb/s) and 2 GHz for 16 QAM with only +-2 dB EVM variation over the 20% FBW (71-86 GHz). The system produces 30-dBm EIRP with enhanced efficiency of 25% EIRP/PDC over the bandwidth
RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition
Authors: Shuya Ding, Zhe Chen, Tianyue Zheng, Jun Luo
Date: 2021-10-29 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Meta-Learning

Radio-Frequency (RF) based device-free Human Activity Recognition (HAR) rises as a promising solution for many applications. However, device-free (or contactless) sensing is often more sensitive to environment changes than device-based (or wearable) sensing. Also, RF datasets strictly require on-line labeling during collection, starkly different from image and text data collections where human interpretations can be leveraged to perform off-line labeling. Therefore, existing solutions to RF-HAR entail a laborious data collection process for adapting to new environments. To this end, we propose RF-Net as a meta-learning based approach to one-shot RF-HAR; it reduces the labeling efforts for environment adaptation to the minimum level. In particular, we first examine three representative RF sensing techniques and two major meta-learning approaches. The results motivate us to innovate in two designs: i) a dual-path base HAR network, where both time and frequency domains are dedicated to learning powerful RF features including spatial and attention-based temporal ones, and ii) a metric-based meta-learning framework to enhance the fast adaption capability of the base network, including an RF-specific metric module along with a residual classification module. We conduct extensive experiments based on all three RF sensing techniques in multiple real-world indoor environments; all results strongly demonstrate the efficacy of RF-Net compared with state-of-the-art baselines.
Human Activity Recognition using Attribute-Based Neural Networks and Context Information
Authors: Stefan Lüdtke, Fernando Moya Rueda, Waqas Ahmed, Gernot A. Fink, Thomas Kirste
Date: 2021-10-28 00:00:00
Tasks: Activity Recognition, Attribute, Human Activity Recognition

We consider human activity recognition (HAR) from wearable sensor data in manual-work processes, like warehouse order-picking. Such structured domains can often be partitioned into distinct process steps, e.g., packaging or transporting. Each process step can have a different prior distribution over activity classes, e.g., standing or walking, and different system dynamics. Here, we show how such context information can be integrated systematically into a deep neural network-based HAR system. Specifically, we propose a hybrid architecture that combines a deep neural network-that estimates high-level movement descriptors, attributes, from the raw-sensor data-and a shallow classifier, which predicts activity classes from the estimated attributes and (optional) context information, like the currently executed process step. We empirically show that our proposed architecture increases HAR performance, compared to state-of-the-art methods. Additionally, we show that HAR performance can be further increased when information about process steps is incorporated, even when that information is only partially correct.
Dengue transmission dynamics in age-structured human populations in the presence of Wolbachia
Authors: Jozsef Z. Farkas, Stephen A. Gourley, Rongsong Liu
Date: 2021-11-08 00:00:00

According to the World Health Organization the global incidence rate of dengue infections have risen drastically in recent years. It is estimated that globally the number of new infections is in the range of $100$ to $400$ million per annum. At the same time a number of recent studies reported the existence of Wolbachia strains, which inhibit dengue virus replication in mosquito species that are primary vectors for dengue transmission. In this study we focus on the impact of Wolbachia on dengue transmission dynamics in an age-structured human population. We introduce a mathematical model, which takes into account age-related effects, such as age-dependent human recovery and mortality rates, as well as age-dependent vector to human dengue transmission efficacy. We deduce an explicit formula for the basic reproduction number $\mathcal{R}_0$, which allows us to assess the impact of the above mentioned age-related effects on the local asymptotic stability of the dengue free equilibrium. The formula we deduce for $\mathcal{R}_0$ also shows the intricate relationship between human demography and the presence of a dengue inhibiting Wolbachia strain.
Defining binary phylogenetic trees using parsimony
Authors: Mareike Fischer
Date: 2021-11-08 00:00:00

