pwc-archive/papers-with-abstracts huggingface dataset:
Quantitative MR Image Reconstruction using Parameter-Specific Dictionary Learning with Adaptive Dictionary-Size and Sparsity-Level Choice
Authors: Andreas Kofler, Kirsten Miriam Kerkering, Laura Göschel, Ariane Fillmer, Cristoph Kolbitsch
Date: 2023-08-07 00:00:00
Tasks: Dictionary Learning, Image Reconstruction

Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). Methods: Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a $T_1$-mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7T scanner. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl) and Shearlets (Sh), and to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit). Results: Our algorithm surpasses MAP, TV, Wl and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven. Conclusion: The proposed method outperforms the reported methods of comparison and yields accurate $T_1$-maps. Although presented for $T_1$-mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs. Significance: From a clinical perspective, the obtained $T_1$-maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.+
Non-line-of-sight reconstruction via structure sparsity regularization
Authors: Duolan Huang, Quan Chen, Zhun Wei, Rui Chen
Date: 2023-08-05 00:00:00
Tasks: Autonomous Driving, Denoising

Non-line-of-sight (NLOS) imaging allows for the imaging of objects around a corner, which enables potential applications in various fields such as autonomous driving, robotic vision, medical imaging, security monitoring, etc. However, the quality of reconstruction is challenged by low signal-noise-ratio (SNR) measurements. In this study, we present a regularization method, referred to as structure sparsity (SS) regularization, for denoising in NLOS reconstruction. By exploiting the prior knowledge of structure sparseness, we incorporate nuclear norm penalization into the cost function of directional light-cone transform (DLCT) model for NLOS imaging system. This incorporation effectively integrates the neighborhood information associated with the directional albedo, thereby facilitating the denoising process. Subsequently, the reconstruction is achieved by optimizing a directional albedo model with SS regularization using fast iterative shrinkage-thresholding algorithm. Notably, the robust reconstruction of occluded objects is observed. Through comprehensive evaluations conducted on both synthetic and experimental datasets, we demonstrate that the proposed approach yields high-quality reconstructions, surpassing the state-of-the-art reconstruction algorithms, especially in scenarios involving short exposure and low SNR measurements.
Treatment Effects in Staggered Adoption Designs with Non-Parallel Trends
Authors: Brantly Callaway, Emmanuel Selorm Tsyawo
Date: 2023-08-05 00:00:00

This paper considers identifying and estimating causal effect parameters in a staggered treatment adoption setting -- that is, where a researcher has access to panel data and treatment timing varies across units. We consider the case where untreated potential outcomes may follow non-parallel trends over time across groups. This implies that the identifying assumptions of leading approaches such as difference-in-differences do not hold. We mainly focus on the case where untreated potential outcomes are generated by an interactive fixed effects model and show that variation in treatment timing provides additional moment conditions that can be used to recover a large class of target causal effect parameters. Our approach exploits the variation in treatment timing without requiring either (i) a large number of time periods or (ii) requiring any extra exclusion restrictions. This is in contrast to essentially all of the literature on interactive fixed effects models which requires at least one of these extra conditions. Rather, our approach directly applies in settings where there is variation in treatment timing. Although our main focus is on a model with interactive fixed effects, our idea of using variation in treatment timing to recover causal effect parameters is quite general and could be adapted to other settings with non-parallel trends across groups such as dynamic panel data models.
El paradigma del marketing digital en la academia, el emprendimiento universitario y las empresas establecidas
Authors: Guillermo Jose Navarro del Toro
Date: 2023-08-05 00:00:00
Tasks: Marketing

The updating required by students in the marketing area is of utmost importance, since they cannot wait until they are professionals to be updated, especially in entrepreneurship and around digital marketing. The objective of this work was to develop a digital marketing specialty module that allows changing the academic-entrepreneurial-entrepreneurial paradigm of the graduate of the Centro Universitario de los Altos (CUAltos) in economic-administrative sciences at the undergraduate, graduate and specialty levels. The module aims to instruct the student in the latest in digital marketing. As part of this, two surveys were applied to the teachers of this center to know the viability and willingness to participate in this module. Among the results, it was discovered that marketing research specialists who work as teachers at CUAltos are willing to train other teachers and students of the University of Guadalajara's system, as well as to advise students in their ventures so that they can apply different components of this field.
Fires and Local Labor Markets
Authors: Raphaelle G. Coulombe, Akhil Rao
Date: 2023-08-04 00:00:00

