pwc-archive/papers-with-abstracts huggingface dataset:
Computing Sensitivities in Reaction Networks using Finite Difference Methods
Authors: Evan Yip, Herbert Sauro
Date: 2021-10-08 00:00:00

In this article, we investigate various numerical methods for computing scaled or logarithmic sensitivities of the form $\partial \ln y/\partial \ln x$. The methods tested include One Point, Two Point, Five Point, and the Richardson Extrapolation. The different methods were applied to a variety of mathematical functions as well as a reaction network model. The algorithms were validated by comparing results with known analytical solutions for functions and using the Reder method for computing the sensitivities in reaction networks via the Tellurium package. For evaluation, two aspects were looked at, accuracy and time taken to compute the sensitivities. Of the four methods, Richardson's extrapolation was by far the most accurate but also the slowest in terms of performance. For fast, reasonably accurate estimates, we recommend the two-point method. For most other cases where the derivatives are changing rapidly, the five-point method is a good choice, although it is three times slower than the two-point method. For ultimate accuracy which would apply particularly to very fast changing derivatives the Richardson method is without doubt the best, but it is seven-times slower than the two point method. We do not recommend the one-point method in any circumstance. The Python software that was used in the study with documentation is available at: \url{https://github.com/evanyfyip/SensitivityAnalysis}.
Role of assortativity in predicting burst synchronization using echo state network
Authors: Mousumi Roy, Abhishek Senapati, Swarup Poria, Arindam Mishra, Chittaranjan Hens
Date: 2021-10-11 00:00:00
Tasks: Time Series, Time Series Analysis

In this study, we use a reservoir computing based echo state network (ESN) to predict the collective burst synchronization of neurons. Specifically, we investigate the ability of ESN in predicting the burst synchronization of an ensemble of Rulkov neurons placed on a scale-free network. We have shown that a limited number of nodal dynamics used as input in the machine can capture the real trend of burst synchronization in this network. Further, we investigate on the proper selection of nodal inputs of degree-degree (positive and negative) correlated networks. We show that for a disassortative network, selection of different input nodes based on degree has no significant role in machine's prediction. However, in the case of assortative network, training the machine with the information (i.e time series) of low-degree nodes gives better results in predicting the burst synchronization. Finally, we explain the underlying mechanism responsible for observing this differences in prediction in a degree correlated network.
Evolutionary drivers, morphological evolution and diversity dynamics of a surviving mammal clade: cainotherioids at the Eocene--Oligocene transition
Authors: R. Weppe, Maëva Orliac, G. Guinot, Fabien L. Condamine
Date: 2021-10-11 00:00:00
Tasks: Diversity

The Eocene--Oligocene transition (EOT) represents a period of global environmental changes particularly marked in Europe and coincides with a dramatic biotic turnover. Here, using an exceptional fossil preservation, we document and analyse the diversity dynamics of a mammal clade, Cainotherioidea (Artiodactyla), that survived the EOT and radiated rapidly immediately after. We infer their diversification history from Quercy Konzentrat--Lagerst{\"a}tte (south-west France) at the species level using Bayesian birth--death models. We show that cainotherioid diversity fluctuated through time, with extinction events at the EOT and in the late Oligocene, and a major speciation burst in the early Oligocene. The latter is in line with our finding that cainotherioids had a high morphological adaptability following environmental changes throughout the EOT, which probably played a key role in the survival and evolutionary success of this clade in the aftermath. Speciation is positively associated with temperature and continental fragmentation in a time-continuous way, while extinction seems to synchronize with environmental change in a punctuated way. Within-clade interactions negatively affected the cainotherioid diversification, while inter-clade competition might explain their final decline during the late Oligocene. Our results provide a detailed dynamic picture of the evolutionary history of a mammal clade in a context of global change.
Recoverability of Ancestral Recombination Graph Topologies
Authors: Elizabeth Hayman, Anastasia Ignatieva, Jotun Hein
Date: 2021-10-10 00:00:00
Tasks: Diversity

Recombination is a powerful evolutionary process that shapes the genetic diversity observed in the populations of many species. Reconstructing genealogies in the presence of recombination from sequencing data is a very challenging problem, as this relies on mutations having occurred on the correct lineages in order to detect the recombination and resolve the placement of edges in the local trees. We investigate the probability of recovering the true topology of ancestral recombination graphs (ARGs)under the coalescent with recombination and gene conversion. We explore how sample size and mutation rate affect the inherent uncertainty in reconstructed ARGs; this sheds light on the theoretical limitations of ARG reconstruction methods. We illustrate our results using estimates of evolutionary rates for several biological organisms; in particular, we find that for parameter values that are realistic for SARS-CoV-2, the probability of reconstructing genealogies that are close to the truth is low.
The evolution of the mechanisms of SARS-CoV-2 evolution revealing vaccine-resistant mutations in Europe and America
Authors: Rui Wang, Jiahui Chen, Guo-Wei Wei
Date: 2021-10-09 00:00:00

