pwc-archive/papers-with-abstracts huggingface dataset:
Three-way Mixed Effect ANOVA to Estimate MRMC Limits of Agreement
Authors: Si Wen, Brandon D. Gallas
Date: 2021-07-19 00:00:00

When evaluating the clinical performance of a medical imaging device, a multi-reader multi-case (MRMC) analysis is usually applied to account for both case and reader variability. For a clinical task that equates to a quantitative measurement, an agreement analysis such as a limits of agreement (LOA) method can be used to compare different measurement methods. In this work, we introduce four types of comparisons; these types differ depending on whether the measurements are within or between readers and within or between modalities. A three-way mixed effect ANOVA model is applied to estimate the variances of individual differences, which is an essential step for estimating LOA. To verify the estimates of LOA, we propose a hierarchical model to simulate quantitative MRMC data. Two simulation studies were conducted to validate both the simulation and the LOA variance estimates. From the simulation results, we can conclude that our estimate of variance is unbiased, and the uncertainty of the estimation drops as the number of readers and cases increases and rises as the value of true variance increases.
A new method of carbon budget analysis with application to present and paleo atmospheric concentration data sets
Authors: Alice Nadeau, Richard McGehee, Clarence Lehman
Date: 2021-07-19 00:00:00

We introduce a new method for analyzing the carbon budget using box models and a mass balance approach. The method describes the net flow of carbon between the atmosphere and other reservoirs. The method assumes that the data can be explained by having the carbon move in and out of the atmosphere through two reservoirs, one consisting of isotopically light carbon (biotic) and the other consisting of isotopically heavy carbon (abiotic). The systems are underdetermined from the data, so the Occam's razor approach is to assume that the least amount of carbon moves between the atmosphere and the reservoirs. As data from known sources and sinks are added to the model, one can determine the constraints on the unknown sources and sinks. To illustrate the method, we analyze data from the Mauna Loa Observatory and data from Antarctic ice cores.
Characterising mosquito biting behaviour at high resolution
Authors: Gregory PD Murray, Emilie Giraud, Felix JH Hol
Date: 2021-07-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Blood feeding represents a critical event in the life cycle of female mosquitoes. In addition to providing nutrients to the mosquito, blood feeding facilitates the transmission of parasites and viruses to hosts, potentially having devastating health consequences. Despite this, our understanding of these short, yet important bouts of behaviour is incomplete. How and where a mosquito decides to feed and the success of feeding can influence the transmission of pathogens, while a more thorough understanding may allow interventions to reduce or prevent infections. Recent advances in machine vision and automated tracking presents the opportunity to observe and understand blood feeding behaviour of mosquitoes at unprecedented spatial and temporal resolution. Here, we combine these technologies with novel designs for behavioural arenas and controllable artificial host cues to enable detailed observations of biting behaviour using relatively inexpensive and readily available materials. Combined with a workflow for quantitative image analysis, we are able to describe nuanced, high resolution biting behaviour under tightly controlled conditions.
Multiscale modelling of desquamation in the interfollicular epidermis
Authors: Claire Miller, Edmund Crampin, James Osborne
Date: 2021-07-19 00:00:00

Maintenance of epidermal thickness is critical to the barrier function of the skin. Decreased tissue thickness, specifically in the stratum corneum (the outermost layer of the tissue), causes discomfort and inflammation, and is related to several severe diseases of the tissue. In order to maintain both stratum corneum thickness and overall tissue thickness it is necessary for the system to balance cell proliferation and cell loss. Cell proliferation in the epidermis occurs in the basal layer and causes constant upwards movement in the tissue. Cell loss occurs when dead cells at the top of the tissue are lost to the environment through a process called desquamation. Desquamation is thought to occur through a gradual reduction in adhesion between cells, due to the cleaving of adhesion proteins by enzymes, in the stratum corneum. In this paper we will investigate combining a (mass action) subcellular model of desquamation with a three dimensional (cell centre based) multicellular model of the interfollicular epidermis to better understand maintenance of epidermal thickness. These investigations show that hypothesised biological models for the degradation of cell-cell adhesion from the literature are able to provide a consistent rate of cell loss in the multicellular model. This loss balances proliferation, and hence maintains a homeostatic tissue thickness. Moreover, we find that multiple proliferative cell populations in the basal layer can be represented by a single proliferative cell population, simplifying investigations with this model. The model is used to investigate a disorder (Netherton Syndrome) which disrupts desquamation. The model shows how biochemical changes can cause disruptions to the tissue, resulting in a reduced tissue thickness and consequently diminishing the protective role of the tissue. A hypothetical treatment result is also investigated. [ABR]
A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection
Authors: Wei Li, Florentina Paraschiv, Georgios Sermpinis
Date: 2021-07-19 00:00:00

