pwc-archive/papers-with-abstracts huggingface dataset:
Modelling African swine fever virus spread in pigs using time-respective network data: scientific support for decision-makers
Authors: Mathieu Andraud, Pachka Hammami, Brandon H. Hayes, Jason A. Galvis, Timothée Vergne, Gustavo Machado, Nicolas Rose
Date: 2021-11-26 00:00:00

African Swine Fever (ASF) represents the main threat to swine production, with heavy economic consequences for both farmers and the food industry. The spread of the virus that causes ASF through Europe raises the issues of identifying transmission routes and assessing their relative contributions in order to provide insights to stakeholders for adapted surveillance and control measures. A simulation model was developed to assess ASF spread over the commercial swine network in France. The model was designed from raw movement data and actual farm characteristics. A metapopulation approach was used, with transmission processes at the herd level potentially leading, through a reaction-diffusion process, to external spread to epidemiologically connected herds. Three transmission routes were considered: local transmission (e.g. fomites, material exchange), movement of animals from infected to susceptible sites, and transit of trucks without physical animal exchange. Surveillance was based on prevalence and mortality detection thresholds, which triggered control measures based on movement ban for detected herds and epidemiologically related herds. The time from infection to detection varied between 8 and 21 days, depending on the detection criteria, but was also dependent on the types of herds in which the infection was introduced. Movement restrictions effectively reduced the transmission between herds, but local transmission was nevertheless observed in higher proportions highlighting the need of global awareness of all actors of the swine industry to mitigate the risk of local spread. Raw movement data were directly used to build a dynamic network on a realistic time-scale. This approach allows for a rapid update of input data without any pre-treatment, which could be important in terms of reactivity, should an introduction occur.
Diffusion approximations in population genetics and the rate of Muller's ratchet
Authors: Camila Bräutigam, Matteo Smerlak
Date: 2021-11-26 00:00:00

Diffusion theory is a central tool of modern population genetics, yielding simple expressions for fixation probabilities and other quantities that are not easily derived from the underlying Wright-Fisher model. Unfortunately, the textbook derivation of diffusion equations as scaling limits requires evolutionary parameters (selection coefficients, mutation rates) to scale like the inverse population size -- a severe restriction that does not always reflect biological reality. Here we note that the Wright-Fisher model can be approximated by diffusion equations under more general conditions, including in regimes where selection and/or mutation are strong compared to genetic drift. As an illustration, we use a diffusion approximation of the Wright-Fisher model to improve estimates for the expected time to fixation of a strongly deleterious allele, i.e. the rate of Muller's ratchet.
SPAGETI: Stabilizing Phylogenetic Assessment with Gene Evolutionary Tree Indices
Authors: Ali Amiryousefi
Date: 2021-11-25 00:00:00

The standard approach to estimate species trees is to align a selected set of genes, concatenate the alignments and then estimate a consensus tree. However, individual genes contain differing levels of evolutionary information, either supporting or conflicting with the consensus. Based on individual gene evolutionary tree, a recent study has demonstrated that this approach may result in incorrect solutions and developed the internode certainty (IC) heuristic for estimating the confidence of splits made on the consensus tree. Although an improvement, this heuristic neglects the differing rates of molecular evolution in individual genes. Here I develop an improved version of this method such that each gene is proportionally weighted based on its overall signal and specifically with the imbalanced signal for each node represented with gene tree.
A General Surplus Decomposition Principle in Life Insurance
Authors: Julian Jetses, Marcus C. Christiansen
Date: 2021-11-25 00:00:00

In with-profit life insurance, the prudent valuation of future insurance liabilities leads to systematic surplus that mainly belongs to the policyholders and is redistributed as bonus. For a fair and lawful redistribution of surplus the insurer needs to decompose the total portfolio surplus with respect to the contributions of individual policies and with respect to different risk sources. For this task, actuaries have a number of heuristic decomposition formulas, but an overarching decomposition principle is still missing. This paper fills that gap by introducing a so-called ISU decomposition principle that bases on infinitesimal sequential updates of the insurer's valuation basis. It is shown that the existing heuristic decomposition formulas can be replicated as ISU decompositions. Furthermore, alternative decomposition principles and their relation to the ISU decomposition principle are discussed. The generality of the ISU concept makes it a useful tool also beyond classical surplus decompositions in life insurance.
Graph Auto-Encoders for Financial Clustering
Authors: Edward Turner
Date: 2021-11-26 00:00:00
Tasks: Clustering, Deep Learning

