pwc-archive/papers-with-abstracts huggingface dataset:
Emergent Properties in a V1-Inspired Network of Hodgkin-Huxley Neurons
Authors: M. Maama, B. Ambrosio, M. A. Aziz-Alaoui, S. M. Mintchev
Date: 2020-04-06 00:00:00

This article is devoted to the theoretical and numerical analysis of a network of excitatory and inhibitory neurons of Hodgkin-Huxley (HH) type, for which the topology is inspired by that of a single local layer of visual cortex V1. Our model is related to the recent CSY model and therefore differs from other classical models in the field. It combines a driven stochastic drive -- which may be interpreted as an ambient drive for each neuron -- with recurrent inputs resulting from the network activity. After a review of the dynamics of a single HH equation for both the deterministic and the stochastically driven case, we proceed to an analysis of the network. This analysis reveals emergent properties of the system such as partial synchronization and synchronization (defined here as a state of the network for which all the neurons spike within a short interval of time), correlation between excitatory and inhibitory conductances, and oscillations in the gamma-band frequency. The collective behavior enumerated herein is observed when the input-amplitude parameter $S^{EE}$ measuring excitatory-to-excitatory coupling (recurrent excitation) increases to within a certain range. Of note, our work indicates a distinct mechanism for obtaining the emergent properties, some of which have been classically observed. As a consequence our article contributes to the understanding of how assemblies of inhibitory and excitatory cells interact together to produce rhythms in the network. It also aims to bring problems from neuroscience to the realm of mathematics, where they can be analyzed rigorously.
Risk sharing under heterogeneous beliefs without convexity
Authors: Felix-Benedikt Liebrich
Date: 2021-08-12 00:00:00

We consider the problem of finding Pareto-optimal allocations of risk among finitely many agents. The associated individual risk measures are law invariant, but with respect to agent-dependent and potentially heterogeneous reference probability measures. Moreover, we assume that the individual risk assessments are consistent with the respective second-order stochastic dominance relations. We do not assume their convexity though. A simple sufficient condition for the existence of Pareto optima is provided. The proof combines local comonotone improvement with a Dieudonn\'e-type argument, which also establishes a link of the optimal allocation problem to the realm of "collapse to the mean" results. Finally, we extend the results to capital requirements with multidimensional security markets.
Identifying poverty traps based on the network structure of economic output
Authors: Vanessa Echeverri, Juan C. Duque, Daniel E. Restrepo
Date: 2021-08-12 00:00:00

In this work, we explore the relationship between monetary poverty and production combining relatedness theory, graph theory, and regression analysis. We develop two measures at product level that capture short-run and long-run patterns of poverty, respectively. We use the network of related products (or product space) and both metrics to estimate the influence of the productive structure of a country in its current and future levels of poverty. We found that poverty is highly associated with poorly connected nodes in the PS, especially products based on natural resources. We perform a series of regressions with several controls (including human capital, institutions, income, and population) to show the robustness of our measures as predictors of poverty. Finally, by means of some illustrative examples, we show how our measures distinguishes between nuanced cases of countries with similar poverty and production and identify possibilities of improving their current poverty levels.
Gamblers Learn from Experience
Authors: Joshua E. Blumenstock, Matthew Olckers
Date: 2020-11-01 00:00:00

Mobile phone-based sports betting has exploded in popularity in many African countries. Commentators worry that low-ability gamblers will not learn from experience, and may rely on debt to gamble. Using data on financial transactions for over 50 000 Kenyan smartphone users, we find that gamblers do learn from experience. Gamblers are less likely to bet following poor results and more likely to bet following good results. The reaction to positive and negative feedback is of equal magnitude and is consistent with a model of Bayesian updating. Using an instrumental variables strategy, we find no evidence that increased gambling leads to increased debt.
A New Multi Objective Mathematical Model for Relief Distribution Location at Natural Disaster Response Phase
Authors: Mohamad Ebrahim Sadeghi, Morteza Khodabakhsh, Mahmood Reza Ganjipoor, Hamed Kazemipoor, Hamed Nozari
Date: 2021-08-11 00:00:00
Tasks: Disaster Response

