pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic failure of cell infection post viral entry: Implications for infection outcomes and antiviral therapy
Authors: Christian Quirouette, Daniel Cresta, Jizhou Li, Kathleen P. Wilkie, Haozhao Liang, Catherine A. A. Beauchemin
Date: 2022-08-01 00:00:00

A virus infection can be initiated with very few or even a single infectious virion, and as such can become extinct, i.e. stochastically fail to take hold or spread significantly. There are many ways that a fully competent infectious virion, having successfully entered a cell, can fail to cause a productive infection, i.e. one that yields infectious virus progeny. Though many discrete, stochastic mathematical models (DSMs) have been developed and used to estimate a virus infection's extinction probability, these typically neglect infection failure post viral entry. The DSM presented herein introduces parameter $\gamma\in(0,1]$ which corresponds to the probability that a virion's entry into a cell will result in a productive cell infection. We derive an expression for the likelihood of infection extinction in this new DSM, and find that prophylactic therapy with an antiviral acting to reduce $\gamma$ is best at increasing an infection's extinction probability, compared to antivirals acting on the rates of virus production or virus entry into cells. Using the DSM, we investigate the difference in the fraction of cells consumed by so-called extinct versus established virus infections, and find that this distinction becomes biologically meaningless as the probability of extinction approaches 100%. We show that infections wherein virus is release by an infected cell as a single burst, rather than at a constant rate over the cell's infectious lifespan, has the same probability of infection extinction, despite previous claims to this effect [Pearson 2011, doi:10.1371/journal.pcbi.1001058]. Instead, extending previous work by others [Yan 2016, doi:10.1007/s00285-015-0961-5], we show how the assumed distribution for the stochastic virus burst size, affects the extinction probability and associated critical antiviral efficacy.
Computational Modelling of Plasticity-Led Evolution
Authors: Eden Tian Hwa Ng, Akira R. Kinjo
Date: 2022-08-01 00:00:00

Plasticity-led evolution is a form of evolution where a change in the environment induces novel traits via phenotypic plasticity, after which the novel traits are genetically accommodated over generations under the novel environment. This mode of evolution is expected to resolve the problem of gradualism (i.e., evolution by the slow accumulation of mutations that induce phenotypic variation) implied by the Modern Evolutionary Synthesis, in the face of a large environmental change. While experimental works are essential for validating that plasticity-led evolution indeed happened, we need computational models to gain insight into its underlying mechanisms and make qualitative predictions. Such computational models should include the developmental process and gene-environment interactions in addition to genetics and natural selection. We point out that gene regulatory network models can incorporate all the above notions. In this review, we highlight results from computational modelling of gene regulatory networks that consolidate the criteria of plasticity-led evolution. Since gene regulatory networks are mathematically equivalent to artificial recurrent neural networks, we also discuss their analogies and discrepancies, which may help further understand the mechanisms underlying plasticity-led evolution.
Network strategies to study Epstein-Barr virus associated carcinomas and potential etiological mechanisms for oncogenesis
Authors: S. Chatterjee, B. S. Sanjeev
Date: 2022-07-31 00:00:00
Tasks: Community Detection