Phylogenetic (i.e. leaf-labeled) trees play a fundamental role in evolutionary research. A typical problem is to reconstruct such trees from data like DNA alignments (whose columns are often referred to as characters), and a simple optimization criterion for such reconstructions is maximum parsimony. It is generally assumed that this criterion works well for data in which state changes are rare. In the present manuscript, we prove that each phylogenetic tree $T$ with $n\geq 20 k$ leaves is uniquely defined by the set $A_k(T)$, which consists of all characters with parsimony score $k$ on $T$. This can be considered as a promising first step towards showing that maximum parsimony as a tree reconstruction criterion is justified when the number of changes in the data is relatively small.
Realism is almost true: A critique of the interface theory of perception
Authors: Arman Rezayati Charan, Shahriar Gharibzadeh, S. Mahdi Firouzabadi
Date: 2021-11-06 00:00:00
Tasks: Philosophy

Objective reality and its relation to our perception have been an important topic of debate in philosophy and cognitive science. Hoffman's "interface theory of perception," which asserts that our perception has no congruency with reality, is recent and controversial among existing theories. Hoffman and Prakash formulated and evaluated their theory using evolutionary game theory and genetic algorithms. This paper investigates the "interface theory of perception," introducing an agent-based simulation. Using the principles and hypothesis of Hoffman's model, first, we regenerate and approve his initial claims to consider interface as a winning perception strategy. Then we move forward to assess his model in more realistic conditions and challenge interface perception model. Our findings indicate that in case of drastic environmental changes, interface perception is no longer compatible with reality and pushes the interface species further toward extinction. Our proposed model will pave the road for future studies to investigate the perception strategies in a more realistic condition.
A metric for tradable biodiversity credits linked to the Living Planet Index and global species conservation
Authors: Axel G. Rossberg, Jacob D. O'Sullivan, Svetlana Malysheva, Nadav M. Shnerb
Date: 2021-11-06 00:00:00

Difficulties identifying appropriate biodiversity impact metrics remain a major barrier to inclusion of biodiversity considerations in environmentally responsible investment. We propose and analyse a simple science-based local metric: the sum of proportional changes in local species abundances relative to their global species abundances, with a correction for species close to extinction. As we show, this metric quantifies changes in the mean long-term global survival probability of species. It links mathematically to a widely cited global biodiversity indicator, the Living Planet Index, for which we propose an improved formula that directly addresses the known problem of singularities caused by extinctions. We show that, in an ideal market, trade in our metric would lead to near-optimal allocation of resources to species conservation. We further show that the metric is closely related to several other metrics and indices already in use. Barriers to adoption are therefore low. Used in conjunction with metrics addressing ecosystem functioning and services, potential areas of application include biodiversity related financial disclosures and voluntary or legislated no net biodiversity loss policies.
Graph topology determines coexistence in the rock-paper-scissors system
Authors: Mark Lowell
Date: 2021-11-05 00:00:00

Species survival in the $(3, 1)$ May-Leonard system is determined by the mobility, with a critical mobility threshold between long-term coexistence and extinction. We show experimentally that the critical mobility threshold is determined by the topology of the graph, with the critical mobility threshold of the periodic lattice twice that of a topological 2-sphere. We use topological techniques to monitor the evolution of patterns in various graphs and estimate their mean persistence time, and show that the difference in critical mobility threshold is due to a specific pattern that can form on the lattice but not on the sphere. Finally, we release the software toolkit we developed to perform these experiments to the community.
Prediction of cognitive decline for enrichment of Alzheimer's disease clinical trials
Authors: Angela Tam, César Laurent, Serge Gauthier, Christian Dansereau
Date: 2021-11-07 00:00:00

A key issue to Alzheimer's disease clinical trial failures is poor participant selection. Participants have heterogeneous cognitive trajectories and many do not decline during trials, which reduces a study's power to detect treatment effects. Trials need enrichment strategies to enroll individuals who will decline. We developed machine learning models to predict cognitive trajectories in participants with early Alzheimer's disease (n=1342) and presymptomatic individuals (n=756) over 24 and 48 months respectively. Baseline magnetic resonance imaging, cognitive tests, demographics, and APOE genotype were used to classify decliners, measured by an increase in CDR-Sum of Boxes, and non-decliners with up to 79% area under the curve (cross-validated and out-of-sample). Using these prognostic models to recruit enriched cohorts of decliners can reduce required sample sizes by as much as 51%, while maintaining the same detection power, and thus may improve trial quality, derisk endpoint failures, and accelerate therapeutic development in Alzheimer's disease.
4Dia: A tool for automated 4D microscope image alignment
Authors: Nimmy S. John, Michelle A. Urman, ChangHwan Lee
Date: 2021-11-07 00:00:00