The importance of understanding SARS-CoV-2 evolution cannot be overemphasized. Recent studies confirm that natural selection is the dominating mechanism of SARS-CoV-2 evolution, which favors mutations that strengthen viral infectivity. We demonstrate that vaccine-breakthrough or antibody-resistant mutations provide a new mechanism of viral evolution. Specifically, vaccine-resistant mutation Y449S in the spike (S) protein receptor-bonding domain (RBD), which occurred in co-mutation [Y449S, N501Y], has reduced infectivity compared to the original SARS-CoV-2 but can disrupt existing antibodies that neutralize the virus. By tracing the evolutionary trajectories of vaccine-resistant mutations in over 1.9 million SARS-CoV-2 genomes, we reveal that the occurrence and frequency of vaccine-resistant mutations correlate strongly with the vaccination rates in Europe and America. We anticipate that as a complementary transmission pathway, vaccine-resistant mutations will become a dominating mechanism of SARS-CoV-2 evolution when most of the world's population is vaccinated. Our study sheds light on SARS-CoV-2 evolution and transmission and enables the design of the next-generation mutation-proof vaccines and antibody drugs.
MAGORINO: Magnitude-only fat fraction and R2* estimation with Rician noise modelling
Authors: Timothy JP Bray, Alan Bainbridge, Margaret A Hall-Craggs, HUI ZHANG
Date: 2021-10-11 00:00:00

Purpose: Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R2* estimation where complex-based methods fail or when phase data is inaccessible or unreliable. However, traditional magnitude-based fitting algorithms do not account for Rician noise, creating a source of bias. To address these issues, we propose an algorithm for Magnitude-Only PDFF and R2* estimation with Rician Noise modelling (MAGORINO). Methods: Simulations of multi-echo gradient echo signal intensities are used to investigate the performance and behavior of MAGORINO over the space of clinically-plausible PDFF, R2* and signal-to-noise ratio (SNR) values. Fitting performance is assessed through detailed simulation, including likelihood function visualization, and in a multi-site, multi-vendor and multi-field-strength phantom dataset and in vivo. Results: Simulations show that Rician noise-based magnitude fitting outperforms existing Gaussian noise-based fitting and reveals two key mechanisms underpinning the observed improvement. Firstly, the likelihood functions exhibit two local optima; Rician noise modelling increases the chance the global optimum corresponds to the ground truth. Secondly, when the global optimum corresponds to ground truth for both noise models, the optimum from Rician noise modelling is closer to ground truth. Multisite phantom experiments show good agreement of MAGORINO PDFF with reference values, and in vivo experiments replicate the performance benefits observed in simulation. Conclusion: MAGORINO reduces Rician noise-related bias in PDFF and R2* estimation, thus addressing a key limitation of existing magnitude-only fitting methods. Our results offer an insight into the importance of the noise model for selecting the correct optimum when multiple plausible optima exist.
Quantification of collective behaviour via causality analysis
Authors: Kirill Lonhus, Dalibor Stys, Renata Rychtarikova
Date: 2021-10-10 00:00:00

Terms such as leader, mediator, and follower sound equal in the description of a pack of wolves, a street protest crowd, or a business team and have very similar meanings. This indicates the presence of some general law or structure that governs collective behaviour. To reveal this, we selected the most common parameter for most levels of the organisation -- motion. A causality analysis of distance correlations was performed to obtain follow-up networks that show who follows whom and how strongly. These networks characterise an observed system in general and work as an automation bridge between the biological experiment and the broad field of network analysis. The proposed method was tested on 3D image data from a controlled experiment on a 6-member school of aquarium fish of Tiger Barb. The network patterns can be easily ethologically interpreted and agreed with expected behaviour.
An Automated Portfolio Trading System with Feature Preprocessing and Recurrent Reinforcement Learning
Authors: Lin Li
Date: 2021-10-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We propose a novel portfolio trading system, which contains a feature preprocessing module and a trading module. The feature preprocessing module consists of various data processing operations, while in the trading part, we integrate the portfolio weight rebalance function with the trading algorithm and make the trading system fully automated and suitable for individual investors, holding a handful of stocks. The data preprocessing procedures are applied to remove the white noise in the raw data set and uncover the general pattern underlying the data set before the processed feature set is inputted into the trading algorithm. Our empirical results reveal that the proposed portfolio trading system can efficiently earn high profit and maintain a relatively low drawdown, which clearly outperforms other portfolio trading strategies.
How Robust are Limit Order Book Representations under Data Perturbation?
Authors: Yufei Wu, Mahmoud Mahfouz, Daniele Magazzeni, Manuela Veloso
Date: 2021-10-10 00:00:00