Diseased conditions are a consequence of some abnormality that are associated with clinical conditions in numerous cells and tissues affecting various organs. The common role of EBV (Epstein-Barr virus) in causing infectious mononucleosis (IM) affecting B-cells and epithelial cells and the development of EBV-associated cancers has been an area of active research. Investigating such significant interactions may help discover new therapeutic targets for certain EBV-associated lymphoproliferative (Burkitt's Lymphoma and Hodgkin's Lymphoma) and non-lymphoproliferative diseases (Gastric cancer and Nasopharyngeal cancer). Based on the DisGeNET (v7.0) data set, we constructed a disease-gene network bipartite graph to identify genes that are involved in various carcinomas namely, gastric cancer (GC), nasopharyngeal cancer (NPC), Hodgkin's lymphoma (HL) and Burkitt's lymphoma (BL). Using the community detection algorithm (Louvain method), we identified communities followed by functional enrichment using over-representation analysis methodology. In this study, we identified the modular communities to explore the relation of this common causative pathogen (EBV) with different carcinomas such as GC, NPC, HL and BL. We could identify the top 10 genes as CASP10, BRAF, NFKBIA, IFNA2, GSTP1, CSF3, GATA3, UBR5, AXIN2 and POLE based on their degree of distribution. Further over-representation analysis showed that the ABL1 gene was significantly over-represented in 3 out of 9 critical biological processes. As a result, we can infer that the EBV pathogen is selective in targeting critical pathways to bring about cellular growth arrest/apoptosis and interfering with vital biological processes, including the TP53 network of genes that leads to further proliferation of damage to vital cellular activities.
Modeling the Role that Habitat Overlap Shape has on the Spread of Brucellosis in the Greater Yellowstone Ecosystem
Authors: Dustin Padilla
Date: 2022-07-31 00:00:00
Tasks: Management

Brucellosis is a zoonotic bacterial infectious disease that affects livestock and wildlife. The Greater Yellowstone Ecosystem is the last area in the United States where cattle are regularly infected with brucellosis. Even though livestock are vaccinated, interactions with reservoir species still result in spill-over cases to cattle. The National Academy of Sciences, Engineering, and Medicine has indicated that modeling efforts should focus on the transmission between elk and cattle and should be conducted to better understand the effects of land-use change and landscape configuration on disease risk. This chapter determines how the landscape's configuration, using the shape and amount of habitat overlap between elk and cattle, contributes to cross-species brucellosis transmission, and how land-use change translates to disease prevalence. A mathematical-epidemiological model is combined with landscape ecology metrics to estimate transmission rates between the species and model disease spread. The results of this study can ultimately help stakeholders develop policy for controlling brucellosis transmission between livestock and elk in the Greater Yellowstone Area. In turn, this could lead to less disease prevalence, reduce associated costs, and assist in population management.
Amino Acid Classification in 2D NMR Spectra via Acoustic Signal Embeddings
Authors: Jia Qi Yip, Dianwen Ng, Bin Ma, Konstantin Pervushin, Eng Siong Chng
Date: 2022-08-01 00:00:00
Tasks: Speaker Verification

Nuclear Magnetic Resonance (NMR) is used in structural biology to experimentally determine the structure of proteins, which is used in many areas of biology and is an important part of drug development. Unfortunately, NMR data can cost thousands of dollars per sample to collect and it can take a specialist weeks to assign the observed resonances to specific chemical groups. There has thus been growing interest in the NMR community to use deep learning to automate NMR data annotation. Due to similarities between NMR and audio data, we propose that methods used in acoustic signal processing can be applied to NMR as well. Using a simulated amino acid dataset, we show that by swapping out filter banks with a trainable convolutional encoder, acoustic signal embeddings from speaker verification models can be used for amino acid classification in 2D NMR spectra by treating each amino acid as a unique speaker. On an NMR dataset comparable in size with of 46 hours of audio, we achieve a classification performance of 97.7% on a 20-class problem. We also achieve a 23% relative improvement by using an acoustic embedding model compared to an existing NMR-based model.
Choice of spatial discretisation influences the progression of viral infection within multicellular tissues
Authors: Thomas Williams, James McCaw, James Osborne
Date: 2022-08-01 00:00:00