Deep learning has shown remarkable results on Euclidean data (e.g. audio, images, text) however this type of data is limited in the amount of relational information it can hold. In mathematics we can model more general relational data in a graph structure while retaining Euclidean data as associated node or edge features. Due to the ubiquity of graph data, and its ability to hold multiple dimensions of information, graph deep learning has become a fast emerging field. We look at applying and optimising graph deep learning on a finance graph to produce more informed clusters of companies. Having clusters produced from multiple streams of data can be highly useful in quantitative finance; not only does it allow clusters to be tailored to the specific task but the culmination of multiple streams allows for cross source pattern recognition that would have otherwise gone unnoticed. This can provide financial institutions with an edge over competitors which is crucial in the heavily optimised world of trading. In this paper we use news co-occurrence and stock price for our data combination. We optimise our model to achieve an average testing precision of 78% and find a clear improvement in clustering capabilities when dual data sources are used; cluster purity rises from 32% for just vertex data and 42% for just edge data to 64% when both are used in comparisons to ground-truth Bloomberg clusters. The framework we provide utilises unsupervised learning which we view as key for future work due to the volume of unlabelled data in financial markets.
Profiling Entity Matching Benchmark Tasks
Authors: Anna Primpeli, Christian Bizer
Date: 2020-10-19 00:00:00
Proceeding: International Conference on Information & Knowledge Management 2020 10
Tasks: Data Integration, Entity Resolution, Feature Engineering

Entity matching is a central task in data integration which has been researched for decades. Over this time, a wide range of benchmark tasks for evaluating entity matching methods has been developed. This resource paper systematically complements, profiles, and compares 21 entity matching benchmark tasks. In order to better understand the specific challenges associated with different tasks, we define a set of profiling dimensions which capture central aspects of the matching tasks. Using these dimensions, we create groups of benchmark tasks having similar characteristics. Afterwards, we assess the difficulty of the tasks in each group by computing baseline evaluation results using standard feature engineering together with two common classification methods. In order to enable the exact reproducibility of evaluation results, matching tasks need to contain exactly defined sets of matching and non-matching record pairs, as well as a fixed development and test split. As this is not the case for some widely-used benchmark tasks, we complement these tasks with fixed sets of non-matching pairs, as well as fixed splits, and provide the resulting development and test sets for public download.
By profiling and complementing the benchmark tasks, we support researchers to select challenging as well as diverse tasks and to compare matching systems on clearly defined grounds.
Graph-boosted Active Learning for Multi-Source Entity Resolution
Authors: Anna Primpeli, Christian Bizer
Date: 2021-09-30 00:00:00
Proceeding: International Semantic Web Conference 2021 9
Tasks: Active Learning, Entity Resolution

Supervised entity resolution methods rely on labeled record pairs for learning matching patterns between two or more data sources. Active learning minimizes the labeling effort by selecting informative pairs for labeling. The existing active learning methods for entity resolution all target two-source matching scenarios and ignore signals that only exist in multi-source settings, such as the Web of Data. In this paper, we propose ALMSER, a graph-boosted active learning method for multi-source entity resolution. To the best of our knowledge, ALMSER is the first active learning-based entity resolution method that is especially tailored to the multi-source setting. ALMSER exploits the rich correspondence graph that exists in multi-source settings for selecting informative record pairs. In addition, the correspondence graph is used to derive complementary training data. We evaluate our method using five multi-source matching tasks having different profiling characteristics. The experimental evaluation shows that leveraging graph signals leads to improved results over active learning methods using margin-based and committee-based query strategies in terms of F1 score on all tasks.
Use of Machine Learning Algorithms for Prediction of Fetal Risk using Cardiotocographic Data
Authors: Zahra Hoodbhoy, Mohammad  Noman, Ayesha Shafique, Ali Nasim, Devyani  Chowdhury, Babar Hasan
Date: 2019-10-11 00:00:00
Proceeding: International Journal of Applied and Basic Medical Research 2019 10
Tasks: BIG-bench Machine Learning, Management

