pwc-archive/papers-with-abstracts huggingface dataset:
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection
Authors: Andong Li, Zhaohong Deng, Qiongdan Lou, Kup-Sze Choi, Hongbin Shen, Shitong Wang
Date: 2021-11-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), MULTI-VIEW LEARNING, Transfer Learning

In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the ac-curacy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset.
Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions
Authors: Huy Phan, Kaare Mikkelsen
Date: 2021-11-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Staging

Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.
Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review
Authors: Avishek Choudhury, Emily Renjilian, Onur Asan
Date: 2021-10-30 00:00:00
Tasks: Articles, Systematic Literature Review

Objectives-Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients (age 65 years and above) functional ability, physical health, and cognitive wellbeing. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods-We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results-We identified 35 eligible studies and classified in three groups-psychological disorder (n=22), eye diseases (n=6), and others (n=7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion- More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.
Simulating long-term impacts of mortality shocks: learning from the cholera pandemic
Authors: Nicole El Karoui, Kaouther Hadji, Sarah Kaakai
Date: 2021-11-16 00:00:00

The aim of this paper is to study the long-term consequence on longevity of a mortality shock. We adopt an historical and modeling approach to study how the population evolution following a mortality shock such as the COVID-19 pandemic could impact future mortality rates. In the first of part the paper, we study the several cholera epidemics in France and in England starting from the 1830s, and their impact on the major development of public health at the end of the nineteenth century. In the second part, we present the mathematical modeling of stochastic Individual-Based models. Using the R package IBMPopSim, this flexible framework is then applied to simulate the long-term impact of a mortality shock, using a toy model where nonlinear population compositional changes affect future mortality rates.
A paradigm shift, or a paradigm adjustment? The evolution of the Oleaceae mating system as a small-scale Kuhnian case-study
Authors: A. Francq, P. Saumitou-Laprade, P. Vernet, S. Billiard
Date: 2021-11-16 00:00:00

Kuhn (1962) proposed an evolutionary model to explain how scientific knowledge is built, based on the concept of paradigm. Even though Kuhn's model is general, it has been applied only to a few topics in evolutionary biology, especially broad-based paradigms. Our goal here is to analyze a small-scale paradigm change that occurred about the mating system of a Mediterranean shrub: P. angustifolia (Oleaceae) through the lens of Kuhn's model. We first summarize the different steps of the paradigm change and replace them in the more general context of the sex ratio theory. Second, we show how the different steps of the paradigm changes can be interpreted by Kuhnian concepts and tools. Finally, we discuss the actual and future status of the new paradigm.
Risk measures beyond frictionless markets
Authors: Maria Arduca, Cosimo Munari
Date: 2021-11-16 00:00:00
Tasks: Translation

We develop a general theory of risk measures that determines the optimal amount of capital to raise and invest in a portfolio of reference traded securities in order to meet a pre-specified regulatory requirement. The distinguishing feature of our approach is that we embed portfolio constraints and transaction costs into the securities market. As a consequence, we have to dispense with the property of translation invariance, which plays a key role in the classical theory. We provide a comprehensive analysis of relevant properties such as star shapedness, positive homogeneity, convexity, quasiconvexity, subadditivity, and lower semicontinuity. In addition, we establish dual representations for convex and quasiconvex risk measures. In the convex case, the absence of a special kind of arbitrage opportunities allows to obtain dual representations in terms of pricing rules that respect market bid-ask spreads and assign a strictly positive price to each nonzero position in the regulator's acceptance set.
A Family of Multi-Asset Automated Market Makers
Authors: Eric Forgy, Leo Lau
Date: 2021-11-15 00:00:00

We present a family of multi-asset automated market makers whose liquidity curves are derived from the financial principles of self financing transactions and rebalancing. The constant product market maker emerges as a special case.
Joint Learning of Visual-Audio Saliency Prediction and Sound Source Localization on Multi-face Videos
Authors: Minglang Qiao, Yufan Liu, Mai Xu, Xin Deng, Bing Li, Weiming Hu, Ali Borji
Date: 2021-11-05 00:00:00
Tasks: Prediction, Saliency Prediction, Sound Source Localization

Visual and audio events simultaneously occur and both attract attention. However, most existing saliency prediction works ignore the influence of audio and only consider vision modality. In this paper, we propose a multitask learning method for visual-audio saliency prediction and sound source localization on multi-face video by leveraging visual, audio and face information. Specifically, we first introduce a large-scale database of multi-face video in visual-audio condition (MVVA), containing eye-tracking data and sound source annotations. Using this database, we find that sound influences human attention, and conversly attention offers a cue to determine sound source on multi-face video. Guided by these findings, a visual-audio multi-task network (VAM-Net) is introduced to predict saliency and locate sound source. VAM-Net consists of three branches corresponding to visual, audio and face modalities. Visual branch has a two-stream architecture to capture spatial and temporal information. Face and audio branches encode audio signals and faces, respectively. Finally, a spatio-temporal multi-modal graph (STMG) is constructed to model the interaction among multiple faces. With joint optimization of these branches, the intrinsic correlation of the tasks of saliency prediction and sound source localization is utilized and their performance is boosted by each other. Experiments show that the proposed method outperforms 12 state-of-the-art saliency prediction methods, and achieves competitive results in sound source localization.
An Underexplored Dilemma between Confidence and Calibration in Quantized Neural Networks
Authors: Guoxuan Xia, Sangwon Ha, Tiago Azevedo, Partha Maji
Date: 2021-11-10 00:00:00
Proceeding: NeurIPS Workshop ICBINB 2021 12
Tasks: Decision Making, Quantization

