pwc-archive/papers-with-abstracts huggingface dataset:
MISO Wireless Localization in The Presence of Reconfigurable Intelligent Surface
Authors: Sajjad Ghiasvand, Arash Nasri, Ali H. Abdollahi Bafghi, Masoumeh Nasiri-Kenari
Date: 2022-08-19 00:00:00

Reconfigurable Intelligent Surface (RIS) plays a pivotal role in the sixth generation networks to enhance communication rate and localization accuracy. In this letter, we propose a positioning algorithm in a RIS-assisted environment, where the Base Station (BS) is multi-antenna, and the Mobile Station (MS) is single-antenna. We show that our method can achieve a high-precision positioning if the line-of-sight (LOS) is obstructed and three RISs are available. We send several known signals to the receiver in different time slots and change the phase shifters of the RISs simultaneously in a proper way, and we propose a technique to eliminate the destructive effect of the angle-of-departure (AoD) in order to determine the distances between each RISs and the MS. The accuracy of the proposed algorithm is better than the algorithms which do not estimate the AoD, shown in the numerical result section.
Modélisation spatio-temporelle de l'utilisation des masques par le grand public en Guadeloupe et en Martinique
Authors: Jonas Sylneon
Date: 2022-08-20 00:00:00

In this article, we develop a deterministic compartmental model including the spatio-temporal aspect and the wearing of masks by the general public for the spread of the Covid-19 epidemic. This model is based on the SIR model of Kermack and McKendrick and includes mobility terms that model the movement of individuals wearing masks between Guadeloupe andMartinique. The objective of the model is to analyze the effects of mobility and the wearing of masks by the general public on the spread of the Covid-19 epidemic in these two regions.Numerical simulations of this model show the importance of wearing masks by the general public, even if they are not very inefficient, and indicate that the model is capable of qualitatively simulating the propagation trends of the Covid-19 epidemic. This work is part ofmy doctoral thesis, the subject of which is entitled {\guillemotleft} stochastic modeling of Covid-19 in an island environment taking into account the spatio-temporal aspect {\guillemotright}.
Predicting the Biological Classification of Cell-Cycle Regulated Genes of Saccharomyces cerevisiae using Community Detection Algorithms on Gene Co-expression Networks
Authors: Jhoirene B. Clemente, Gabriel Besas, Jerick Callado, John Erol Evangelista
Date: 2022-08-22 00:00:00
Tasks: Clustering, Community Detection, graph construction, Time Series, Time Series Analysis

The conventional approach for analyzing gene expression data involves clustering algorithms. Cluster analyses provide partitioning of the set of genes that can predict biological classification based on its similarity in n-dimensional space. In this study, we investigate whether network analysis will provide an advantage over the traditional approach. We identify the advantages and disadvantages of using the value-based and the rank-based construction in creating a graph representation of the original gene-expression data in a time-series format. We tested four community detection algorithms, namely, the Clauset-Newman-Moore (greedy), Louvain, Leiden, and Girvan-Newman algorithms in predicting the 5 functional groups of genes. We used the Adjusted Rand Index to assess the quality of the predicted communities with respect to the biological classifications. We showed that Girvan-Newman outperforms the 3 modularity-based algorithms in both value-based and ranked-based constructed graphs. Moreover, we also show that when compared to the conventional clustering algorithms such as K-means, Spectral, Birch, and Agglomerative algorithms, we obtained a higher ARI with Girvan-Newman. This study also provides a tool for graph construction, visualization, and community detection for further analysis of gene expression data.
Effect in the spectra of eigenvalues and dynamics of RNNs trained with Excitatory-Inhibitory constraint
Authors: Cecilia Jarne, Mariano Caruso
Date: 2022-08-22 00:00:00
Tasks: Decision Making

In order to comprehend and enhance models that describes various brain regions is important to study the dynamics of trained recurrent neural networks. Including Dales law in such models usually presents several challenges. However, this is an important aspect that allows computational models to better capture the characteristics of the brain. Here we present a framework to train networks using such constraint. Then we have used it to train them in simple decision making tasks. We characterized the eigenvalue distributions of the recurrent weight matrices of such networks. Interestingly, we discovered that the non-dominant eigenvalues of the recurrent weight matrix are distributed in a circle with a radius less than 1 for those whose initial condition before training was random normal and in a ring for those whose initial condition was random orthogonal. In both cases, the radius does not depend on the fraction of excitatory and inhibitory units nor the size of the network. Diminution of the radius, compared to networks trained without the constraint, has implications on the activity and dynamics that we discussed here.
Research on Creative Thinking Mode Based on Category Theory
Authors: Tong Wang
Date: 2022-08-20 00:00:00