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.
A characterisation of cross-impact kernels
Authors: Mathieu Rosenbaum, Mehdi Tomas
Date: 2021-07-19 00:00:00

Trading a financial asset pushes its price as well as the prices of other assets, a phenomenon known as cross-impact. We consider a general class of kernel-based cross-impact models and investigate suitable parameterisations for trading purposes. We focus on kernels that guarantee that prices are martingales and anticipate future order flow (martingale-admissible kernels) and those that ensure there is no possible price manipulation (no-statistical-arbitrage-admissible kernels). We determine the overlap between these two classes and provide formulas for calibration of cross-impact kernels on data. We illustrate our results using SP500 futures data.
A New Attempt to Identify Long-term Precursors for Endogenous Financial Crises in the Market Correlation Structures
Authors: Anton J. Heckens, Thomas Guhr
Date: 2021-07-19 00:00:00

Prediction of events in financial markets is every investor's dream and, usually, wishful thinking. From a more general, economic and societal viewpoint, the identification of indicators for large events is highly desirable to assess systemic risks. Unfortunately, the very nature of financial markets, particularly the predominantly non-Markovian character as well as non-stationarity, make this challenge a formidable one, leaving little hope for fully fledged answers. Nevertheless, it is called for to collect pieces of evidence in a variety of observables to be assembled like the pieces of a puzzle that eventually might help to catch a glimpse of long-term indicators or precursors for large events - if at all in a statistical sense. Here, we present a new piece for this puzzle. We use the quasi-stationary market states which exist in the time evolution of the correlation structure in financial markets. Recently, we identified such market states relative to the collective motion of the market as a whole. We study their precursor properties in the US stock markets over 16 years, including two endogenous crises, the dot-com bubble burst and the pre-phase of the Lehman Brothers crash. We identify certain interesting features and critically discuss their suitability as indicators.
The Future will be Different than Today: Model Evaluation Considerations when Developing Translational Clinical Biomarker
Authors: Yichen Lu, Jane Fridlyand, Tiffany Tang, Ting Qi, Noah Simon, Ning Leng
Date: 2021-07-13 00:00:00

Finding translational biomarkers stands center stage of the future of personalized medicine in healthcare. We observed notable challenges in identifying robust biomarkers as some with great performance in one scenario often fail to perform well in new trials (e.g. different population, indications). With rapid development in the clinical trial world (e.g. assay, disease definition), new trials very likely differ from legacy ones in many perspectives and in development of biomarkers this heterogeneity should be considered. In response, we recommend considering building in the heterogeneity when evaluating biomarkers. In this paper, we present one evaluation strategy by using leave-one-study-out (LOSO) in place of conventional cross-validation (cv) methods to account for the potential heterogeneity across trials used for building and testing the biomarkers. To demonstrate the performance of K-fold vs LOSO cv in estimating the effect size of biomarkers, we leveraged data from clinical trials and simulation studies. In our assessment, LOSO cv provided a more objective estimate of the future performance. This conclusion remained true across different evaluation metrics and different statistical methods.
Analysing Cyberbullying using Natural Language Processing by Understanding Jargon in Social Media
Authors: Bhumika Bhatia, Anuj Verma, Anjum, Rahul Katarya
Date: 2021-04-23 00:00:00
Tasks: Binary Classification

Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, cyberbullying amongst children and other vulnerable groups are only growing over online classes, and increased access to social platforms, especially post COVID-19. It is paramount to detect and ensure minors' safety across social platforms so that any violence or hate-crime is automatically detected and strict action is taken against it. In our work, we explore binary classification by using a combination of datasets from various social media platforms that cover a wide range of cyberbullying such as sexism, racism, abusive, and hate-speech. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus, achieving a higher precision in comparison to models without slang preprocessing.
Boost-R: Gradient Boosted Trees for Recurrence Data
Authors: Xiao Liu, Rong pan
Date: 2021-07-03 00:00:00
Tasks: regression