There has been an increasing recognition of the utility of models of the spatial dynamics of viral spread within tissues. Multicellular models, where cells are represented as discrete regions of space coupled to a virus density surface, are a popular approach to capture these dynamics. Conventionally, such models are simulated by discretising the viral surface and depending on the rate of viral diffusion and other considerations, a finer or coarser discretisation may be used. The impact that this choice may have on the behaviour of the system has not been studied. Here we demonstrate that, if rates of viral diffusion are small, then the choice of spatial discretisation of the viral surface can have quantitative and even qualitative influence on model outputs. We investigate in detail the mechanisms driving these phenomena and discuss the constraints on the design and implementation of multicellular viral dynamics models for different parameter configurations.
Sampling the proteome by emerging single-molecule and mass-spectrometry methods
Authors: Michael J. MacCoss, Javier Alfaro, Meni Wanunu, Danielle A. Faivre, Nikolai Slavov
Date: 2022-07-31 00:00:00

Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules. This larger number of molecules and the associated larger dynamic range have major implications in the development of proteomics technologies. We examine these implications for both liquid chromatography-tandem mass spectrometry (LC-MS/MS) and single-molecule counting and provide estimates on how many molecules are routinely measured in proteomics experiments by LC-MS/MS. We review strategies that have been helpful for counting billions of protein molecules by LC-MS/MS and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome. We also examine the theoretical possibilities for scaling up single-molecule and mass spectrometry proteomics approaches to quantifying the billions of protein molecules that make up the proteomes of our cells.
Solving the optimal stopping problem with reinforcement learning: an application in financial option exercise
Authors: Leonardo Kanashiro Felizardo, Elia Matsumoto, Emilio Del-Moral-Hernandez
Date: 2022-07-21 00:00:00
Tasks: Management, Reinforcement Learning (RL), Time Series Analysis

The optimal stopping problem is a category of decision problems with a specific constrained configuration. It is relevant to various real-world applications such as finance and management. To solve the optimal stopping problem, state-of-the-art algorithms in dynamic programming, such as the least-squares Monte Carlo (LSMC), are employed. This type of algorithm relies on path simulations using only the last price of the underlying asset as a state representation. Also, the LSMC was thinking for option valuation where risk-neutral probabilities can be employed to account for uncertainty. However, the general optimal stopping problem goals may not fit the requirements of the LSMC showing auto-correlated prices. We employ a data-driven method that uses Monte Carlo simulation to train and test artificial neural networks (ANN) to solve the optimal stopping problem. Using ANN to solve decision problems is not entirely new. We propose a different architecture that uses convolutional neural networks (CNN) to deal with the dimensionality problem that arises when we transform the whole history of prices into a Markovian state. We present experiments that indicate that our proposed architecture improves results over the previous implementations under specific simulated time series function sets. Lastly, we employ our proposed method to compare the optimal exercise of the financial options problem with the LSMC algorithm. Our experiments show that our method can capture more accurate exercise opportunities when compared to the LSMC. We have outstandingly higher (above 974\% improvement) expected payoff from these exercise policies under the many Monte Carlo simulations that used the real-world return database on the out-of-sample (test) data.
How Covid mobility restrictions modified the population of investors in Italian stock markets
Authors: Paola Deriu, Fabrizio Lillo, Piero Mazzarisi, Francesca Medda, Adele Ravagnani, Antonio Russo
Date: 2022-07-30 00:00:00

This paper investigates how Covid mobility restrictions impacted the population of investors of the Italian stock market. The analysis tracks the trading activity of individual investors in Italian stocks in the period January 2019-September 2021, investigating how their composition and the trading activity changed around the Covid-19 lockdown period (March 9 - May 19, 2020) and more generally in the period of the pandemic. The results pinpoint that the lockdown restriction was accompanied by a surge in interest toward stock market, as testified by the trading volume by households. Given the generically falling prices during the lockdown, the households, which are typically contrarian, were net buyers, even if less than expected from their trading activity in 2019. This can be explained by the arrival, during the lockdown, of a group of about 185k new investors (i.e. which had never traded since January 2019) which were on average ten year younger and with a larger fraction of males than the pre-lockdown investors. By looking at the gross P&L, there is clear evidence that these new investors were more skilled in trading. There are thus indications that the lockdown, and more generally the Covid pandemic, created a sort of regime change in the population of financial investors.
D3Former: Debiased Dual Distilled Transformer for Incremental Learning
Authors: Abdelrahman Mohamed, Rushali Grandhe, K J Joseph, Salman Khan, Fahad Khan
Date: 2022-07-25 00:00:00
Tasks: class-incremental learning, Continual Learning, Incremental Learning

