pwc-archive/papers-with-abstracts huggingface dataset:
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications
Authors: Xingbo Fu, Binchi Zhang, Yushun Dong, Chen Chen, Jundong Li
Date: 2022-07-24 00:00:00
Tasks: BIG-bench Machine Learning

Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are trained over massive graph data. However, in many real-world scenarios, such as hospitalization prediction in healthcare systems, the graph data is usually stored at multiple data owners and cannot be directly accessed by any other parties due to privacy concerns and regulation restrictions. Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner. In this survey, we conduct a comprehensive review of the literature in FGML. Specifically, we first provide a new taxonomy to divide the existing problems in FGML into two settings, namely, FL with structured data and structured FL. Then, we review the mainstream techniques in each setting and elaborate on how they address the challenges under FGML. In addition, we summarize the real-world applications of FGML from different domains and introduce open graph datasets and platforms adopted in FGML. Finally, we present several limitations in the existing studies with promising research directions in this field.
Privacy Against Inference Attacks in Vertical Federated Learning
Authors: Borzoo Rassouli, Morteza Varasteh, Deniz Gunduz
Date: 2022-07-24 00:00:00
Tasks: Federated Learning, Inference Attack, Privacy Preserving, Vertical Federated Learning

Vertical federated learning is considered, where an active party, having access to true class labels, wishes to build a classification model by utilizing more features from a passive party, which has no access to the labels, to improve the model accuracy. In the prediction phase, with logistic regression as the classification model, several inference attack techniques are proposed that the adversary, i.e., the active party, can employ to reconstruct the passive party's features, regarded as sensitive information. These attacks, which are mainly based on a classical notion of the center of a set, i.e., the Chebyshev center, are shown to be superior to those proposed in the literature. Moreover, several theoretical performance guarantees are provided for the aforementioned attacks. Subsequently, we consider the minimum amount of information that the adversary needs to fully reconstruct the passive party's features. In particular, it is shown that when the passive party holds one feature, and the adversary is only aware of the signs of the parameters involved, it can perfectly reconstruct that feature when the number of predictions is large enough. Next, as a defense mechanism, a privacy-preserving scheme is proposed that worsen the adversary's reconstruction attacks, while preserving the full benefits that VFL brings to the active party. Finally, experimental results demonstrate the effectiveness of the proposed attacks and the privacy-preserving scheme.
Physics-Informed Learning of Aerosol Microphysics
Authors: Paula Harder, Duncan Watson-Parris, Philip Stier, Dominik Strassel, Nicolas R. Gauger, Janis Keuper
Date: 2022-07-24 00:00:00
Tasks: GPU

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. In order to represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. We are able to learn the variables' tendencies achieving an average $R^2$ score of $77.1\% $. We further explore methods to inform and constrain the neural network with physical knowledge to reduce mass violation and enforce mass positivity. On a GPU we achieve a speed-up of up to over 64x compared to the original model.
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Authors: Ramneet Kaur, Kaustubh Sridhar, Sangdon Park, Susmit Jha, Anirban Roy, Oleg Sokolsky, Insup Lee
Date: 2022-07-24 00:00:00
Tasks: Anomaly Detection, Autonomous Driving, Autonomous Vehicles, Conformal Prediction, Out-of-Distribution Detection, Out of Distribution (OOD) Detection, Prediction Intervals, Time Series, Time Series Analysis

Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution. This hinders their deployment in safety-critical applications such as autonomous vehicles and healthcare. The detection of a shift from the training distribution of individual datapoints has gained attention. A number of techniques have been proposed for such out-of-distribution (OOD) detection. But in many applications, the inputs to a machine learning model form a temporal sequence. Existing techniques for OOD detection in time-series data either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data.Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher's method leads to the proposed detector CODiT with guarantees on false detection in time-series data. We illustrate the efficacy of CODiT by achieving state-of-the-art results on computer vision datasets in autonomous driving. We also show that CODiT can be used for OOD detection in non-vision datasets by performing experiments on the physiological GAIT sensory dataset. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD.
SGAT: Simplicial Graph Attention Network
Authors: See Hian Lee, Feng Ji, Wee Peng Tay
Date: 2022-07-24 00:00:00
Tasks: Graph Attention, Graph Learning, Graph Neural Network, Node Classification