Modern convolutional neural networks (CNNs) are known to be overconfident in terms of their calibration on unseen input data. That is to say, they are more confident than they are accurate. This is undesirable if the probabilities predicted are to be used for downstream decision making. When considering accuracy, CNNs are also surprisingly robust to compression techniques, such as quantization, which aim to reduce computational and memory costs. We show that this robustness can be partially explained by the calibration behavior of modern CNNs, and may be improved with overconfidence. This is due to an intuitive result: low confidence predictions are more likely to change post-quantization, whilst being less accurate. High confidence predictions will be more accurate, but more difficult to change. Thus, a minimal drop in post-quantization accuracy is incurred. This presents a potential conflict in neural network design: worse calibration from overconfidence may lead to better robustness to quantization. We perform experiments applying post-training quantization to a variety of CNNs, on the CIFAR-100 and ImageNet datasets.
A Survey on Neural-symbolic Learning Systems
Authors: Dongran Yu, Bo Yang, Dayou Liu, Hui Wang, Shirui Pan
Date: 2021-11-10 00:00:00
Tasks: Survey

In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence. However, they have been found to lack effective reasoning and cognitive ability. On the other hand, symbolic systems exhibit exceptional cognitive intelligence but suffer from poor learning capabilities when compared to neural systems. Recognizing the advantages and disadvantages of both methodologies, an ideal solution emerges: combining neural systems and symbolic systems to create neural-symbolic learning systems that possess powerful perception and cognition. The purpose of this paper is to survey the advancements in neural-symbolic learning systems from four distinct perspectives: challenges, methods, applications, and future directions. By doing so, this research aims to propel this emerging field forward, offering researchers a comprehensive and holistic overview. This overview will not only highlight the current state-of-the-art but also identify promising avenues for future research.
A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering
Authors: Xuyang Yan, Mrinmoy Sarkar, Biniam Gebru, Shabnam Nazmi, Abdollah Homaifar
Date: 2021-11-10 00:00:00
Tasks: Clustering, feature selection

Feature selection methods are widely used to address the high computational overheads and curse of dimensionality in classifying high-dimensional data. Most conventional feature selection methods focus on handling homogeneous features, while real-world datasets usually have a mixture of continuous and discrete features. Some recent mixed-type feature selection studies only select features with high relevance to class labels and ignore the redundancy among features. The determination of an appropriate feature subset is also a challenge. In this paper, a supervised feature selection method using density-based feature clustering (SFSDFC) is proposed to obtain an appropriate final feature subset for mixed-type data. SFSDFC decomposes the feature space into a set of disjoint feature clusters using a novel density-based clustering method. Then, an effective feature selection strategy is employed to obtain a subset of important features with minimal redundancy from those feature clusters. Extensive experiments as well as comparison studies with five state-of-the-art methods are conducted on SFSDFC using thirteen real-world benchmark datasets and results justify the efficacy of the SFSDFC method.
Fourier Neural Networks for Function Approximation
Authors: R Subhash Chandra Bose, Kakarla Yaswanth
Date: 2021-10-21 00:00:00

The success of Neural networks in providing miraculous results when applied to a wide variety of tasks is astonishing. Insight in the working can be obtained by studying the universal approximation property of neural networks. It is proved extensively that neural networks are universal approximators. Further it is proved that deep Neural networks are better approximators. It is specifically proved that for a narrow neural network to approximate a function which is otherwise implemented by a deep Neural network, the network take exponentially large number of neurons. In this work, we have implemented existing methodologies for a variety of synthetic functions and identified their deficiencies. Further, we examined that Fourier neural network is able to perform fairly good with only two layers in the neural network. A modified Fourier Neural network which has sinusoidal activation and two hidden layer is proposed and the results are tabulated.
Complementary Ensemble Learning
Authors: Hung Nguyen, Morris Chang
Date: 2021-11-09 00:00:00
Tasks: Diversity, Ensemble Learning

To achieve high performance of a machine learning (ML) task, a deep learning-based model must implicitly capture the entire distribution from data. Thus, it requires a huge amount of training samples, and data are expected to fully present the real distribution, especially for high dimensional data, e.g., images, videos. In practice, however, data are usually collected with a diversity of styles, and several of them have insufficient number of representatives. This might lead to uncertainty in models' prediction, and significantly reduce ML task performance. In this paper, we provide a comprehensive study on this problem by looking at model uncertainty. From this, we derive a simple but efficient technique to improve performance of state-of-the-art deep learning models. Specifically, we train auxiliary models which are able to complement state-of-the-art model uncertainty. As a result, by assembling these models, we can significantly improve the ML task performance for types of data mentioned earlier. While slightly improving ML classification accuracy on benchmark datasets (e.g., 0.2% on MNIST), our proposed method significantly improves on limited data (i.e., 1.3% on Eardrum and 3.5% on ChestXray).
Comparing Cross Correlation-Based Similarities
Authors: Luciano da F. Costa
Date: 2021-11-08 00:00:00
Tasks: Template Matching

