pwc-archive/papers-with-abstracts huggingface dataset:
In-situ Water quality monitoring in Oil and Gas operations
Authors: Satish Kumar, Rui Kou, Henry Hill, Jake Lempges, Eric Qian, Vikram Jayaram
Date: 2023-01-20 00:00:00

From agriculture to mining, to energy, surface water quality monitoring is an essential task. As oil and gas operators work to reduce the consumption of freshwater, it is increasingly important to actively manage fresh and non-fresh water resources over the long term. For large-scale monitoring, manual sampling at many sites has become too time-consuming and unsustainable, given the sheer number of dispersed ponds, small lakes, playas, and wetlands over a large area. Therefore, satellite-based environmental monitoring presents great potential. Many existing satellite-based monitoring studies utilize index-based methods to monitor large water bodies such as rivers and oceans. However, these existing methods fail when monitoring small ponds-the reflectance signal received from small water bodies is too weak to detect. To address this challenge, we propose a new Water Quality Enhanced Index (WQEI) Model, which is designed to enable users to determine contamination levels in water bodies with weak reflectance patterns. Our results show that 1) WQEI is a good indicator of water turbidity validated with 1200 water samples measured in the laboratory, and 2) by applying our method to commonly available satellite data (e.g. LandSat8), one can achieve high accuracy water quality monitoring efficiently in large regions. This provides a tool for operators to optimize the quality of water stored within surface storage ponds and increasing the readiness and availability of non-fresh water.
DeepCOVID-Fuse: A Multi-modality Deep Learning Model Fusing Chest X-Radiographs and Clinical Variables to Predict COVID-19 Risk Levels
Authors: Yunan Wu, Amil Dravid, Ramsey Michael Wehbe, Aggelos K. Katsaggelos
Date: 2023-01-20 00:00:00
Tasks: ICU Admission

Propose: To present DeepCOVID-Fuse, a deep learning fusion model to predict risk levels in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate the performance of pre-trained fusion models on full or partial combination of chest x-ray (CXRs) or chest radiograph and clinical variables. Materials and Methods: The initial CXRs, clinical variables and outcomes (i.e., mortality, intubation, hospital length of stay, ICU admission) were collected from February 2020 to April 2020 with reverse-transcription polymerase chain reaction (RT-PCR) test results as the reference standard. The risk level was determined by the outcome. The fusion model was trained on 1657 patients (Age: 58.30 +/- 17.74; Female: 807) and validated on 428 patients (56.41 +/- 17.03; 190) from Northwestern Memorial HealthCare system and was tested on 439 patients (56.51 +/- 17.78; 205) from a single holdout hospital. Performance of pre-trained fusion models on full or partial modalities were compared on the test set using the DeLong test for the area under the receiver operating characteristic curve (AUC) and the McNemar test for accuracy, precision, recall and F1. Results: The accuracy of DeepCOVID-Fuse trained on CXRs and clinical variables is 0.658, with an AUC of 0.842, which significantly outperformed (p < 0.05) models trained only on CXRs with an accuracy of 0.621 and AUC of 0.807 and only on clinical variables with an accuracy of 0.440 and AUC of 0.502. The pre-trained fusion model with only CXRs as input increases accuracy to 0.632 and AUC to 0.813 and with only clinical variables as input increases accuracy to 0.539 and AUC to 0.733. Conclusion: The fusion model learns better feature representations across different modalities during training and achieves good outcome predictions even when only some of the modalities are used in testing.
Visual Semantic Relatedness Dataset for Image Captioning
Authors: Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró
Date: 2023-01-20 00:00:00
Tasks: Image Captioning, text similarity

Modern image captioning system relies heavily on extracting knowledge from images to capture the concept of a static story. In this paper, we propose a textual visual context dataset for captioning, in which the publicly available dataset COCO Captions (Lin et al., 2014) has been extended with information about the scene (such as objects in the image). Since this information has a textual form, it can be used to leverage any NLP task, such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach.
An Asynchronous Intensity Representation for Framed and Event Video Sources
Authors: Andrew C. Freeman, Montek Singh, Ketan Mayer-Patel
Date: 2023-01-20 00:00:00
Tasks: Computational Efficiency