We study the dynamic effects of fires on county labor markets in the US using a novel geophysical measure of fire exposure based on satellite imagery. We find increased fire exposure causes lower employment growth in the short and medium run, with medium-run effects being linked to migration. We also document heterogeneous effects across counties by education and industrial concentration levels, states of the business cycle, and fire size. By overcoming challenges in measuring fire impacts, we identify vulnerable places and economic states, offering guidance on tailoring relief efforts and contributing to a broader understanding of natural disasters' economic impacts.
Mapping Global Value Chains at the Product Level
Authors: Lea Karbevska, César A. Hidalgo
Date: 2023-06-12 00:00:00
Tasks: Navigate

Value chain data is crucial to navigate economic disruptions, such as those caused by the COVID-19 pandemic and the war in Ukraine. Yet, despite its importance, publicly available value chain datasets, such as the ``World Input-Output Database'', ``Inter-Country Input-Output Tables'', ``EXIOBASE'' or the ``EORA'', lack detailed information about products (e.g. Radio Receivers, Telephones, Electrical Capacitors, LCDs, etc.) and rely instead on more aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications). Here, we introduce a method based on machine learning and trade theory to infer product-level value chain relationships from fine-grained international trade data. We apply our method to data summarizing the exports and imports of 300+ world regions (e.g. states in the U.S., prefectures in Japan, etc.) and 1200+ products to infer value chain information implicit in their trade patterns. Furthermore, we use proportional allocation to assign the trade flow between regions and countries. This work provides an approximate method to map value chain data at the product level with a relevant trade flow, that should be of interest to people working in logistics, trade, and sustainable development.
Uniqueness of equilibrium and redistributive policies: a geometric approach to efficiency
Authors: Andrea Loi, Stefano Matta, Daria Uccheddu
Date: 2023-08-07 00:00:00
Tasks: Decision Making

This paper examines the relationship between resource reallocation, uniqueness of equilibrium and efficiency in economics. We explore the implications of reallocation policies for stability, conflict, and decision-making by analysing the existence of geodesic coordinate functions in the equilibrium manifold. Our main result shows that in an economy with M = 2 consumers and L goods, if L coordinate functions, representing policies, are geodesics on the equilibrium manifold (a property that we call the finite geodesic property), then the equilibrium is globally unique. The presence of geodesic variables indicates optimization and efficiency in the economy, while non-geodesic variables add complexity. Finally, we establish a link between the existing results on curvature, minimal entropy, geodesics and uniqueness in smooth exchange economies. This study contributes to the understanding of the geometric and economic properties of equilibria and offers potential applications in policy considerations. Keywords: Uniqueness of equilibrium, redistributive policies, geodesics, equilibrium manifold, equilibrium selection, curvature, geodesics.
How to choose a Compatible Committee?
Authors: Ritu Dutta, Rajnish Kumnar, Surajit Borkotokey
Date: 2023-08-07 00:00:00

Electing a committee of size k from m alternatives (k < m) is an interesting problem under the multi-winner voting rules. However, very few committee selection rules found in the literature consider the coalitional possibilities among the alternatives that the voters believe that certain coalitions are more effective and can more efficiently deliver desired outputs. To include such possibilities, in this present study, we consider a committee selection problem (or multi-winner voting problem) where voters are able to express their opinion regarding interdependencies among alternatives. Using a dichotomous preference scale termed generalized approval evaluation we construct an $m$-person coalitional game which is more commonly called a cooperative game with transferable utilities. To identify each alternative's score we use the Shapley value (Shapley, 1953) of the cooperative game we construct for the purpose. Our approach to the committee selection problem emphasizes on an important issue called the compatibility principle. Further, we show that the properties of the Shapley value are well suited in the committee selection context too. We explore several properties of the proposed committee selection rule.
Weighted position value for Network games
Authors: Niharika Kakoty, Surajit Borkotokey, Rajnish Kumar, Abhijit Bora
Date: 2023-08-07 00:00:00
Tasks: Position