In class incremental learning (CIL) setting, groups of classes are introduced to a model in each learning phase. The goal is to learn a unified model performant on all the classes observed so far. Given the recent popularity of Vision Transformers (ViTs) in conventional classification settings, an interesting question is to study their continual learning behaviour. In this work, we develop a Debiased Dual Distilled Transformer for CIL dubbed $\textrm{D}^3\textrm{Former}$. The proposed model leverages a hybrid nested ViT design to ensure data efficiency and scalability to small as well as large datasets. In contrast to a recent ViT based CIL approach, our $\textrm{D}^3\textrm{Former}$ does not dynamically expand its architecture when new tasks are learned and remains suitable for a large number of incremental tasks. The improved CIL behaviour of $\textrm{D}^3\textrm{Former}$ owes to two fundamental changes to the ViT design. First, we treat the incremental learning as a long-tail classification problem where the majority samples from new classes vastly outnumber the limited exemplars available for old classes. To avoid the bias against the minority old classes, we propose to dynamically adjust logits to emphasize on retaining the representations relevant to old tasks. Second, we propose to preserve the configuration of spatial attention maps as the learning progresses across tasks. This helps in reducing catastrophic forgetting by constraining the model to retain the attention on the most discriminative regions. $\textrm{D}^3\textrm{Former}$ obtains favorable results on incremental versions of CIFAR-100, MNIST, SVHN, and ImageNet datasets. Code is available at https://tinyurl.com/d3former
Tutorial on the development of AI models for medical image analysis
Authors: Thijs Kooi
Date: 2022-07-14 00:00:00
Tasks: Medical Image Analysis

The idea of using computers to read medical scans was introduced as early as 1966. However, limits to machine learning technology meant progress was slow initially. The Alexnet breakthrough in 2012 sparked new interest in the topic, which resulted in the release of 100s of medical AI solutions on the market. In spite of success for some diseases and modalities, many challenges remain. Research typically focuses on the development of specific applications or techniques, clinical evaluation, or meta analysis of clinical studies or techniques through surveys or challenges. However, limited attention has been given to the development process of improving real world performance. In this tutorial, we address the latter and discuss some techniques to conduct the development process in order to make this as efficient as possible.
Few-Shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext Tasks
Authors: Ji, H (Ji, Hong) [1] ; Gao, Z (Gao, Zhi) [1] ; Zhang, YJ (Zhang, Yongjun) [1] ; Wan, Y (Wan, Yu) [1] ; Li, C (Li, Can) [1] ; Mei, TC (Mei, Tiancan)
Date: 2022-07-06 00:00:00
Proceeding: journal 2022 7
Tasks: Contrastive Learning, Few-Shot Learning, Scene Classification

Small data hold big artificial intelligence (AI) potential. As one of the promising small data AI approaches, few-shot learning has the goal to learn a model efficiently that can recognize novel classes with extremely limited training samples. Therefore, it is critical to accumulate useful prior knowledge obtained from large-scale base class dataset. To realize few-shot scene classification of optical remote sensing images, we start from a baseline model that trains all base classes using a standard cross-entropy loss leveraging two auxiliary objectives to capture intrinsical characteristics across the semantic classes. Specifically, rotation prediction learns to recognize the 2-D rotation of an input to guide the learning of class-transferable knowledge, and contrastive learning aims to pull together the positive pairs while pushing apart the negative pairs to promote intraclass consistency and interclass inconsistency. We jointly optimize two such pretext tasks and semantic class prediction task in an end-to-end manner. To further overcome the overfitting issue, we introduce a regularization technique, adversarial model perturbation, to calibrate the pretext tasks so as to enhance the generalization ability. Extensive experiments on public remote sensing benchmarks including Northwestern Polytechnical University (NWPU)-RESISC45, aerial image dataset (AID), and Wuhan University (WHU)-remote sensing (RS)-19 demonstrate that our method works effectively and achieves best performance that significantly outperforms many state-of-the-art approaches.
AutoQubo: data-driven automatic QUBO generation
Authors: Alberto Moraglio, Serban Georgescu, Przemysław Sadowski
Date: 2022-07-19 00:00:00
Proceeding: Genetic and Evolutionary Computation Conference (GECCO) 2022 7

