pwc-archive/papers-with-abstracts huggingface dataset:
Phenomenology of an in-host model of hepatitis C
Authors: Alexis Nangue, Alan D. Rendall
Date: 2022-06-10 00:00:00

This paper carries out an analysis of the global properties of solutions of an in-host model of hepatitis C for general values of its parameters. A previously unknown stable steady state on the boundary of the positive orthant is exhibited. It is proved that the model exhibits Hopf bifurcations and hence periodic solutions. A general parametrization of positive steady states is given and it is determined when the number of steady states is odd or even, according to the value of a certain basic reproductive ratio. This implies, in particular, that when this reproductive ratio is greater than one there always exists at least one positive steady state. A positive steady state which bifurcates from an infection-free state when the reproductive ratio passes through one is always stable, i.e. no backward bifurcation occurs in this model.
Some Optimisation Problems in Insurance with a Terminal Distribution Constraint
Authors: Katia Colaneri, Julia Eisenberg, Benedetta Salterini
Date: 2022-06-08 00:00:00

In this paper, we study two optimisation settings for an insurance company, under the constraint that the terminal surplus at a deterministic and finite time $T$ follows a normal distribution with a given mean and a given variance. In both cases, the surplus of the insurance company is assumed to follow a Brownian motion with drift. First, we allow the insurance company to pay dividends and seek to maximise the expected discounted dividend payments or to minimise the ruin probability under the terminal distribution constraint. Here, we find explicit expressions for the optimal strategies in both cases: in discrete and continuous time settings. Second, we let the insurance company buy a reinsurance contract for a pool of insured or a branch of business. To achieve a certain level of sustainability (i.e. the collected premia should be sufficient to buy reinsurance and to pay the occurring claims) the initial capital is set to be zero. We only allow for piecewise constant reinsurance strategies producing a normally distributed terminal surplus, whose mean and variance lead to a given Value at Risk or Expected Shortfall at some confidence level $\alpha$. We investigate the question which admissible reinsurance strategy produces a smaller ruin probability, if the ruin-checks are due at discrete deterministic points in time.
Model-based machine learning of critical brain dynamics
Authors: Hernan Bocaccio, Enzo Tagliazucchi
Date: 2022-06-10 00:00:00
Tasks: BIG-bench Machine Learning, model

Criticality can be exactly demonstrated in certain models of brain activity, yet it remains challenging to identify in empirical data. We trained a fully connected deep neural network to learn the phases of an excitable model unfolding on the anatomical connectome of human brain. This network was then applied to brain-wide fMRI data acquired during the descent from wakefulness to deep sleep. We report high correlation between the predicted proximity to the critical point and the exponents of cluster size distributions, indicative of subcritical dynamics. This result demonstrates that conceptual models can be leveraged to identify the dynamical regime of real neural systems.
On the role of theory and modeling in neuroscience
Authors: Daniel Levenstein, Veronica A. Alvarez, Asohan Amarasingham, Habiba Azab, Zhe Sage Chen, Richard C. Gerkin, Andrea Hasenstaub, Ramakrishnan Iyer, Renaud B. Jolivet, Sarah Marzen, Joseph D. Monaco, Astrid A. Prinz, Salma Quraishi, Fidel Santamaria, Sabyasachi Shivkumar, Matthew F. Singh, Roger Traub, Horacio G. Rotstein, Farzan Nadim, A. David Redish
Date: 2020-03-30 00:00:00
Tasks: Descriptive, Philosophy

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena across a wide range of scales and often requires consideration of these phenomena at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative approaches each play a distinct role in defining and bridging levels of abstraction will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.
Two-phase model of compressive stress induced on a surrounding hyperelastic medium by an expanding tumour
Authors: Gopikrishnan C. Remesan, Jennifer A Flegg, Helen M Byrne
Date: 2022-06-10 00:00:00