The success of machine learning models in the financial domain is highly reliant on the quality of the data representation. In this paper, we focus on the representation of limit order book data and discuss the opportunities and challenges for learning representations of such data. We also experimentally analyse the issues associated with existing representations and present a guideline for future research in this area.
Reinforcement Learning for Systematic FX Trading
Authors: Gabriel Borrageiro, Nick Firoozye, Paolo Barucca
Date: 2021-10-10 00:00:00
Tasks: Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

We explore online inductive transfer learning, with a feature representation transfer from a radial basis function network formed of Gaussian mixture model hidden processing units to a direct, recurrent reinforcement learning agent. This agent is put to work in an experiment, trading the major spot market currency pairs, where we accurately account for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to the agent via a quadratic utility, who learns to target a position directly. We improve upon earlier work by targeting a risk position in an online transfer learning context. Our agent achieves an annualised portfolio information ratio of 0.52 with a compound return of 9.3\%, net of execution and funding cost, over a 7-year test set; this is despite forcing the model to trade at the close of the trading day at 5 pm EST when trading costs are statistically the most expensive.
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
Authors: Ximei Wang, Xinyang Chen, Jianmin Wang, Mingsheng Long
Date: 2021-10-09 00:00:00
Proceeding: ICLR 2022 4
Tasks: Age Estimation, model, Object Recognition, regression, Value prediction

To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classification problems while rare attention has been paid to deep regression, which usually requires more human effort to labeling. Further, due to the intrinsic difference between categorical and continuous label space, the common intuitions for classification, e.g., cluster assumptions or pseudo labeling strategies, cannot be naturally adapted into deep regression. To this end, we first delved into the existing data-efficient methods in deep learning and found that they either encourage invariance to data stochasticity (e.g., consistency regularization under different augmentations) or model stochasticity (e.g., difference penalty for predictions of models with different dropout). To take the power of both worlds, we propose a novel X-model by simultaneously encouraging the invariance to {data stochasticity} and {model stochasticity}. Further, the X-model plays a minimax game between the feature extractor and task-specific heads to further enhance the invariance to model stochasticity. Extensive experiments verify the superiority of the X-model among various tasks, from a single-value prediction task of age estimation to a dense-value prediction task of keypoint localization, a 2D synthetic, and a 3D realistic dataset, as well as a multi-category object recognition task.
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning
Authors: Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, Pheng-Ann Heng
Date: 2021-10-09 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Continual Learning, Sensitivity

The backpropagation networks are notably susceptible to catastrophic forgetting, where networks tend to forget previously learned skills upon learning new ones. To address such the 'sensitivity-stability' dilemma, most previous efforts have been contributed to minimizing the empirical risk with different parameter regularization terms and episodic memory, but rarely exploring the usages of the weight loss landscape. In this paper, we investigate the relationship between the weight loss landscape and sensitivity-stability in the continual learning scenario, based on which, we propose a novel method, Flattening Sharpness for Dynamic Gradient Projection Memory (FS-DGPM). In particular, we introduce a soft weight to represent the importance of each basis representing past tasks in GPM, which can be adaptively learned during the learning process, so that less important bases can be dynamically released to improve the sensitivity of new skill learning. We further introduce Flattening Sharpness (FS) to reduce the generalization gap by explicitly regulating the flatness of the weight loss landscape of all seen tasks. As demonstrated empirically, our proposed method consistently outperforms baselines with the superior ability to learn new skills while alleviating forgetting effectively.
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Authors: Sayantan Kumar, Sean C. Yu, Thomas Kannampallil, Zachary Abrams, Andrew Michelson, Philip R. O. Payne
Date: 2021-10-09 00:00:00
Tasks: Deep Learning, Explainable Models, ICU Mortality, Mortality Prediction