Neuromorphic "event" cameras, designed to mimic the human vision system with asynchronous sensing, unlock a new realm of high-speed and high dynamic range applications. However, researchers often either revert to a framed representation of event data for applications, or build bespoke applications for a particular camera's event data type. To usher in the next era of video systems, accommodate new event camera designs, and explore the benefits to asynchronous video in classical applications, we argue that there is a need for an asynchronous, source-agnostic video representation. In this paper, we introduce a novel, asynchronous intensity representation for both framed and non-framed data sources. We show that our representation can increase intensity precision and greatly reduce the number of samples per pixel compared to grid-based representations. With framed sources, we demonstrate that by permitting a small amount of loss through the temporal averaging of similar pixel values, we can reduce our representational sample rate by more than half, while incurring a drop in VMAF quality score of only 4.5. We also demonstrate lower latency than the state-of-the-art method for fusing and transcoding framed and event camera data to an intensity representation, while maintaining $2000\times$ the temporal resolution. We argue that our method provides the computational efficiency and temporal granularity necessary to build real-time intensity-based applications for event cameras.
Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation
Authors: Christian Schmidt, Heinrich Martin Overhoff
Date: 2023-01-20 00:00:00
Tasks: Feature Engineering, GPU, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Cardiac image segmentation is a powerful tool in regard to diagnostics and treatment of cardiovascular diseases. Purely feature-based detection of anatomical structures like the mitral valve is a laborious task due to specifically required feature engineering and is especially challenging in echocardiograms, because of their inherently low contrast and blurry boundaries between some anatomical structures. With the publication of further annotated medical datasets and the increase in GPU processing power, deep learning-based methods in medical image segmentation became more feasible in the past years. We propose a fully automatic detection method for mitral valve hinge points, which uses a U-Net based deep neural net to segment cardiac chambers in echocardiograms in a first step, and subsequently extracts the mitral valve hinge points from the resulting segmentations in a second step. Results measured with this automatic detection method were compared to reference coordinate values, which with median absolute hinge point coordinate errors of 1.35 mm for the x- (15-85 percentile range: [0.3 mm; 3.15 mm]) and 0.75 mm for the y- coordinate (15-85 percentile range: [0.15 mm; 1.88 mm]).
Towards Robust Video Instance Segmentation with Temporal-Aware Transformer
Authors: Zhenghao Zhang, Fangtao Shao, Zuozhuo Dai, Siyu Zhu
Date: 2023-01-20 00:00:00
Tasks: Decoder, Instance Segmentation, Semantic Segmentation, Video Instance Segmentation

Most existing transformer based video instance segmentation methods extract per frame features independently, hence it is challenging to solve the appearance deformation problem. In this paper, we observe the temporal information is important as well and we propose TAFormer to aggregate spatio-temporal features both in transformer encoder and decoder. Specifically, in transformer encoder, we propose a novel spatio-temporal joint multi-scale deformable attention module which dynamically integrates the spatial and temporal information to obtain enriched spatio-temporal features. In transformer decoder, we introduce a temporal self-attention module to enhance the frame level box queries with the temporal relation. Moreover, TAFormer adopts an instance level contrastive loss to increase the discriminability of instance query embeddings. Therefore the tracking error caused by visually similar instances can be decreased. Experimental results show that TAFormer effectively leverages the spatial and temporal information to obtain context-aware feature representation and outperforms state-of-the-art methods.
The Impossibility of Parallelizing Boosting
Authors: Amin Karbasi, Kasper Green Larsen
Date: 2023-01-23 00:00:00

The aim of boosting is to convert a sequence of weak learners into a strong learner. At their heart, these methods are fully sequential. In this paper, we investigate the possibility of parallelizing boosting. Our main contribution is a strong negative result, implying that significant parallelization of boosting requires an exponential blow-up in the total computing resources needed for training.
Explaining Deep Learning Hidden Neuron Activations using Concept Induction
Authors: Abhilekha Dalal, Md Kamruzzaman Sarker, Adrita Barua, Pascal Hitzler
Date: 2023-01-23 00:00:00
Tasks: Deep Learning

One of the current key challenges in Explainable AI is in correctly interpreting activations of hidden neurons. It seems evident that accurate interpretations thereof would provide insights into the question what a deep learning system has internally \emph{detected} as relevant on the input, thus lifting some of the black box character of deep learning systems. The state of the art on this front indicates that hidden node activations appear to be interpretable in a way that makes sense to humans, at least in some cases. Yet, systematic automated methods that would be able to first hypothesize an interpretation of hidden neuron activations, and then verify it, are mostly missing. In this paper, we provide such a method and demonstrate that it provides meaningful interpretations. It is based on using large-scale background knowledge -- a class hierarchy of approx. 2 million classes curated from the Wikipedia Concept Hierarchy -- together with a symbolic reasoning approach called \emph{concept induction} based on description logics that was originally developed for applications in the Semantic Web field. Our results show that we can automatically attach meaningful labels from the background knowledge to individual neurons in the dense layer of a Convolutional Neural Network through a hypothesis and verification process.
FedExP: Speeding Up Federated Averaging via Extrapolation
Authors: Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi
Date: 2023-01-23 00:00:00
Tasks: Federated Learning

Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning (FL) optimization due to its simple implementation, stateless nature, and privacy guarantees combined with secure aggregation. Recent work has sought to generalize the vanilla averaging in FedAvg to a generalized gradient descent step by treating client updates as pseudo-gradients and using a server step size. While the use of a server step size has been shown to provide performance improvement theoretically, the practical benefit of the server step size has not been seen in most existing works. In this work, we present FedExP, a method to adaptively determine the server step size in FL based on dynamically varying pseudo-gradients throughout the FL process. We begin by considering the overparameterized convex regime, where we reveal an interesting similarity between FedAvg and the Projection Onto Convex Sets (POCS) algorithm. We then show how FedExP can be motivated as a novel extension to the extrapolation mechanism that is used to speed up POCS. Our theoretical analysis later also discusses the implications of FedExP in underparameterized and non-convex settings. Experimental results show that FedExP consistently converges faster than FedAvg and competing baselines on a range of realistic FL datasets.
Huber-Robust Confidence Sequences
Authors: Hongjian Wang, Aaditya Ramdas
Date: 2023-01-23 00:00:00
Tasks: valid

Confidence sequences are confidence intervals that can be sequentially tracked, and are valid at arbitrary data-dependent stopping times. This paper presents confidence sequences for a univariate mean of an unknown distribution with a known upper bound on the $p$-th central moment ($p$ > 1), but allowing for (at most) $\epsilon$ fraction of arbitrary distribution corruption, as in Huber's contamination model. We do this by designing new robust exponential supermartingales, and show that the resulting confidence sequences attain the optimal width achieved in the nonsequential setting. Perhaps surprisingly, the constant margin between our sequential result and the lower bound is smaller than even fixed-time robust confidence intervals based on the trimmed mean, for example. Since confidence sequences are a common tool used within A/B/n testing and bandits, these results open the door to sequential experimentation that is robust to outliers and adversarial corruptions.
Deep Learning Meets Sparse Regularization: A Signal Processing Perspective
Authors: Rahul Parhi, Robert D. Nowak
Date: 2023-01-23 00:00:00
Tasks: Deep Learning

Deep learning has been wildly successful in practice and most state-of-the-art machine learning methods are based on neural networks. Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of deep neural networks. In this article, we present a relatively new mathematical framework that provides the beginning of a deeper understanding of deep learning. This framework precisely characterizes the functional properties of neural networks that are trained to fit to data. The key mathematical tools which support this framework include transform-domain sparse regularization, the Radon transform of computed tomography, and approximation theory, which are all techniques deeply rooted in signal processing. This framework explains the effect of weight decay regularization in neural network training, the use of skip connections and low-rank weight matrices in network architectures, the role of sparsity in neural networks, and explains why neural networks can perform well in high-dimensional problems.
WDC Products: A Multi-Dimensional Entity Matching Benchmark
Authors: Ralph Peeters, Reng Chiz Der, Christian Bizer
Date: 2023-01-23 00:00:00
Tasks: Contrastive Learning, Data Integration, Entity Resolution, Multi-class Classification

The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the size of the development set. Current entity matching benchmarks usually represent single points in the space along such dimensions or they provide for the evaluation of matching methods along a single dimension, for instance the amount of training data. This paper presents WDC Products, an entity matching benchmark which provides for the systematic evaluation of matching systems along combinations of three dimensions while relying on real-world data. The three dimensions are (i) amount of corner-cases (ii) generalization to unseen entities, and (iii) development set size (training set plus validation set). Generalization to unseen entities is a dimension not covered by any of the existing English-language benchmarks yet but is crucial for evaluating the robustness of entity matching systems. Instead of learning how to match entity pairs, entity matching can also be formulated as a multi-class classification task that requires the matcher to recognize individual entities. WDC Products is the first benchmark that provides a pair-wise and a multi-class formulation of the same tasks. We evaluate WDC Products using several state-of-the-art matching systems, including Ditto, HierGAT, and R-SupCon. The evaluation shows that all matching systems struggle with unseen entities to varying degrees. It also shows that for entity matching contrastive learning is more training data efficient compared to cross-encoders.
A New Approach to Learning Linear Dynamical Systems
Authors: Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau
Date: 2023-01-23 00:00:00