Every year, natural disasters such as earthquake, flood, hurricane and etc. impose immense financial and humane losses on governments owing to their unpredictable character and arise of emergency situations and consequently the reduction of the abilities due to serious damages to infrastructures, increases demand for logistic services and supplies. First, in this study the necessity of paying attention to locating procedures in emergency situations is pointed out and an outline for the studied case of disaster relief supply chain was discussed and the problem was validated at small scale. On the other hand, to solve this kind of problems involving three objective functions and complicated time calculation, meta-heuristic methods which yield almost optimum solutions in less time are applied. The EC method and NSGA II algorithm are among the evolutionary multi-objective optimization algorithms applied in this case. In this study the aforementioned algorithm is used for solving problems at large scale.
Networks of News and Cross-Sectional Returns
Authors: Junjie Hu, Wolfgang Karl Härdle
Date: 2021-08-12 00:00:00
Tasks: Articles

We uncover networks from news articles to study cross-sectional stock returns. By analyzing a huge dataset of more than 1 million news articles collected from the internet, we construct time-varying directed networks of the S&P500 stocks. The well-defined directed news networks are formed based on a modest assumption about firm-specific news structure, and we propose an algorithm to tackle type-I errors in identifying the stock tickers. We find strong evidence for the comovement effect between the news-linked stocks returns and reversal effect from the lead stock return on the 1-day ahead follower stock return, after controlling for many known effects. Furthermore, a series of portfolio tests reveal that the news network attention proxy, network degree, provides a robust and significant cross-sectional predictability of the monthly stock returns. Among different types of news linkages, the linkages of within-sector stocks, large size lead firms, and lead firms with lower stock liquidity are crucial for cross-sectional predictability.
A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets
Authors: Peter Akioyamen, Yi Zhou Tang, Hussien Hussien
Date: 2021-08-05 00:00:00
Tasks: Dimensionality Reduction

Financial markets are of much interest to researchers due to their dynamic and stochastic nature. With their relations to world populations, global economies and asset valuations, understanding, identifying and forecasting trends and regimes are highly important. Attempts have been made to forecast market trends by employing machine learning methodologies, while statistical techniques have been the primary methods used in developing market regime switching models used for trading and hedging. In this paper we present a novel framework for the detection of regime switches within the US financial markets. Principal component analysis is applied for dimensionality reduction and the k-means algorithm is used as a clustering technique. Using a combination of cluster analysis and classification, we identify regimes in financial markets based on publicly available economic data. We display the efficacy of the framework by constructing and assessing the performance of two trading strategies based on detected regimes.
Comment on "An appropriate approach to pricing european-style options with the Adomian decomposition method"
Authors: Francisco M. Fernández
Date: 2021-08-12 00:00:00
Tasks: Relation

We show that the Adomian decomposition method proposed by Ke et al [ANZIAM J. \textbf{59} (2018) 349] is just the Taylor series approach in disguise. The latter approach is simpler, more straightforward and yields a recurrence relation free from integrals.
Sparse Temporal Disaggregation
Authors: Luke Mosley, Idris Eckley, Alex Gibberd
Date: 2021-08-12 00:00:00

Temporal disaggregation is a method commonly used in official statistics to enable high-frequency estimates of key economic indicators, such as GDP. Traditionally, such methods have relied on only a couple of high-frequency indicator series to produce estimates. However, the prevalence of large, and increasing, volumes of administrative and alternative data-sources motivates the need for such methods to be adapted for high-dimensional settings. In this article, we propose a novel sparse temporal-disaggregation procedure and contrast this with the classical Chow-Lin method. We demonstrate the performance of our proposed method through simulation study, highlighting various advantages realised. We also explore its application to disaggregation of UK gross domestic product data, demonstrating the method's ability to operate when the number of potential indicators is greater than the number of low-frequency observations.
Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification
Authors: Wouter van Loon, Frank de Vos, Marjolein Fokkema, Botond Szabo, Marisa Koini, Reinhold Schmidt, Mark de Rooij
Date: 2021-08-12 00:00:00
Tasks: Classification, regression