Recurrence data arise from multi-disciplinary domains spanning reliability, cyber security, healthcare, online retailing, etc. This paper investigates an additive-tree-based approach, known as Boost-R (Boosting for Recurrence Data), for recurrent event data with both static and dynamic features. Boost-R constructs an ensemble of gradient boosted additive trees to estimate the cumulative intensity function of the recurrent event process, where a new tree is added to the ensemble by minimizing the regularized L2 distance between the observed and predicted cumulative intensity. Unlike conventional regression trees, a time-dependent function is constructed by Boost-R on each tree leaf. The sum of these functions, from multiple trees, yields the ensemble estimator of the cumulative intensity. The divide-and-conquer nature of tree-based methods is appealing when hidden sub-populations exist within a heterogeneous population. The non-parametric nature of regression trees helps to avoid parametric assumptions on the complex interactions between event processes and features. Critical insights and advantages of Boost-R are investigated through comprehensive numerical examples. Datasets and computer code of Boost-R are made available on GitHub. To our best knowledge, Boost-R is the first gradient boosted additive-tree-based approach for modeling large-scale recurrent event data with both static and dynamic feature information.
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Authors: Jongjin Park, Sukmin Yun, Jongheon Jeong, Jinwoo Shin
Date: 2021-06-29 00:00:00
Tasks: Contrastive Learning, Representation Learning

Semi-supervised learning (SSL) has been a powerful strategy to incorporate few labels in learning better representations. In this paper, we focus on a practical scenario that one aims to apply SSL when unlabeled data may contain out-of-class samples - those that cannot have one-hot encoded labels from a closed-set of classes in label data, i.e., the unlabeled data is an open-set. Specifically, we introduce OpenCoS, a simple framework for handling this realistic semi-supervised learning scenario based upon a recent framework of self-supervised visual representation learning. We first observe that the out-of-class samples in the open-set unlabeled dataset can be identified effectively via self-supervised contrastive learning. Then, OpenCoS utilizes this information to overcome the failure modes in the existing state-of-the-art semi-supervised methods, by utilizing one-hot pseudo-labels and soft-labels for the identified in- and out-of-class unlabeled data, respectively. Our extensive experimental results show the effectiveness of OpenCoS under the presence of out-of-class samples, fixing up the state-of-the-art semi-supervised methods to be suitable for diverse scenarios involving open-set unlabeled data.
Selective Focusing Learning in Conditional GANs
Authors: Kyeongbo Kong, Kyunghun Kim, Woo-Jin Song, Suk-Ju Kang
Date: 2021-07-08 00:00:00
Tasks: Diversity

Conditional generative adversarial networks (cGANs) have demonstrated remarkable success due to their class-wise controllability and superior quality for complex generation tasks. Typical cGANs solve the joint distribution matching problem by decomposing two easier sub-problems: marginal matching and conditional matching. From our toy experiments, we found that it is the best to apply only conditional matching to certain samples due to the content-aware optimization of the discriminator. This paper proposes a simple (a few lines of code) but effective training methodology, selective focusing learning, which enforces the discriminator and generator to learn easy samples of each class rapidly while maintaining diversity. Our key idea is to selectively apply conditional and joint matching for the data in each mini-batch. We conducted experiments on recent cGAN variants in ImageNet (64x64 and 128x128), CIFAR-10, and CIFAR-100 datasets, and improved the performance significantly (up to 35.18% in terms of FID) without sacrificing diversity.
Dynamic Cat Swarm Optimization Algorithm for Backboard Wiring Problem
Authors: Aram Ahmed, Tarik A. Rashid, Soran Saeed
Date: 2021-04-27 00:00:00
Tasks: Diversity

This paper presents a powerful swarm intelligence meta-heuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization. The original Cat Swarm Optimization suffers from the shortcoming of 'premature convergence', which is the possibility of entrapment in local optima which usually happens due to the off-balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real world scenario are used. In addition, the Dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion as well as future directions to further improve the algorithm are discussed.
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
Authors: Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan, Ashwin Balakrishna, Jeffrey Ichnowski, Ellen Novoseller, Minho Hwang, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg
Date: 2021-06-29 00:00:00
Tasks: Robot Manipulation