Background: A major contributor to under‑five mortality is the death of children in the 1st month 
of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal 
cardiotocograph (CTGs) can be used as a monitoring tool to identify high‑risk women during labor. 
Aim: The objective of this study was to study the precision of machine learning algorithm techniques 
on CTG data in identifying high‑risk fetuses. Methods: CTG data of 2126 pregnant women were 
obtained from the University of California Irvine Machine Learning Repository. Ten different 
machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 
score for each class and overall accuracy of each model were obtained to predict normal, suspect, 
and pathological fetal states. Model with best performance on specified metrics was then identified. 
Results: Determined by obstetricians’ interpretation of CTGs as gold standard, 70% of them were 
normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification 
models generated by XGBoost, decision tree, and random forest had high precision (>96%) to 
predict the suspect and pathological state of the fetus based on the CTG tracings. However, on 
testing data, XGBoost model had the highest precision to predict a pathological fetal state (>92%). 
Conclusion: The classification model developed using XGBoost technique had the highest prediction 
accuracy for an adverse fetal outcome. Lay health‑care workers in low‑ and middle‑income 
countries can use this model to triage pregnant women in remote areas for early referral and further 
management
AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud Segmentation
Authors: Seunghoi Kim, Daniel C. Alexander
Date: 2021-11-25 00:00:00
Proceeding: British Machine Vision Conference (BMVC) 2021 11
Tasks: 3D Part Segmentation, Point Cloud Segmentation, Segmentation

3D point cloud segmentation provides a high-level semantic understanding of object structure that is valuable in applications such as medicine, robotics and self-driving. In this paper, we propose an Adversarial Graph Convolutional Network for 3D point cloud segmentation. Many current networks encounter problems such as low segmentation accuracy and high complexities due to their crude network architectures and local feature aggregation methods. 
To overcome these problems, we propose a) a graph convolutional network (GCN) in an adversarial learning scheme where a discriminator network provides a segmentation network with informative information to improve segmentation
accuracy and b) a graph convolution, GeoEdgeConv, as a means of local feature aggregation to improve segmentation accuracy and space and time complexities. By using an embedding L2 loss as an adversarial loss, the proposed network is learned to reduce noisy labels by enforcing the consistency between neighbouring labels. Preserving geometric structures over convolution layers by using both point and relative position features, GeoEdgeConv helps learn fine details of complex structures, and thus improves segmentation accuracy in boundaries and reduces label noise inside a class without increased computational complexity. Experiments on ShapeNet Part demonstrate that our model outperforms the state-of-the-art (SOTA) with lower complexity and it has strong prospects in applications requiring low power but high segmentation performance.
Scalable Matching and Clustering of Entities with FAMER
Authors: Alieh Saeedi, Markus Nentwig, Eric Peukert, Erhard Rahm
Date: 2018-10-01 00:00:00
Proceeding: Complex Systems Informatics and Modeling Quarterly 2018 10
Tasks: Clustering, Clustering Algorithms Evaluation, Entity Resolution

Entity resolution identifies semantically equivalent entities, e.g. describing the same product or customer. It is especially challenging for Big Data applications where large volumes of data from many sources have to be matched and integrated. We therefore introduce a scalable entity resolution framework called FAMER (FAst Multi-source Entity Resolution system) that is based on Apache Flink for distributed execution and that can holistically match entities from multiple sources. For the latter purpose, FAMER includes multiple clustering schemes that group matching entities from different sources within clusters. In addition to previously known clustering schemes FAMER includes new approaches tailored to multi-source entity resolution. We perform a detailed comparative evaluation of eight clustering schemes for different real-life and synthetically generated datasets. The evaluation considers both the match quality as well as the scalability for different numbers of machines and data sizes.
An Empirical Study of Topic Transition in Dialogue
Authors: Mayank Soni, Brendan Spillane, Emer Gilmartin, Christian Saam, Benjamin R. Cowan, Vincent Wade
Date: 2021-11-28 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: Open-Domain Dialog

Transitioning between topics is a natural component of human-human dialog. Although topic transition has been studied in dialogue for decades, only a handful of corpora based studies have been performed to investigate the subtleties of topic transitions. Thus, this study annotates 215 conversations from the switchboard corpus and investigates how variables such as length, number of topic transitions, topic transitions share by participants and turns/topic are related. This work presents an empirical study on topic transition in switchboard corpus followed by modelling topic transition with a precision of 83% for in-domain(id) test set and 82% on 10 out-of-domain}(ood) test set. It is envisioned that this work will help in emulating human-human like topic transition in open-domain dialog systems.
FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
Authors: Yang Lin, Tianyu Zhang, Peiqin Sun, Zheng Li, Shuchang Zhou
Date: 2021-11-27 00:00:00
Tasks: Quantization

Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been developed mainly on Convolutional Neural Networks (CNNs), and suffer severe degradation when applied to fully quantized vision transformers. In this work, we demonstrate that many of these difficulties arise because of serious inter-channel variation in LayerNorm inputs, and present, Power-of-Two Factor (PTF), a systematic method to reduce the performance degradation and inference complexity of fully quantized vision transformers. In addition, observing an extreme non-uniform distribution in attention maps, we propose Log-Int-Softmax (LIS) to sustain that and simplify inference by using 4-bit quantization and the BitShift operator. Comprehensive experiments on various transformer-based architectures and benchmarks show that our Fully Quantized Vision Transformer (FQ-ViT) outperforms previous works while even using lower bit-width on attention maps. For instance, we reach 84.89% top-1 accuracy with ViT-L on ImageNet and 50.8 mAP with Cascade Mask R-CNN (Swin-S) on COCO. To our knowledge, we are the first to achieve lossless accuracy degradation (~1%) on fully quantized vision transformers. The code is available at https://github.com/megvii-research/FQ-ViT.
AdaDM: Enabling Normalization for Image Super-Resolution
Authors: Jie Liu, Jie Tang, Gangshan Wu
Date: 2021-11-27 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Normalization like Batch Normalization (BN) is a milestone technique to normalize the distributions of intermediate layers in deep learning, enabling faster training and better generalization accuracy. However, in fidelity image Super-Resolution (SR), it is believed that normalization layers get rid of range flexibility by normalizing the features and they are simply removed from modern SR networks. In this paper, we study this phenomenon quantitatively and qualitatively. We found that the standard deviation of the residual feature shrinks a lot after normalization layers, which causes the performance degradation in SR networks. Standard deviation reflects the amount of variation of pixel values. When the variation becomes smaller, the edges will become less discriminative for the network to resolve. To address this problem, we propose an Adaptive Deviation Modulator (AdaDM), in which a modulation factor is adaptively predicted to amplify the pixel deviation. For better generalization performance, we apply BN in state-of-the-art SR networks with the proposed AdaDM. Meanwhile, the deviation amplification strategy in AdaDM makes the edge information in the feature more distinguishable. As a consequence, SR networks with BN and our AdaDM can get substantial performance improvements on benchmark datasets. Extensive experiments have been conducted to show the effectiveness of our method.
Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching
Authors: Zhengxiang Wang
Date: 2021-11-29 00:00:00
Tasks: Data Augmentation, Language Modelling, Question Similarity, Semantic Text Matching, Text Augmentation

To investigate the role of linguistic knowledge in data augmentation (DA) for Natural Language Processing (NLP), we designed two adapted DA programs and applied them to LCQMC (a Large-scale Chinese Question Matching Corpus) for a binary Chinese question matching classification task. The two DA programs produce augmented texts by five simple text editing operations (or DA techniques), largely irrespective of language generation rules, but one is enhanced with a pre-trained n-gram language model to fuse it with prior linguistic knowledge. We then trained four neural network models (BOW, CNN, LSTM, and GRU) and a pre-trained model (ERNIE-Gram) on the LCQMCs train sets of varying size as well as the related augmented train sets produced by the two DA programs. The results show that there are no significant performance differences between the models trained on the two types of augmented train sets, both when the five DA techniques are applied together or separately. Moreover, due to the inability of the five DA techniques to make strictly paraphrastic augmented texts, the results indicate the need of sufficient amounts of training examples for the classification models trained on them to mediate the negative impact of false matching augmented text pairs and improve performance, a limitation of random text editing perturbations used as a DA approach. Similar results were also obtained for English.
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
Authors: Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, David Rolnick
Date: 2021-11-29 00:00:00
Tasks: Benchmarking, Physical Simulations

Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in replacing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To fill this gap, we build a large dataset, ClimART, with more than \emph{10 million samples from present, pre-industrial, and future climate conditions}, based on the Canadian Earth System Model. ClimART poses several methodological challenges for the ML community, such as multiple out-of-distribution test sets, underlying domain physics, and a trade-off between accuracy and inference speed. We also present several novel baselines that indicate shortcomings of datasets and network architectures used in prior work. Download instructions, baselines, and code are available at: https://github.com/RolnickLab/climart