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.
FreaAI: Automated extraction of data slices to test machine learning models
Authors: Samuel Ackerman, Orna Raz, Marcel Zalmanovici
Date: 2021-08-12 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) solutions are prevalent. However, many challenges exist in making these solutions business-grade. One major challenge is to ensure that the ML solution provides its expected business value. In order to do that, one has to bridge the gap between the way ML model performance is measured and the solution requirements. In previous work (Barash et al, "Bridging the gap...") we demonstrated the effectiveness of utilizing feature models in bridging this gap. Whereas ML performance metrics, such as the accuracy or F1-score of a classifier, typically measure the average ML performance, feature models shed light on explainable data slices that are too far from that average, and therefore might indicate unsatisfied requirements. For example, the overall accuracy of a bank text terms classifier may be very high, say $98\% \pm 2\%$, yet it might perform poorly for terms that include short descriptions and originate from commercial accounts. A business requirement, which may be implicit in the training data, may be to perform well regardless of the type of account and length of the description. Therefore, the under-performing data slice that includes short descriptions and commercial accounts suggests poorly-met requirements. In this paper we show the feasibility of automatically extracting feature models that result in explainable data slices over which the ML solution under-performs. Our novel technique, IBM FreaAI aka FreaAI, extracts such slices from structured ML test data or any other labeled data. We demonstrate that FreaAI can automatically produce explainable and statistically-significant data slices over seven open datasets.
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Authors: Vasileios Baltatzis, Kyriaki-Margarita Bintsi, Loic Le Folgoc, Octavio E. Martinez Manzanera, Sam Ellis, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel
Date: 2021-08-11 00:00:00
Tasks: Lung Nodule Classification

Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results. In lung nodule classification, for example, many works report results on the publicly available LIDC dataset. In theory, this should allow a direct comparison of the performance of proposed methods and assess the impact of individual contributions. When analyzing seven recent works, however, we find that each employs a different data selection process, leading to largely varying total number of samples and ratios between benign and malignant cases. As each subset will have different characteristics with varying difficulty for classification, a direct comparison between the proposed methods is thus not always possible, nor fair. We study the particular effect of truthing when aggregating labels from multiple experts. We show that specific choices can have severe impact on the data distribution where it may be possible to achieve superior performance on one sample distribution but not on another. While we show that we can further improve on the state-of-the-art on one sample selection, we also find that on a more challenging sample selection, on the same database, the more advanced models underperform with respect to very simple baseline methods, highlighting that the selected data distribution may play an even more important role than the model architecture. This raises concerns about the validity of claimed methodological contributions. We believe the community should be aware of these pitfalls and make recommendations on how these can be avoided in future work.
Voxel-level Importance Maps for Interpretable Brain Age Estimation
Authors: Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Alexander Hammers, Daniel Rueckert
Date: 2021-08-11 00:00:00
Tasks: Age Estimation, Hippocampus, Prediction, regression

Brain aging, and more specifically the difference between the chronological and the biological age of a person, may be a promising biomarker for identifying neurodegenerative diseases. For this purpose accurate prediction is important but the localisation of the areas that play a significant role in the prediction is also crucial, in order to gain clinicians' trust and reassurance about the performance of a prediction model. Most interpretability methods are focused on classification tasks and cannot be directly transferred to regression tasks. In this study, we focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We interpret its predictions by extracting importance maps, which discover the parts of the brain that are the most important for brain age. In order to do so, we assume that voxels that are not useful for the regression are resilient to noise addition. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We average the importance maps of the subjects and end up with a population-based importance map, which displays the regions of the brain that are influential for the task. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature, highlighting that the hippocampus and the ventricles are the most relevant regions for brain aging.
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
Authors: Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan
Date: 2021-08-11 00:00:00
Tasks: Segmentation

Lymphoma detection and segmentation from whole-body Positron Emission Tomography/Computed Tomography (PET/CT) volumes are crucial for surgical indication and radiotherapy. Designing automatic segmentation methods capable of effectively exploiting the information from PET and CT as well as resolving their uncertainty remain a challenge. In this paper, we propose an lymphoma segmentation model using an UNet with an evidential PET/CT fusion layer. Single-modality volumes are trained separately to get initial segmentation maps and an evidential fusion layer is proposed to fuse the two pieces of evidence using Dempster-Shafer theory (DST). Moreover, a multi-task loss function is proposed: in addition to the use of the Dice loss for PET and CT segmentation, a loss function based on the concordance between the two segmentation is added to constrain the final segmentation. We evaluate our proposal on a database of polycentric PET/CT volumes of patients treated for lymphoma, delineated by the experts. Our method get accurate segmentation results with Dice score of 0.726, without any user interaction. Quantitative results show that our method is superior to the state-of-the-art methods.
SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery
Authors: Aggelina Chatziagapi, ShahRukh Athar, Francesc Moreno-Noguer, Dimitris Samaras
Date: 2021-08-11 00:00:00