In Network games under cooperative framework, the position value is a link based allocation rule. It is obtained from the Shapley value of an associated cooperative game where the links of the network are considered players. The Shapley value of each of the links is then divided equally among the players who form those links. The inherent assumption is that the value is indifferent to the weights of the players in the network. Depending on how much central a player is in the network, or the ability of making links with other players etc., for example, players can be considered to have weights. Thus, in such situations, dividing the Shapley value equally among the players can be an over-simplistic notion. We propose a generalised version of the position value: the weighted position value that allocates the Shapley shares proportional to the players' weights. These weights of the players are exogenously given. We provide two axiomatic characterizations of our value. Finally, a bidding mechanism is formulated to show that any sub-game perfect equilibrium (SPE) of this mechanism coincides with the weighted position value.
The Expected Shapley value on a class of probabilistic games
Authors: Surajit Borkotokey, Sujata Gowala, Rajnish Kumar
Date: 2023-08-07 00:00:00

We study a class of probabilistic cooperative games which can be treated as an extension of the classical cooperative games with transferable utilities. The coalitions have an exogenous probability of being realized. This probability distribution is known beforehand and the distribution of the expected worth needs to be done before the realization of the state. We obtain a value for this class of games and present three characterizations of this value using natural extensions of the axioms used in the seminal axiomatization of the Shapley value. The value, which we call the Expected Shapley value, allocates the players their expected worth with respect to a probability distribution.
A comprehensive review of deep learning in lung cancer
Authors: Farzane Tajidini
Date: 2023-07-31 00:00:00
Tasks: Cancer Classification, Classification, Deep Learning

To provide the reader with a historical perspective on cancer classification approaches, we first discuss the fundamentals of the area of cancer diagnosis in this article, including the processes of cancer diagnosis and the standard classification methods employed by clinicians. Current methods for cancer diagnosis are deemed ineffective, calling for new and more intelligent approaches.
Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks
Authors: Vincent Bürgin, Raphael Prevost, Marijn F. Stollenga
Date: 2023-07-27 00:00:00
Tasks: Graph Neural Network

Automatic vertebra localization and identification in CT scans is important for numerous clinical applications. Much progress has been made on this topic, but it mostly targets positional localization of vertebrae, ignoring their orientation. Additionally, most methods employ heuristics in their pipeline that can be sensitive in real clinical images which tend to contain abnormalities. We introduce a simple pipeline that employs a standard prediction with a U-Net, followed by a single graph neural network to associate and classify vertebrae with full orientation. To test our method, we introduce a new vertebra dataset that also contains pedicle detections that are associated with vertebra bodies, creating a more challenging landmark prediction, association and classification task. Our method is able to accurately associate the correct body and pedicle landmarks, ignore false positives and classify vertebrae in a simple, fully trainable pipeline avoiding application-specific heuristics. We show our method outperforms traditional approaches such as Hungarian Matching and Hidden Markov Models. We also show competitive performance on the standard VerSe challenge body identification task.
A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe
Authors: Angelica Urbanelli, Luca Barco, Edoardo Arnaudo, Claudio Rossi
Date: 2023-07-27 00:00:00

The increasing frequency of catastrophic natural events, such as wildfires, calls for the development of rapid and automated wildfire detection systems. In this paper, we propose a wildfire identification solution to improve the accuracy of automated satellite-based hotspot detection systems by leveraging multiple information sources. We cross-reference the thermal anomalies detected by the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) hotspot services with the European Forest Fire Information System (EFFIS) database to construct a large-scale hotspot dataset for wildfire-related studies in Europe. Then, we propose a novel multimodal supervised machine learning approach to disambiguate hotspot detections, distinguishing between wildfires and other events. Our methodology includes the use of multimodal data sources, such as the ERSI annual Land Use Land Cover (LULC) and the Copernicus Sentinel-3 data. Experimental results demonstrate the effectiveness of our approach in the task of wildfire identification.
Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting
Authors: Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly
Date: 2023-07-23 00:00:00
Tasks: Data Augmentation, Diversity