\emph{In vitro} experiments in which tumour cells are seeded in a gelatinous medium, or hydrogel, show how mechanical interactions between tumour cells and the tissue in which they are embedded, together with local levels of an externally-supplied, diffusible nutrient (e.g., oxygen), affect the tumour's growth dynamics. In this article, we present a mathematical model that describes these \emph{in vitro} experiments. We use the model to understand how tumour growth generates mechanical deformations in the hydrogel and how these deformations in turn influence the tumour's growth. The hydrogel is viewed as a nonlinear hyperelastic material and the tumour is modelled as a two-phase mixture, comprising a viscous tumour cell phase and an isotropic, inviscid interstitial fluid phase. Using a combination of numerical and analytical techniques, we show how the tumour's growth dynamics change as the mechanical properties of the hydrogel vary. When the hydrogel is soft, nutrient availability dominates the dynamics: the tumour evolves to a large equilibrium configuration where the proliferation rate of nutrient-rich cells on the tumour boundary balances the death rate of nutrient-starved cells in the central, necrotic core. As the hydrogel stiffness increases, mechanical resistance to growth increases and the tumour's equilibrium size decreases. Indeed, for small tumours embedded in stiff hydrogels, the inhibitory force experienced by the tumour cells may be so large that the tumour is eliminated. Analysis of the model identifies parameter regimes in which the presence of the hydrogel drives tumour elimination.
Prediction and experimental evidence of the optimisation of the angular branching process in the thallus growth of Podospora anserina
Authors: Clara Ledoux, Florence Chapeland-Leclerc, Gwenaël Ruprich-Robert, Cécilia Bobée, Christophe Lalanne, Éric Herbert, Pascal David
Date: 2022-06-10 00:00:00

Based upon apical growth and hyphal branching, the two main processes that drive the growth pattern of a fungal network, we propose here a two-dimensions simulation based on a binary-tree modelling allowing us to extract the main characteristics of a generic thallus growth. In particular, we showed that, in a homogeneous environment, the fungal growth can be optimized for exploration and exploitation of its surroundings with a specific angular distribution of apical branching. Two complementary methods of extracting angle values have been used to confront the result of the simulation with experimental data obtained from the thallus growth of the saprophytic filamentous fungus Podospora anserina. Finally, we propose here a validated model that, while being computationally low-cost, is powerful enough to test quickly multiple conditions and constraints. It will allow in future works to deepen the characterization of the growth dynamic of fungal network, in addition to laboratory experiments, that could be sometimes expensive, tedious or of limited scope.
Simulation, Modeling and Prediction of a Pharmacodynamic Animal Tissue Culture Compartment Model by Physical Informed Neural Network
Authors: Jiahao Ma
Date: 2022-06-09 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Compartment models of cell culture are widely used in cytology, pharmacology, toxicology and other fields. Numerical simulation, data modeling and prediction of compartment models can be realized by traditional differential equation modeling methods. At the same time, with the development of software and hardware, Physical Informed Neural Network (PINN) is widely used to solve differential equation models. This work models, simulates and predicts the cell culture compartment model based on the machine learning framework PyTorch with an 16 hidden layers neural network, including 8 linear layers and 8 feedback active layers. The results showed a loss value of 0.0004853 for three-component four-parameter quantitative pharmacodynamic model predictions in this way, which is evaluated by Mean Square Error (MSE). In summary, Physical Informed Neural Network can serve as an effective tool to deal with cell culture compartment models and may perform better in dealing with big datasets.
Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach
Authors: Steeven Janny, Quentin Possamai, Laurent Bako, Madiha Nadri, Christian Wolf
Date: 2022-04-19 00:00:00
Tasks: State Space Models

The identification of a nonlinear dynamic model is an open topic in control theory, especially from sparse input-output measurements. A fundamental challenge of this problem is that very few to zero prior knowledge is available on both the state and the nonlinear system model. To cope with this challenge, we investigate the effectiveness of deep learning in the modeling of dynamic systems with nonlinear behavior by advocating an approach which relies on three main ingredients: (i) we show that under some structural conditions on the to-be-identified model, the state can be expressed in function of a sequence of the past inputs and outputs; (ii) this relation which we call the state map can be modelled by resorting to the well-documented approximation power of deep neural networks; (iii) taking then advantage of existing learning schemes, a state-space model can be finally identified. After the formulation and analysis of the approach, we show its ability to identify three different nonlinear systems. The performances are evaluated in terms of open-loop prediction on test data generated in simulation as well as a real world data-set of unmanned aerial vehicle flight measurements.
Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directions
Authors: Rucha Shinde, Shruti Patil, Ketan Kotecha, Vidyasagar Potdar, Ganeshsree Selvachandran, Ajith Abraham
Date: 2022-05-30 00:00:00
Tasks: Systematic Literature Review