We present SIDER(Single-Image neural optimization for facial geometric DEtail Recovery), a novel photometric optimization method that recovers detailed facial geometry from a single image in an unsupervised manner. Inspired by classical techniques of coarse-to-fine optimization and recent advances in implicit neural representations of 3D shape, SIDER combines a geometry prior based on statistical models and Signed Distance Functions (SDFs) to recover facial details from single images. First, it estimates a coarse geometry using a morphable model represented as an SDF. Next, it reconstructs facial geometry details by optimizing a photometric loss with respect to the ground truth image. In contrast to prior work, SIDER does not rely on any dataset priors and does not require additional supervision from multiple views, lighting changes or ground truth 3D shape. Extensive qualitative and quantitative evaluation demonstrates that our method achieves state-of-the-art on facial geometric detail recovery, using only a single in-the-wild image.
Learning to Segment Medical Images from Few-Shot Sparse Labels
Authors: Pedro H. T. Gama, Hugo Oliveira, Jefersson A. dos Santos
Date: 2021-08-12 00:00:00
Tasks: Few-Shot Semantic Segmentation, Medical Image Segmentation, Meta-Learning, Semantic Segmentation

In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotations (grid and points). The results show that our method is the most suitable when the target domain highly differs from source domains, achieving Jaccard scores comparable to dense labels, using less than 2% of the pixels of an image with labels in few-shot scenarios.
Automatic Gaze Analysis: A Survey of Deep Learning based Approaches
Authors: Shreya Ghosh, Abhinav Dhall, Munawar Hayat, Jarrod Knibbe, Qiang Ji
Date: 2021-08-12 00:00:00
Tasks: Deep Learning, Gaze Estimation, Survey

Eye gaze analysis is an important research problem in the field of Computer Vision and Human-Computer Interaction. Even with notable progress in the last 10 years, automatic gaze analysis still remains challenging due to the uniqueness of eye appearance, eye-head interplay, occlusion, image quality, and illumination conditions. There are several open questions, including what are the important cues to interpret gaze direction in an unconstrained environment without prior knowledge and how to encode them in real-time. We review the progress across a range of gaze analysis tasks and applications to elucidate these fundamental questions, identify effective methods in gaze analysis, and provide possible future directions. We analyze recent gaze estimation and segmentation methods, especially in the unsupervised and weakly supervised domain, based on their advantages and reported evaluation metrics. Our analysis shows that the development of a robust and generic gaze analysis method still needs to address real-world challenges such as unconstrained setup and learning with less supervision. We conclude by discussing future research directions for designing a real-world gaze analysis system that can propagate to other domains including Computer Vision, Augmented Reality (AR), Virtual Reality (VR), and Human Computer Interaction (HCI). Project Page: https://github.com/i-am-shreya/EyeGazeSurvey}{https://github.com/i-am-shreya/EyeGazeSurvey
Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery
Authors: Chitra Agastya, Sirak Ghebremusse, Ian Anderson, Colorado Reed, Hossein Vahabi, Alberto Todeschini
Date: 2021-08-12 00:00:00
Tasks: Contrastive Learning

Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will continue to be a major challenge necessitating careful monitoring and tracking of agricultural water usage. Historically, monitoring water usage has been a slow and expensive manual process with many imperfections and abuses. Ma-chine learning and remote sensing developments have increased the ability to automatically monitor irrigation patterns, but existing techniques often require curated and labelled irrigation data, which are expensive and time consuming to obtain and may not exist for impactful areas such as developing countries. In this paper, we explore an end-to-end real world application of irrigation detection with uncurated and unlabeled satellite imagery. We apply state-of-the-art self-supervised deep learning techniques to optical remote sensing data, and find that we are able to detect irrigation with up to nine times better precision, 90% better recall and 40% more generalization ability than the traditional supervised learning methods.