In biomedical image analysis, data imbalance is common across several imaging modalities. Data augmentation is one of the key solutions in addressing this limitation. Generative Adversarial Networks (GANs) are increasingly being relied upon for data augmentation tasks. Biomedical image features are sensitive to evaluating the efficacy of synthetic images. These features can have a significant impact on metric scores when evaluating synthetic images across different biomedical imaging modalities. Synthetically generated images can be evaluated by comparing the diversity and quality of real images. Multi-scale Structural Similarity Index Measure and Cosine Distance are used to evaluate intra-class diversity, while Frechet Inception Distance is used to evaluate the quality of synthetic images. Assessing these metrics for biomedical and non-biomedical imaging is important to investigate an informed strategy in evaluating the diversity and quality of synthetic images. In this work, an empirical assessment of these metrics is conducted for the Deep Convolutional GAN in a biomedical and non-biomedical setting. The diversity and quality of synthetic images are evaluated using different sample sizes. This research intends to investigate the variance in diversity and quality across biomedical and non-biomedical imaging modalities. Results demonstrate that the metrics scores for diversity and quality vary significantly across biomedical-to-biomedical and biomedical-to-non-biomedical imaging modalities.
The identification of garbage dumps in the rural areas of Cyprus through the application of deep learning to satellite imagery
Authors: Andrew Keith Wilkinson
Date: 2023-07-23 00:00:00
Tasks: Data Augmentation

Garbage disposal is a challenging problem throughout the developed world. In Cyprus, as elsewhere, illegal ``fly-tipping" is a significant issue, especially in rural areas where few legal garbage disposal options exist. However, there is a lack of studies that attempt to measure the scale of this problem, and few resources available to address it. A method of automating the process of identifying garbage dumps would help counter this and provide information to the relevant authorities. The aim of this study was to investigate the degree to which artificial intelligence techniques, together with satellite imagery, can be used to identify illegal garbage dumps in the rural areas of Cyprus. This involved collecting a novel dataset of images that could be categorised as either containing, or not containing, garbage. The collection of such datasets in sufficient raw quantities is time consuming and costly. Therefore a relatively modest baseline set of images was collected, then data augmentation techniques used to increase the size of this dataset to a point where useful machine learning could occur. From this set of images an artificial neural network was trained to recognise the presence or absence of garbage in new images. A type of neural network especially suited to this task known as ``convolutional neural networks" was used. The efficacy of the resulting model was evaluated using an independently collected dataset of test images. The result was a deep learning model that could correctly identify images containing garbage in approximately 90\% of cases. It is envisaged that this model could form the basis of a future system that could systematically analyse the entire landscape of Cyprus to build a comprehensive ``garbage" map of the island.
Learning to Segment from Noisy Annotations: A Spatial Correction Approach
Authors: Jiachen Yao, Yikai Zhang, Songzhu Zheng, Mayank Goswami, Prateek Prasanna, Chao Chen
Date: 2023-07-21 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in annotation time and in the annotators' expertise. Existing methods mostly assume noisy labels in different pixels are \textit{i.i.d}. However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution. In this paper, we propose a novel Markov model for segmentation noisy annotations that encodes both spatial correlation and bias. Further, to mitigate such label noise, we propose a label correction method to recover true label progressively. We provide theoretical guarantees of the correctness of the proposed method. Experiments show that our approach outperforms current state-of-the-art methods on both synthetic and real-world noisy annotations.
Body Fat Estimation from Surface Meshes using Graph Neural Networks
Authors: Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
Date: 2023-07-13 00:00:00

Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estimation are the body mass index (BMI), waist circumference, or the waist-hip-ratio. However, those are rather imprecise measures that do not allow for a discrimination between different types of fat or between fat and muscle tissue. The estimation of visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volume has shown to be a more accurate measure for named risk factors. In this work, we show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area. We furthermore envision this method to be applicable to cheaper and easily accessible medical surface scans instead of expensive medical images.