Healthcare systems are increasingly incorporating Artificial Intelligence into their systems, but it is not a solution for all difficulties. AI's extraordinary potential is being held back by challenges such as a lack of medical datasets for training AI models, adversarial attacks, and a lack of trust due to its black box working style. We explored how blockchain technology can improve the reliability and trustworthiness of AI-based healthcare. This paper has conducted a Systematic Literature Review to explore the state-of-the-art research studies conducted in healthcare applications developed with different AI techniques and Blockchain Technology. This systematic literature review proceeds with three different paths as natural language processing-based healthcare systems, computer vision-based healthcare systems and acoustic AI-based healthcare systems. We found that 1) Defence techniques for adversarial attacks on AI are available for specific kind of attacks and even adversarial training is AI based technique which in further prone to different attacks. 2) Blockchain can address security and privacy issues in healthcare fraternity. 3) Medical data verification and user provenance can be enabled with Blockchain. 4) Blockchain can protect distributed learning on heterogeneous medical data. 5) The issues like single point of failure, non-transparency in healthcare systems can be resolved with Blockchain. Nevertheless, it has been identified that research is at the initial stage. As a result, we have synthesized a conceptual framework using Blockchain Technology for AI-based healthcare applications that considers the needs of each NLP, Computer Vision, and Acoustic AI application. A global solution for all sort of adversarial attacks on AI based healthcare. However, this technique has significant limits and challenges that need to be addressed in future studies.
A Simple Yet Efficient Method for Adversarial Word-Substitute Attack
Authors: Tianle Li, Yi Yang
Date: 2022-05-07 00:00:00
Tasks: text-classification, Text Classification

NLP researchers propose different word-substitute black-box attacks that can fool text classification models. In such attack, an adversary keeps sending crafted adversarial queries to the target model until it can successfully achieve the intended outcome. State-of-the-art attack methods usually require hundreds or thousands of queries to find one adversarial example. In this paper, we study whether a sophisticated adversary can attack the system with much less queries. We propose a simple yet efficient method that can reduce the average number of adversarial queries by 3-30 times and maintain the attack effectiveness. This research highlights that an adversary can fool a deep NLP model with much less cost.
Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities
Authors: Aravind Sesagiri Raamkumar, Yinping Yang
Date: 2022-05-09 00:00:00
Tasks: Response Generation

Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this paper examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features.
Frictional Authors
Authors: Devlin Gualtieri
Date: 2022-05-09 00:00:00
Tasks: Friction

I present a method for text analysis based on an analogy with the dynamic friction of sliding surfaces. One surface is an array of points with a 'friction coefficient' derived from the distribution frequency of a text's alphabetic characters. The other surface is a test patch having points with this friction coefficient equal to a median value. Examples are presented from an analysis of a broad range of public domain texts, and comparison is made to the Flesch Reading Ease. Source code for the analysis program is provided.
Solution of DeBERTaV3 on CommonsenseQA
Authors: Letian Peng, Zuchao Li, Hai Zhao
Date: 2022-04-30 00:00:00
Tasks: Answer Selection, Natural Language Inference, text-classification, Text Classification

We report the performance of DeBERTaV3 on CommonsenseQA in this report. We simply formalize the answer selection as a text classification for DeBERTaV3. The strong natural language inference ability of DeBERTaV3 helps its single and ensemble model set the new (w/o external knowledge) state-of-the-art on CommonsenseQA.
The Construction and Evaluation of the LEAFTOP Dataset of Automatically Extracted Nouns in 1480 Languages
Authors: Greg Baker, Diego Molla-Aliod
Date: 2022-05-09 00:00:00

The LEAFTOP (language extracted automatically from thousands of passages) dataset consists of nouns that appear in multiple places in the four gospels of the New Testament. We use a naive approach -- probabilistic inference -- to identify likely translations in 1480 other languages. We evaluate this process and find that it provides lexiconaries with accuracy from 42% (Korafe) to 99% (Runyankole), averaging 72% correct across evaluated languages. The process translates up to 161 distinct lemmas from Koine Greek (average 159). We identify nouns which appear to be easy and hard to translate, language families where this technique works, and future possible improvements and extensions. The claims to novelty are: the use of a Koine Greek New Testament as the source language; using a fully-annotated manually-created grammatically parse of the source text; a custom scraper for texts in the target languages; a new metric for language similarity; a novel strategy for evaluation on low-resource languages.
gDDIM: Generalized denoising diffusion implicit models
Authors: Qinsheng Zhang, Molei Tao, Yongxin Chen
Date: 2022-06-11 00:00:00
Tasks: Denoising

Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective. We discover that the DDIM can be obtained by using some specific approximations of the score when solving the corresponding stochastic differential equation. We present an interpretation of the accelerating effects of DDIM that also explains the advantages of a deterministic sampling scheme over the stochastic one for fast sampling. Building on this insight, we extend DDIM to general DMs, coined generalized DDIM (gDDIM), with a small but delicate modification in parameterizing the score network. We validate gDDIM in two non-isotropic DMs: Blurring diffusion model (BDM) and Critically-damped Langevin diffusion model (CLD). We observe more than 20 times acceleration in BDM. In the CLD, a diffusion model by augmenting the diffusion process with velocity, our algorithm achieves an FID score of 2.26, on CIFAR10, with only 50 number of score function evaluations~(NFEs) and an FID score of 2.86 with only 27 NFEs. Code is available at https://github.com/qsh-zh/gDDIM
Cooperation between Independent Market Makers
Authors: Bingyan Han
Date: 2022-06-11 00:00:00
Tasks: Q-Learning

With the digitalization of the financial market, dealers are increasingly handling market-making activities by algorithms. Recent antitrust literature raises concerns on collusion caused by artificial intelligence. This paper studies the possibility of cooperation between market makers via independent Q-learning. Market making with inventory risk is formulated as a repeated general-sum game. Under a stag-hunt type payoff, we find that market makers can learn cooperative strategies without communication. In general, high spreads can have the largest probability even when the lowest spread is the unique Nash equilibrium. Moreover, introducing more agents into the game does not necessarily eliminate the presence of supra-competitive spreads.
Intra Encoding Complexity Control with a Time-Cost Model for Versatile Video Coding
Authors: Yan Huang, Jizheng Xu, Li Zhang, Yan Zhao, Li Song
Date: 2022-06-13 00:00:00

For the latest video coding standard Versatile Video Coding (VVC), the encoding complexity is much higher than previous video coding standards to achieve a better coding efficiency, especially for intra coding. The complexity becomes a major barrier of its deployment and use. Even with many fast encoding algorithms, it is still practically important to control the encoding complexity to a given level. Inspired by rate control algorithms, we propose a scheme to precisely control the intra encoding complexity of VVC. In the proposed scheme, a Time-PlanarCost (viz. Time-Cost, or T-C) model is utilized for CTU encoding time estimation. By combining a set of predefined parameters and the T-C model, CTU-level complexity can be roughly controlled. Then to achieve a precise picture-level complexity control, a framework is constructed including uneven complexity pre-allocation, preset selection and feedback. Experimental results show that, for the challenging intra coding scenario, the complexity error quickly converges to under 3.21%, while keeping a reasonable time saving and rate-distortion (RD) performance. This proves the efficiency of the proposed methods.
Deep Learning-Based MR Image Re-parameterization
Authors: Abhijeet Narang, Abhigyan Raj, Mihaela Pop, Mehran Ebrahimi
Date: 2022-06-11 00:00:00
Tasks: Deep Learning

Magnetic resonance (MR) image re-parameterization refers to the process of generating via simulations of an MR image with a new set of MRI scanning parameters. Different parameter values generate distinct contrast between different tissues, helping identify pathologic tissue. Typically, more than one scan is required for diagnosis; however, acquiring repeated scans can be costly, time-consuming, and difficult for patients. Thus, using MR image re-parameterization to predict and estimate the contrast in these imaging scans can be an effective alternative. In this work, we propose a novel deep learning (DL) based convolutional model for MRI re-parameterization. Based on our preliminary results, DL-based techniques hold the potential to learn the non-linearities that govern the re-parameterization.
Strategies to Maintain Voltage on Long, Lightly Loaded Feeders with Widespread Residential Level 2 Plug-in Electric Vehicle Charging
Authors: Don Scoffield, John Smart, Timothy Pennington, C. Birk Jones, Matthew Lave, Anudeep Medam, Bhaskar Mitra
Date: 2022-06-11 00:00:00

Long, lightly loaded feeders serving residential loads may begin to experience voltage excursions as plug-in electric vehicle (PEV) penetration increases. Residential PEV charging tends to occur during peak-load hours on residential feeders, leading to increased peak loads and potential voltage excursions. To avoid voltage excursions, two PEV charging control strategies were investigated using the IEEE 34-bus feeder. The first strategy shifts PEV charging energy from peak hours to off-peak hours; the other strategy allows PEVs to provide reactive power support. Undervoltage excursions seen in a simulation of uncontrolled charging of 200 PEVs were improved dramatically when these two control strategies were used. The minimum voltage on the feeder improved from 0.855 pu when PEV charging was uncontrolled to 0.959 pu when both control strategies were applied together.