This paper presents a general method to derive automatically a Quadratic Unconstrained Binary Optimisation (QUBO) formulation of, in principle, any combinatorial optimisation problem from its high-level description given in any programming language in a free format. Currently ad-hoc approaches to formulating a problem at a time as a QUBO are the norm. The proposed method goes beyond the state-of-the-art as it is general in its applicability. The method is data-driven and based on sampling, but the obtained QUBO is exact rather than only an approximation of the original problem for optimisation problems occurring in practice. The method is of particular interest in the context of Quantum Ising Machines - quantum computers specialised to optimisation tasks. It can be understood as a compiler that makes these machines usable without specific expertise by automating the translation of high-level description of combinatorial optimisation problems to a low-level format suitable for the quantum hardware.
Bayesian Variable Selection in a Million Dimensions
Authors: Martin Jankowiak
Date: 2022-08-02 00:00:00
Tasks: regression, Variable Selection

Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty. However, wider adoption of Bayesian variable selection has been hampered by computational challenges, especially in difficult regimes with a large number of covariates P or non-conjugate likelihoods. To scale to the large P regime we introduce an efficient MCMC scheme whose cost per iteration is sublinear in P. In addition we show how this scheme can be extended to generalized linear models for count data, which are prevalent in biology, ecology, economics, and beyond. In particular we design efficient algorithms for variable selection in binomial and negative binomial regression, which includes logistic regression as a special case. In experiments we demonstrate the effectiveness of our methods, including on cancer and maize genomic data.
COMET: Coverage-guided Model Generation For Deep Learning Library Testing
Authors: Meiziniu Li, Jialun Cao, Yongqiang Tian, Tsz On Li, Ming Wen, Shing-Chi Cheung
Date: 2022-08-02 00:00:00
Tasks: Deep Learning, Diversity

Recent deep learning (DL) applications are mostly built on top of DL libraries. The quality assurance of these libraries is critical to the dependable deployment of DL applications. Techniques have been proposed to generate various DL models and apply them to test these libraries. However, their test effectiveness is constrained by the diversity of layer API calls in their generated DL models. Our study reveals that these techniques can cover at most 34.1% layer inputs, 25.9% layer parameter values, and 15.6% layer sequences. As a result, we find that many bugs arising from specific layer API calls (i.e., specific layer inputs, parameter values, or layer sequences) can be missed by existing techniques. Because of this limitation, we propose COMET to effectively generate DL models with diverse layer API calls for DL library testing. COMET: (1) designs a set of mutation operators and a coverage-based search algorithm to diversify layer inputs, layer parameter values, and layer sequences in DL models. (2) proposes a model synthesis method to boost the test efficiency without compromising the layer API call diversity. Our evaluation result shows that COMET outperforms baselines by covering twice as many layer inputs (69.7% vs. 34.1%), layer parameter values (50.2% vs. 25.9%), and layer sequences (39.0% vs. 15.6%) as those by the state-of-the-art. Moreover, COMET covers 3.4% more library branches than those by existing techniques. Finally, COMET detects 32 new bugs in the latest version of eight popular DL libraries, including TensorFlow and MXNet, with 21 of them confirmed by DL library developers and 7 of those confirmed bugs have been fixed by developers.