The research on the brain mechanism of creativity mainly has two aspects, one is the creative thinking process, and the other is the brain structure and functional connection characteristics of highly creative people. The billions of nerve cells in the brain connect and interact with each other. The hundreds of millions of nerve cells in the brain connect and interact with each other. The human brain has a high degree of complexity at the biological level, especially the rational thinking ability of the human brain. Starting from the connection of molecules, cells, neural networks and the neural function structure of the brain, it may be fundamentally impossible to study the rational thinking mode of human beings. Human's rational thinking mode has a high degree of freedom and transcendence, and such problems cannot be expected to be studied by elaborating the realization of the nervous system. The rational thinking of the brain is mainly based on the structured thinking mode, and the structured thinking mode shows the great scientific power. This paper studies the theoretical model of innovative thinking based on of category theory, and analyzes the creation process of two scientific theories which are landmarks in the history of science, and provides an intuitive, clear interpretation model and rigorous mathematical argument for the creative thinking. The structured thinking way have great revelation and help to create new scientific theories.
A hybrid transmission model for Plasmodium vivax accounting for superinfection, immunity and the hypnozoite reservoir
Authors: Somya Mehra, Peter G. Taylor, James M. McCaw, Jennifer A. Flegg
Date: 2022-08-22 00:00:00
Tasks: Blocking, Epidemiology

Malaria is a vector-borne disease that exacts a grave toll in the Global South. The epidemiology of Plasmodium vivax, the most geographically expansive agent of human malaria, is characterised by the accrual of a reservoir of dormant parasites known as hypnozoites. Relapses, arising from hypnozoite activation events, comprise the majority of the blood-stage infection burden, with implications for the acquisition of immunity and the distribution of superinfection. Here, we construct a hybrid transmission model for P. vivax that concurrently accounts for the accrual of the hypnozoite reservoir, (blood-stage) superinfection and the acquisition of immunity. We begin by analytically characterising within-host dynamics as a function of mosquito-to-human transmission intensity, extending our previous model (comprising an open network of infinite server queues) to capture a discretised immunity level. To model transmission-blocking and antidisease immunity, we allow for geometric decay in the respective probabilities of successful human-to-mosquito transmission and symptomatic blood-stage infection as a function of this immunity level. Under a hybrid approximation -- whereby probabilistic within-host distributions are cast as expected population-level proportions -- we couple host and vector dynamics to recover a deterministic compartmental model in line with Ross-Macdonald theory. We then perform a steady-state analysis for this compartmental model, informed by the (analytic) distributions derived at the within-host level. To characterise transient dynamics, we derive a reduced system of integrodifferential equations (IDEs), likewise informed by our within-host queueing network, allowing us to recover population-level distributions for various quantities of epidemiological interest. Our model provides insights into important, but poorly understood, epidemiological features of P. vivax.
Controlling species densities in structurally perturbed intransitive cycles with higher-order interactions
Authors: Sourin Chatterjee, Sayantan Nag Chowdhury, Dibakar Ghosh, Chittaranjan Hens
Date: 2022-08-22 00:00:00

The persistence of biodiversity of species is a challenging proposition in ecological communities in the face of Darwinian selection. The present article investigates beyond the pairwise competitive interactions and provides a novel perspective for understanding the influence of higher-order interactions on the evolution of social phenotypes. Our simple model yields a prosperous outlook to demonstrate the impact of perturbations on intransitive competitive higher-order interactions. Using a mathematical technique, we show how alone the perturbed interaction network can quickly determine the coexistence equilibrium of competing species instead of solving a large system of ordinary differential equations. It is possible to split the system into multiple feasible cluster states depending on the number of perturbations. Our analysis also reveals the ratio between the unperturbed and perturbed species is inversely proportional to the amount of employed perturbation. Our results suggest that nonlinear dynamical systems and interaction topologies can be interplayed to comprehend species' coexistence under adverse conditions. Particularly our findings signify that less competition between two species increases their abundance and outperforms others.
The Point of No Return: Evolution of Excess Mutation Rate is Possible Even for Simple Mutation Models
Authors: Brian Mintz, Feng Fu
Date: 2022-08-20 00:00:00

Under constant selection, each trait has a fixed fitness, and small mutation rates allow populations to efficiently exploit the optimal trait. Therefore it is reasonable to expect mutation rates will evolve downwards. However, we find this need not be the case, examining several models of mutation. While upwards evolution of mutation rate has been found with frequency or time dependent fitness, we demonstrate its possibility in a much simpler context. This work uses adaptive dynamics to study the evolution of mutation rate, and the replicator-mutator equation to model trait evolution. Our approach differs from previous studies by considering a wide variety of methods to represent mutation. We use a finite string approach inspired by genetics, as well as a model of local mutation on a discretization of the unit intervals, handling mutation beyond the endpoints in three ways. The main contribution of this work is a demonstration that the evolution of mutation rate can be significantly more complicated than what is usually expected in relatively simple models.
Stability of the Epstein-Zin problem
Authors: Michael Monoyios, Oleksii Mostovyi
Date: 2022-08-21 00:00:00