Recent advances in microscopy enable three-dimensional live imaging at a high resolution. Long-term live imaging of a multicellular organism requires immobilization of the organism under stable physiological conditions. Despite proper immobilization, challenges remain within live imaging data analysis due to other intrinsic and extrinsic dynamics, which can result in misalignments of an image series over time. 4Dia, an ImageJ/Fiji macro script, aligns 3D timelapse images through Z-stacks as well as over time using any user selected channel. 4Dia can be used for essentially any tissue sample with no limit on the size of Z-stack or the number of timepoints.
Dynamics of unfolded protein aggregation
Authors: Utkarsh Upadhyay, Chandrima Barua, Shivani Devi, Jay Prakash Kumar, R. K. Brojen Singh
Date: 2021-11-07 00:00:00

Unfolded protein aggregation in cellular system is a problem causing various types of diseases depending on which type unfolded proteins aggregate. This phenomenon of aggregation may take place during production, storage, shipment or delivery in the cellular medium. In the present work, we studied a simplified and extended version of unfolded protein aggregation model by Lumry and Eyring using stochastic approach. We solved analytically the Master equation of the model for the probability distribution $P(x,t)$ of the unfolded protein population and the solution was found to be time dependent complex binomial distribution. In the large population limit $P(x,t)\sim \Lambda(x,t)\times Pois(x,t)$. Further, the distribution became Normal distribution at large population and mean of the distribution limit: $P(x,t)\sim\Lambda(x,t)\times N(\langle qx\rangle,\langle qx\rangle)$. The fluctuations inherent in the dynamics measured by Fano factor can have sub-Poisson, Poisson and super-Poisson at different situations.
On biological flow networks: Antagonism between hydrodynamic and metabolic stimuli as driver of topological transitions
Authors: Felix Kramer, Carl D. Modes
Date: 2021-11-08 00:00:00

A plethora of computational models have been developed in recent decades to account for the morphogenesis of complex biological fluid networks, such as capillary beds. Contemporary adaptation models are based on optimization schemes where networks react and adapt toward given flow patterns. Doing so, a system reduces dissipation and network volume, thereby altering its final form. Yet, recent numeric studies on network morphogenesis, incorporating uptake of metabolites by the embedding tissue, have indicated the conventional approach to be insufficient. Here, we systematically study a hybrid-model which combines the network adaptation schemes intended to generate space-filling perfusion as well as optimal filtration of metabolites. As a result, we find hydrodynamic stimuli (wall-shear stress) and filtration based stimuli (uptake of metabolites) to be antagonistic as hydrodynamically optimized systems have suboptimal uptake qualities and vice versa. We show that a switch between different optimization regimes is typically accompanied with a complex transition between topologically redundant meshes and spanning trees. Depending on the metabolite demand and uptake capabilities of the adaptating networks, we are further able to demonstrate the existence of nullity re-entrant behavior and the development of compromised phenotypes such as dangling non-perfused vessels and bottlenecks.
Portfolio analysis with mean-CVaR and mean-CVaR-skewness criteria based on mean-variance mixture models
Authors: Nuerxiati Abudurexiti, Kai He, Dongdong Hu, Svetlozar T. Rachev, Hasanjan Sayit, Ruoyu Sun
Date: 2021-11-08 00:00:00
Tasks: Form, Portfolio Optimization