The real-valued Jaccard and coincidence indices, in addition to their conceptual and computational simplicity, have been verified to be able to provide promising results in tasks such as template matching, tending to yield peaks that are sharper and narrower than those typically obtained by standard cross-correlation, while also attenuating substantially secondary matchings. In this work, the multiset-based correlations based on the real-valued multiset Jaccard and coincidence indices are compared from the perspective of template matching, with encouraging results which have implications for pattern recognition, deep learning, and scientific modeling in general. The multiset-based correlation methods, and especially the coincidence index, presented remarkable performance characterized by sharper and narrower peaks while secondary peaks were attenuated, which was maintained even in presence of intense levels of noise. In particular, the two methods derived from the coincidence index led to particularly interesting results. The cross correlation, however, presented the best robustness to symmetric additive noise, which suggested a new combination of the considered approaches. After a preliminary investigation of the relative performance of the multiset approaches, as well as the classic cross-correlation, a systematic comparison framework is proposed and applied for the study of the aforementioned methods. Several results are reported, including the confirmation, at least for the considered type of data, of the coincidence correlation as providing enhanced performance regarding detection of narrow, sharp peaks while secondary matches are duly attenuated. The combined method also resulted promising for dealing with signals in presence of intense additive noise.
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks
Authors: Chao Pang, Xinzhuo Jiang, Krishna S Kalluri, Matthew Spotnitz, Ruijun Chen, Adler Perotte, Karthik Natarajan
Date: 2021-11-10 00:00:00
Tasks: Disease Prediction, Transfer Learning

Embedding algorithms are increasingly used to represent clinical concepts in healthcare for improving machine learning tasks such as clinical phenotyping and disease prediction. Recent studies have adapted state-of-the-art bidirectional encoder representations from transformers (BERT) architecture to structured electronic health records (EHR) data for the generation of contextualized concept embeddings, yet do not fully incorporate temporal data across multiple clinical domains. Therefore we developed a new BERT adaptation, CEHR-BERT, to incorporate temporal information using a hybrid approach by augmenting the input to BERT using artificial time tokens, incorporating time, age, and concept embeddings, and introducing a new second learning objective for visit type. CEHR-BERT was trained on a subset of Columbia University Irving Medical Center-York Presbyterian Hospital's clinical data, which includes 2.4M patients, spanning over three decades, and tested using 4-fold cross-validation on the following prediction tasks: hospitalization, death, new heart failure (HF) diagnosis, and HF readmission. Our experiments show that CEHR-BERT outperformed existing state-of-the-art clinical BERT adaptations and baseline models across all 4 prediction tasks in both ROC-AUC and PR-AUC. CEHR-BERT also demonstrated strong transfer learning capability, as our model trained on only 5% of data outperformed comparison models trained on the entire data set. Ablation studies to better understand the contribution of each time component showed incremental gains with every element, suggesting that CEHR-BERT's incorporation of artificial time tokens, time and age embeddings with concept embeddings, and the addition of the second learning objective represents a promising approach for future BERT-based clinical embeddings.
Adaptive Similarity Embedding for Unsupervised Multi-View Feature Selection
Authors: Yuan Wan, Shengzi Sun, Cheng Zeng
Date: 2020-01-27 00:00:00
Proceeding: IEEE Xplore 2020 1
Tasks: feature selection, MULTI-VIEW LEARNING

Multi-view learning has become a significant research topic in image processing, data mining, and machine learning due
to the proliferation of multi-view data. Considering the difficulty in obtaining labeled data in many real applications, we focus on the
multi-view unsupervised feature selection problem. Most existing multi-view feature selections introduce an identical similarity matrix
among different views, which cannot preserve the specific correlation between every single view. Also, some of these methods just
consider either global or local structures. In this paper, we propose an embedding method, Adaptive Similarity Embedding for
Unsupervised Multi-View Feature Selection (ASE-UMFS). This method reduces the high-dimensional data to the low dimensions and
unifies different views to a combined weight matrix. We also use parameters to constraint the similarity matrix for the local structure, where the regularization term is used to add a prior of uniform distribution; taking into account the independence in the projection matrix among different views, optimization of the similarity matrix is further improved. To confirm the effectiveness of ASE-UMFS,
comparisons are made with benchmark algorithms on real-world data sets. The experimental results demonstrate that the proposed
algorithm outperforms several state-of-the-art methods in multi-view learning.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Authors: Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
Date: 2020-08-31 00:00:00
Proceeding: Proceedings of the National Academy of Sciences 2020 8
Tasks: Diversity, Language Modeling, Language Modelling, Representation Learning

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Evolutionary-scale language modeling is a logical step toward predictive and generative artificial intelligence for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone; no information about the properties of the sequences is given through supervision or domain-specific features. The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure, and improving state-of-the-art features for long-range contact prediction.