We investigate the stability of the Epstein-Zin problem with respect to small distortions in the dynamics of the traded securities. We work in incomplete market model settings, where our parametrization of perturbations allows for joint distortions in returns and volatility of the risky assets and the interest rate. Considering empirically the most relevant specifications of risk aversion and elasticity of intertemporal substitution, we provide a condition that guarantees the convexity of the domain of the underlying problem and results in the existence and uniqueness of a solution to it. Then, we prove the convergence of the optimal consumption streams, the associated wealth processes, the indirect utility processes, and the value functions in the limit when the model perturbations vanish.
Do diverse and inclusive workplaces benefit investors? An Empirical Analysis on Europe and the United States
Authors: Karoline Bax
Date: 2022-08-22 00:00:00
Tasks: Diversity

As the COVID-19 pandemic restrictions slow down, employees start to return to their offices. Hence, the discussions on optimal workplaces and issues of diversity and inclusion have peaked. Previous research has shown that employees and companies benefit from positive workplace changes. This research questions whether allowing for diversity and inclusion criteria in portfolio construction is beneficial to investors. By considering the new Diversity & Inclusion (D&I) score by Refinitiv, I find evidence that investors might suffer lower returns and pay for investing in responsible (i.e., more diverse and inclusive) employers in both the US and European market.
Fair pricing and hedging under small perturbations of the numéraire on a finite probability space
Authors: William Busching, Delphine Hintz, Oleksii Mostovyi, Alexey Pozdnyakov
Date: 2022-08-21 00:00:00

We consider the problem of fair pricing and hedging under small perturbations of the num\'eraire. We show that for replicable claims, the change of num\'eraire affects neither the fair price nor the hedging strategy. For non-replicable claims, we demonstrate that is not the case. By reformulating the key stochastic control problem in a more tractable form, we show that both the fair price and optimal strategy are stable with respect to small perturbations of the num\'eraire. Further, our approach allows for explicit asymptotic formulas describing the fair price and hedging strategy's leading order correction terms. Mathematically, our results constitute stability and asymptotic analysis of a stochastic control problem under certain perturbations of the integrator of the controlled process, where constraints make this problem hard to analyze.
Valuation of general GMWB annuities in a low interest rate environment
Authors: Claudio Fontana, Francesco Rotondi
Date: 2022-08-22 00:00:00
Tasks: Management

Variable annuities with Guaranteed Minimum Withdrawal Benefits (GMWB) entitle the policy holder to periodic withdrawals together with a terminal payoff linked to the performance of an equity fund. In this paper, we consider the valuation of a general class of GMWB annuities, allowing for step-up, bonus and surrender features, taking also into account mortality risk and death benefits. When dynamic withdrawals are allowed, the valuation of GMWB annuities leads to a stochastic optimal control problem, which we address here by dynamic programming techniques. Adopting a Hull-White interest rate model, correlated with the equity fund, we propose an efficient tree-based algorithm. We perform a thorough analysis of the determinants of the market value of GMWB annuities and of the optimal withdrawal strategies. In particular, we study the impact of a low/negative interest rate environment. Our findings indicate that low/negative rates profoundly affect the optimal withdrawal behaviour and, in combination with step-up and bonus features, increase significantly the fair values of GMWB annuities, which can only be compensated by large management fees.
Comparing and quantifying tail dependence
Authors: Karl Friedrich Siburg, Christopher Strothmann, Gregor Weiß
Date: 2022-08-22 00:00:00

We introduce a new stochastic order for the tail dependence between random variables. We then study different measures of tail dependence which are monotone in the proposed order, thereby extending various known tail dependence coefficients from the literature. We apply our concepts in an empirical study where we investigate the tail dependence for different pairs of S&P 500 stocks and indices, and illustrate the advantage of our measures of tail dependence over the classical tail dependence coefficient.
A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction
Authors: Weimin Lyu, Xinyu Dong, Rachel Wong, Songzhu Zheng, Kayley Abell-Hart, Fusheng Wang, Chao Chen
Date: 2022-08-09 00:00:00
Tasks: Mortality Prediction

Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases. Meanwhile, large amounts of narrative clinical notes provide complementary information, but are often not integrated into predictive models. In this paper, we provide a novel multimodal transformer to fuse clinical notes and structured EHR data for better prediction of in-hospital mortality. To improve interpretability, we propose an integrated gradients (IG) method to select important words in clinical notes and discover the critical structured EHR features with Shapley values. These important words and clinical features are visualized to assist with interpretation of the prediction outcomes. We also investigate the significance of domain adaptive pretraining and task adaptive fine-tuning on the Clinical BERT, which is used to learn the representations of clinical notes. Experiments demonstrated that our model outperforms other methods (AUCPR: 0.538, AUCROC: 0.877, F1:0.490).
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling
Authors: Mengyang Liu, Haozheng Luo, Leonard Thong, Yinghao Li, Chao Zhang, Le Song
Date: 2022-08-07 00:00:00
Tasks: Denoising, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, text annotation

Weak labeling is a popular weak supervision strategy for Named Entity Recognition (NER) tasks, with the goal of reducing the necessity for hand-crafted annotations. Although there are numerous remarkable annotation tools for NER labeling, the subject of integrating weak labeling sources is still unexplored. We introduce a web-based tool for text annotation called SciAnnotate, which stands for scientific annotation tool. Compared to frequently used text annotation tools, our annotation tool allows for the development of weak labels in addition to providing a manual annotation experience. Our tool provides users with multiple user-friendly interfaces for creating weak labels. SciAnnotate additionally allows users to incorporate their own language models and visualize the output of their model for evaluation. In this study, we take multi-source weak label denoising as an example, we utilized a Bertifying Conditional Hidden Markov Model to denoise the weak label generated by our tool. We also evaluate our annotation tool against the dataset provided by Mysore which contains 230 annotated materials synthesis procedures. The results shows that a 53.7% reduction in annotation time obtained AND a 1.6\% increase in recall using weak label denoising. Online demo is available at https://sciannotate.azurewebsites.net/(demo account can be found in README), but we don't host a model server with it, please check the README in supplementary material for model server usage.
Unit Testing for Concepts in Neural Networks
Authors: Charles Lovering, Ellie Pavlick
Date: 2022-07-28 00:00:00

Many complex problems are naturally understood in terms of symbolic concepts. For example, our concept of "cat" is related to our concepts of "ears" and "whiskers" in a non-arbitrary way. Fodor (1998) proposes one theory of concepts, which emphasizes symbolic representations related via constituency structures. Whether neural networks are consistent with such a theory is open for debate. We propose unit tests for evaluating whether a system's behavior is consistent with several key aspects of Fodor's criteria. Using a simple visual concept learning task, we evaluate several modern neural architectures against this specification. We find that models succeed on tests of groundedness, modularlity, and reusability of concepts, but that important questions about causality remain open. Resolving these will require new methods for analyzing models' internal states.
When BERT Fails -- The Limits of EHR Classification
Authors: Augusto Garcia-Agundez, Carsten Eickhoff
Date: 2022-07-26 00:00:00
Tasks: Classification, Readmission Prediction

Transformers are powerful text representation learners, useful for all kinds of clinical decision support tasks. Although they outperform baselines on readmission prediction, they are not infallible. Here, we look into one such failure case, and report patterns that lead to inferior predictive performance.
SDBERT: SparseDistilBERT, a faster and smaller BERT model
Authors: Devaraju Vinoda, Pawan Kumar Yadav
Date: 2022-07-28 00:00:00
Tasks: Knowledge Distillation

In this work we introduce a new transformer architecture called SparseDistilBERT (SDBERT), which is a combination of sparse attention and knowledge distillantion (KD). We implemented sparse attention mechanism to reduce quadratic dependency on input length to linear. In addition to reducing computational complexity of the model, we used knowledge distillation (KD). We were able to reduce the size of BERT model by 60% while retaining 97% performance and it only took 40% of time to train.
Generalized Attention Mechanism and Relative Position for Transformer
Authors: R. V. R. Pandya
Date: 2022-07-24 00:00:00
Tasks: Position

In this paper, we propose generalized attention mechanism (GAM) by first suggesting a new interpretation for self-attention mechanism of Vaswani et al. . Following the interpretation, we provide description for different variants of attention mechanism which together form GAM. Further, we propose a new relative position representation within the framework of GAM. This representation can be easily utilized for cases in which elements next to each other in input sequence can be at random locations in actual dataset/corpus.
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text
Authors: Oiwi Parker Jones, Brendan Shillingford
Date: 2022-07-24 00:00:00
Tasks: Reading Comprehension, Transliteration

In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically, given that there were not enough data to train an end-to-end deep learning model. One method is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach which approximately composes an FST with a recurrent neural network (RNN). We find that the hybrid approach outperforms the end-to-end FST by partitioning the original problem into one part that can be modelled by hand, using an FST, and into another part, which is easily solved by an RNN trained on the available data.