The paper Zhao et al. (2015) shows that mean-CVaR-skewness portfolio optimization problems based on asymetric Laplace (AL) distributions can be transformed into quadratic optimization problems under which closed form solutions can be found. In this note, we show that such result also holds for mean-risk-skewness portfolio optimization problems when the underlying distribution is a larger class of normal mean-variance mixture (NMVM) models than the class of AL distributions. We then study the value at risk (VaR) and conditional value at risk (CVaR) risk measures on portfolios of returns with NMVM distributions. They have closed form expressions for portfolios of normal and more generally elliptically distributed returns as discussed in Rockafellar & Uryasev (2000) and in Landsman & Valdez (2003). When the returns have general NMVM distributions, these risk measures do not give closed form expressions. In this note, we give approximate closed form expressions for VaR and CVaR of portfolios of returns with NMVM distributions. Numerical tests show that our closed form formulas give accurate values for VaR and CVaR and shortens the computational time for portfolio optimization problems associated with VaR and CVaR considerably.
Equity-Linked Life Insurances on Maximum of Several Assets
Authors: Battulga Gankhuu
Date: 2021-11-07 00:00:00

Economic variables play important roles in any economic model, and sudden and dramatic changes exist in the financial market and economy. For this reason, to price and hedge equity-linked life insurance products, including segregated funds and unit-linked life insurance products on maximum price of several assets, this paper introduces Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process. By assuming that a regime-switching process is generated by a homogeneous Markov process and a residual process follows a heteroscedastic model, we obtain joint distribution of endogenous variables and insured's future lifetime random variable under risk-neutral probability probability measure. Using the distribution function, we obtain net single premiums and hedging formulas of the equity-linked life insurance products. An advantage of our model is it depends on economic variables and is not complicated as compared to previous papers.
Projection of Functionals and Fast Pricing of Exotic Options
Authors: Valentin Tissot-Daguette
Date: 2021-11-05 00:00:00

We investigate the approximation of path functionals. In particular, we advocate the use of the Karhunen-Lo\`eve expansion, the continuous analogue of Principal Component Analysis, to extract relevant information from the image of a functional. Having accurate estimate of functionals is of paramount importance in the context of exotic derivatives pricing, as presented in the practical applications. Specifically, we show how a simulation-based procedure, which we call the Karhunen-Lo\`eve Monte Carlo (KLMC) algorithm, allows fast and efficient computation of the price of path-dependent options. We also explore the path signature as an alternative tool to project both paths and functionals.
Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification
Authors: Chengyi Zhuang, Qiang Ma
Date: 2018-04-01 00:00:00
Proceeding: World Wide Web Conference 2018 4
Tasks: Classification

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as the internet, social networks, biological networks, and many others. The importance of being able to effectively mine and learn from such data continues to grow as more and more structured data become available. In this paper, we present a simple and scalable semi-supervised learning method for graph-structured data in which only a very small portion of the training data are labeled. To sufficiently embed the graph knowledge, our method performs graph convolution from different views of the raw data. In particular, a dual graph convolutional neural network method is devised to jointly consider the two essential assumptions of semi-supervised learning: (1) local consistency and (2) global consistency. Accordingly, two convolutional neural networks are devised to embed the local-consistency-based and global-consistency-based knowledge, respectively. Given the different data transformations from the two networks, we then introduce an unsupervised temporal loss function for the ensemble. In experiments using both unsupervised and supervised loss functions, our method outperforms state-of-the-art techniques on different datasets.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Authors: M.Raissi, P.Perdikaris, G.E.Karniadakis
Date: 2019-02-01 00:00:00
Proceeding: Journal of Computational Physics 2019 2

We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct types of algorithms, namely continuous time and discrete time models. The first type of models forms a new family of data-efficient spatio-temporal function approximators, while the latter type allows the use of arbitrarily accurate implicit Runge–Kutta time stepping schemes with unlimited number of stages. The effectiveness of the proposed framework is demonstrated through a collection of classical problems in fluids, quantum mechanics, reaction–diffusion systems, and the propagation of nonlinear shallow-water waves.
Recurrent Convolutional Neural Networks for Text Classification
Authors: Siwei Lai, Liheng Xu, Kang Liu, Jun Zhao
Date: 2015-01-01 00:00:00
Proceeding: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence 2015 1
Tasks: Classification, Emotion Recognition in Conversation, text-classification, Text Classification

Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features. In our model, we apply a recurrent structure to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks. We also
employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key components in texts. We conduct experiments on four commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.