Robot manipulation for untangling 1D deformable structures such as ropes, cables, and wires is challenging due to their infinite dimensional configuration space, complex dynamics, and tendency to self-occlude. Analytical controllers often fail in the presence of dense configurations, due to the difficulty of grasping between adjacent cable segments. We present two algorithms that enhance robust cable untangling, LOKI and SPiDERMan, which operate alongside HULK, a high-level planner from prior work. LOKI uses a learned model of manipulation features to refine a coarse grasp keypoint prediction to a precise, optimized location and orientation, while SPiDERMan uses a learned model to sense task progress and apply recovery actions. We evaluate these algorithms in physical cable untangling experiments with 336 knots and over 1500 actions on real cables using the da Vinci surgical robot. We find that the combination of HULK, LOKI, and SPiDERMan is able to untangle dense overhand, figure-eight, double-overhand, square, bowline, granny, stevedore, and triple-overhand knots. The composition of these methods successfully untangles a cable from a dense initial configuration in 68.3% of 60 physical experiments and achieves 50% higher success rates than baselines from prior work. Supplementary material, code, and videos can be found at https://tinyurl.com/rssuntangling.
Prediction of the final rank of Players in PUBG with the optimal number of features
Authors: Diptakshi Sen, Rupam Kumar Roy, Ritajit Majumdar, Kingshuk Chatterjee, Debayan Ganguly
Date: 2021-07-01 00:00:00

PUBG is an online video game that has become very popular among the youths in recent years. Final rank, which indicates the performance of a player, is one of the most important feature for this game. This paper focuses on predicting the final rank of the players based on their skills and abilities. In this paper we have used different machine learning algorithms to predict the final rank of the players on a dataset obtained from kaggle which has 29 features. Using the correlation heatmap,we have varied the number of features used for the model. Out of these models GBR and LGBM have given the best result with the accuracy of 91.63% and 91.26% respectively for 14 features and the accuracy of 90.54% and 90.01% for 8 features. Although the accuracy of the models with 14 features is slightly better than 8 features, the empirical time taken by 8 features is 1.4x lesser than 14 features for LGBM and 1.5x lesser for GBR. Furthermore, reducing the number of features any more significantly hampers the performance of all the ML models. Therefore, we conclude that 8 is the optimal number of features that can be used to predict the final rank of a player in PUBG with high accuracy and low run-time.
On Out-of-distribution Detection with Energy-based Models
Authors: Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Date: 2021-07-03 00:00:00
Tasks: Density Estimation, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Several density estimation methods have shown to fail to detect out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous data. Energy-based models (EBMs) are flexible, unnormalized density models which seem to be able to improve upon this failure mode. In this work, we provide an extensive study investigating OOD detection with EBMs trained with different approaches on tabular and image data and find that EBMs do not provide consistent advantages. We hypothesize that EBMs do not learn semantic features despite their discriminative structure similar to Normalizing Flows. To verify this hypotheses, we show that supervision and architectural restrictions improve the OOD detection of EBMs independent of the training approach.
Federated Learning with Dynamic Transformer for Text to Speech
Authors: Zhenhou Hong, Jianzong Wang, Xiaoyang Qu, Jie Liu, Chendong Zhao, Jing Xiao
Date: 2021-07-09 00:00:00
Tasks: Federated Learning, text-to-speech, Text to Speech

Text to speech (TTS) is a crucial task for user interaction, but TTS model training relies on a sizable set of high-quality original datasets. Due to privacy and security issues, the original datasets are usually unavailable directly. Recently, federated learning proposes a popular distributed machine learning paradigm with an enhanced privacy protection mechanism. It offers a practical and secure framework for data owners to collaborate with others, thus obtaining a better global model trained on the larger dataset. However, due to the high complexity of transformer models, the convergence process becomes slow and unstable in the federated learning setting. Besides, the transformer model trained in federated learning is costly communication and limited computational speed on clients, impeding its popularity. To deal with these challenges, we propose the federated dynamic transformer. On the one hand, the performance is greatly improved comparing with the federated transformer, approaching centralize-trained Transformer-TTS when increasing clients number. On the other hand, it achieves faster and more stable convergence in the training phase and significantly reduces communication time. Experiments on the LJSpeech dataset also strongly prove our method's advantage.
Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies
Authors: Derek Snow
Date: 2020-02-10 00:00:00
Proceeding: Journal of Financial Data Science 2020 2
Tasks: Algorithmic Trading, Asset Management, BIG-bench Machine Learning, Decision Making, Management, Marketing, Position

This is the first in a series of arti-cles dealing with machine learning in asset management. Asset management can be broken into the following tasks: (1) portfolio construction, (2) risk management, (3) capital management, (4) infra-structure and deployment, and (5) sales and marketing. This article focuses on portfolio construction using machine learning. Historically, algorithmic trading could be more narrowly defined as the automation of sell-side trade execution, but since the introduction of more advanced algorithms, the definition has grown to include idea generation, alpha factor design, asset allocation, position sizing, and the testing of strategies. Machine learning, from the vantage of a decision-making tool, can help in all these areas.