Complex deep learning models show high prediction tasks in various clinical prediction tasks but their inherent complexity makes it more challenging to explain model predictions for clinicians and healthcare providers. Existing research on explainability of deep learning models in healthcare have two major limitations: using post-hoc explanations and using raw clinical variables as units of explanation, both of which are often difficult for human interpretation. In this work, we designed a self-explaining deep learning framework using the expert-knowledge driven clinical concepts or intermediate features as units of explanation. The self-explaining nature of our proposed model comes from generating both explanations and predictions within the same architectural framework via joint training. We tested our proposed approach on a publicly available Electronic Health Records (EHR) dataset for predicting patient mortality in the ICU. In order to analyze the performance-interpretability trade-off, we compared our proposed model with a baseline having the same set-up but without the explanation components. Experimental results suggest that adding explainability components to a deep learning framework does not impact prediction performance and the explanations generated by the model can provide insights to the clinicians to understand the possible reasons behind patient mortality.
Multi-task learning on the edge: cost-efficiency and theoretical optimality
Authors: Sami Fakhry, Romain Couillet, Malik Tiomoko
Date: 2021-10-09 00:00:00
Tasks: Multi-Task Learning

This article proposes a distributed multi-task learning (MTL) algorithm based on supervised principal component analysis (SPCA) which is: (i) theoretically optimal for Gaussian mixtures, (ii) computationally cheap and scalable. Supporting experiments on synthetic and real benchmark data demonstrate that significant energy gains can be obtained with no performance loss.
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets
Authors: Jake Grigsby, Yanjun Qi
Date: 2021-10-10 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning (RL)

Recent Offline Reinforcement Learning methods have succeeded in learning high-performance policies from fixed datasets of experience. A particularly effective approach learns to first identify and then mimic optimal decision-making strategies. Our work evaluates this method's ability to scale to vast datasets consisting almost entirely of sub-optimal noise. A thorough investigation on a custom benchmark helps identify several key challenges involved in learning from high-noise datasets. We re-purpose prioritized experience sampling to locate expert-level demonstrations among millions of low-performance samples. This modification enables offline agents to learn state-of-the-art policies in benchmark tasks using datasets where expert actions are outnumbered nearly 65:1.
Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets
Authors: Syed Rawshon Jamil
Date: 2021-10-10 00:00:00
Tasks: Classification, Time Series, Time Series Analysis, Time Series Classification, Weather Forecasting

Time Series Classification (TSC) has drawn a lot of attention in literature because of its broad range of applications for different domains, such as medical data mining, weather forecasting. Although TSC algorithms are designed for balanced datasets, most real-life time series datasets are imbalanced. The Skewed distribution is a problem for time series classification both in distance-based and feature-based algorithms under the condition of poor class separability. To address the imbalance problem, both sampling-based and algorithmic approaches are used in this paper. Different methods significantly improve time series classification's performance on imbalanced datasets. Despite having a high imbalance ratio, the result showed that F score could be as high as 97.6% for the simulated TwoPatterns Dataset.
Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide Susceptibility
Authors: Li Chen, Yulin Ding, Saeid Pirasteh, Han Hu, Qing Zhu, Haowei Zeng, Haojia Yu, Qisen Shang, Yongfei Song
Date: 2021-10-03 00:00:00
Tasks: Meta-Learning

Predicting a landslide susceptibility map (LSM) is essential for risk recognition and disaster prevention. Despite the successful application of data-driven approaches for LSM prediction, most methods generally apply a single global model to predict the LSM for an entire target region. However, in large-scale areas with significant environmental change, various parts of the region hold different landslide-inducing environments, and therefore, should be predicted with respective models. This study first segmented target scenarios into blocks for individual analysis. Then, the critical problem is that in each block with limited samples, conducting training and testing a model is impossible for a satisfactory LSM prediction, especially in dangerous mountainous areas where landslide surveying is expensive. To solve the problem, we trained an intermediate representation by the meta-learning paradigm, which is superior for capturing information valuable for few-shot adaption from LSM tasks. We hypothesized that there are more general and vital concepts concerning landslide causes and are sensitive to variations in input features. Thus, we can quickly few-shot adapt the models from the intermediate representation for different blocks or even unseen tasks using very few exemplar samples. Experimental results on the two study areas demonstrated the validity of our block-wise analysis in large scenarios and revealed the top few-shot adaption performances of the proposed methods.
2021 Drexel Society of Artificial Intelligence Research Conference
Authors: Ethan Jacob Moyer, Yigit Can Alparslan
Date: 2021-08-25 00:00:00
Tasks: BIG-bench Machine Learning

The 2021 Drexel Society of Artificial Intelligence Research Conference highlights papers focused on a broad set of papers in machine learning. This was our organizations' first annual conference. It was conducted virtually via Zoom. The highlights are currently posted on YouTube.