Linear dynamical systems are the foundational statistical model upon which control theory is built. Both the celebrated Kalman filter and the linear quadratic regulator require knowledge of the system dynamics to provide analytic guarantees. Naturally, learning the dynamics of a linear dynamical system from linear measurements has been intensively studied since Rudolph Kalman's pioneering work in the 1960's. Towards these ends, we provide the first polynomial time algorithm for learning a linear dynamical system from a polynomial length trajectory up to polynomial error in the system parameters under essentially minimal assumptions: observability, controllability, and marginal stability. Our algorithm is built on a method of moments estimator to directly estimate Markov parameters from which the dynamics can be extracted. Furthermore, we provide statistical lower bounds when our observability and controllability assumptions are violated.
Sampling-based Nyström Approximation and Kernel Quadrature
Authors: Satoshi Hayakawa, Harald Oberhauser, Terry Lyons
Date: 2023-01-23 00:00:00
Tasks: Learning Theory

We analyze the Nystr\"om approximation of a positive definite kernel associated with a probability measure. We first prove an improved error bound for the conventional Nystr\"om approximation with i.i.d. sampling and singular-value decomposition in the continuous regime; the proof techniques are borrowed from statistical learning theory. We further introduce a refined selection of subspaces in Nystr\"om approximation with theoretical guarantees that is applicable to non-i.i.d. landmark points. Finally, we discuss their application to convex kernel quadrature and give novel theoretical guarantees as well as numerical observations.
On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
Authors: Lu Xia, Michiel E. Hochstenbach, Stefano Massei
Date: 2023-01-23 00:00:00

When training neural networks with low-precision computation, rounding errors often cause stagnation or are detrimental to the convergence of the optimizers; in this paper we study the influence of rounding errors on the convergence of the gradient descent method for problems satisfying the Polyak-\Lojasiewicz inequality. Within this context, we show that, in contrast, biased stochastic rounding errors may be beneficial since choosing a proper rounding strategy eliminates the vanishing gradient problem and forces the rounding bias in a descent direction. Furthermore, we obtain a bound on the convergence rate that is stricter than the one achieved by unbiased stochastic rounding. The theoretical analysis is validated by comparing the performances of various rounding strategies when optimizing several examples using low-precision fixed-point number formats.
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model
Authors: Nikolaos Nakis, Abdulkadir Çelikkanat, Louis Boucherie, Christian Djurhuus, Felix Burmester, Daniel Mathias Holmelund, Monika Frolcová, Morten Mørup
Date: 2023-01-23 00:00:00
Tasks: Graph Representation Learning, Representation Learning

Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typically, these representation learning approaches embed the networks into a low-dimensional space in which the role of each individual can be characterized in terms of their latent position. A major current concern in social networks is the emergence of polarization and filter bubbles promoting a mindset of "us-versus-them" that may be defined by extreme positions believed to ultimately lead to political violence and the erosion of democracy. Such polarized networks are typically characterized in terms of signed links reflecting likes and dislikes. We propose the latent Signed relational Latent dIstance Model (SLIM) utilizing for the first time the Skellam distribution as a likelihood function for signed networks and extend the modeling to the characterization of distinct extreme positions by constraining the embedding space to polytopes. On four real social signed networks of polarization, we demonstrate that the model extracts low-dimensional characterizations that well predict friendships and animosity while providing interpretable visualizations defined by extreme positions when endowing the model with an embedding space restricted to polytopes.
Rethinking the Expressive Power of GNNs via Graph Biconnectivity
Authors: Bohang Zhang, Shengjie Luo, LiWei Wang, Di He
Date: 2023-01-23 00:00:00

Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-structured data. While numerous approaches have been proposed to improve GNNs in terms of the Weisfeiler-Lehman (WL) test, generally there is still a lack of deep understanding of what additional power they can systematically and provably gain. In this paper, we take a fundamentally different perspective to study the expressive power of GNNs beyond the WL test. Specifically, we introduce a novel class of expressivity metrics via graph biconnectivity and highlight their importance in both theory and practice. As biconnectivity can be easily calculated using simple algorithms that have linear computational costs, it is natural to expect that popular GNNs can learn it easily as well. However, after a thorough review of prior GNN architectures, we surprisingly find that most of them are not expressive for any of these metrics. The only exception is the ESAN framework, for which we give a theoretical justification of its power. We proceed to introduce a principled and more efficient approach, called the Generalized Distance Weisfeiler-Lehman (GD-WL), which is provably expressive for all biconnectivity metrics. Practically, we show GD-WL can be implemented by a Transformer-like architecture that preserves expressiveness and enjoys full parallelizability. A set of experiments on both synthetic and real datasets demonstrates that our approach can consistently outperform prior GNN architectures.