Heterogeneous graphs have multiple node and edge types and are semantically richer than homogeneous graphs. To learn such complex semantics, many graph neural network approaches for heterogeneous graphs use metapaths to capture multi-hop interactions between nodes. Typically, features from non-target nodes are not incorporated into the learning procedure. However, there can be nonlinear, high-order interactions involving multiple nodes or edges. In this paper, we present Simplicial Graph Attention Network (SGAT), a simplicial complex approach to represent such high-order interactions by placing features from non-target nodes on the simplices. We then use attention mechanisms and upper adjacencies to generate representations. We empirically demonstrate the efficacy of our approach with node classification tasks on heterogeneous graph datasets and further show SGAT's ability in extracting structural information by employing random node features. Numerical experiments indicate that SGAT performs better than other current state-of-the-art heterogeneous graph learning methods.
From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach
Authors: Jesse Read
Date: 2022-07-24 00:00:00
Tasks: Multi-Label Learning, Multi-Task Learning, Transfer Learning

In multi-label learning, a particular case of multi-task learning where a single data point is associated with multiple target labels, it was widely assumed in the literature that, to obtain best accuracy, the dependence among the labels should be explicitly modeled. This premise led to a proliferation of methods offering techniques to learn and predict labels together, for example where the prediction for one label influences predictions for other labels. Even though it is now acknowledged that in many contexts a model of dependence is not required for optimal performance, such models continue to outperform independent models in some of those very contexts, suggesting alternative explanations for their performance beyond label dependence, which the literature is only recently beginning to unravel. Leveraging and extending recent discoveries, we turn the original premise of multi-label learning on its head, and approach the problem of joint-modeling specifically under the absence of any measurable dependence among task labels; for example, when task labels come from separate problem domains. We shift insights from this study towards building an approach for transfer learning that challenges the long-held assumption that transferability of tasks comes from measurements of similarity between the source and target domains or models. This allows us to design and test a method for transfer learning, which is model driven rather than purely data driven, and furthermore it is black box and model-agnostic (any base model class can be considered). We show that essentially we can create task-dependence based on source-model capacity. The results we obtain have important implications and provide clear directions for future work, both in the areas of multi-label and transfer learning.
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems
Authors: Yating Wang, Wing Tat Leung, Guang Lin
Date: 2022-07-24 00:00:00
Tasks: Sparse Learning

In this work, we propose an adaptive sparse learning algorithm that can be applied to learn the physical processes and obtain a sparse representation of the solution given a large snapshot space. Assume that there is a rich class of precomputed basis functions that can be used to approximate the quantity of interest. We then design a neural network architecture to learn the coefficients of solutions in the spaces which are spanned by these basis functions. The information of the basis functions are incorporated in the loss function, which minimizes the differences between the downscaled reduced order solutions and reference solutions at multiple time steps. The network contains multiple submodules and the solutions at different time steps can be learned simultaneously. We propose some strategies in the learning framework to identify important degrees of freedom. To find a sparse solution representation, a soft thresholding operator is applied to enforce the sparsity of the output coefficient vectors of the neural network. To avoid over-simplification and enrich the approximation space, some degrees of freedom can be added back to the system through a greedy algorithm. In both scenarios, that is, removing and adding degrees of freedom, the corresponding network connections are pruned or reactivated guided by the magnitude of the solution coefficients obtained from the network outputs. The proposed adaptive learning process is applied to some toy case examples to demonstrate that it can achieve a good basis selection and accurate approximation. More numerical tests are performed on two-phase multiscale flow problems to show the capability and interpretability of the proposed method on complicated applications.
Gradient-based Bi-level Optimization for Deep Learning: A Survey
Authors: Can Chen, Xi Chen, Chen Ma, Zixuan Liu, Xue Liu
Date: 2022-07-24 00:00:00
Tasks: Deep Learning, Hyperparameter Optimization, Survey

Bi-level optimization, especially the gradient-based category, has been widely used in the deep learning community including hyperparameter optimization and meta-knowledge extraction. Bi-level optimization embeds one problem within another and the gradient-based category solves the outer-level task by computing the hypergradient, which is much more efficient than classical methods such as the evolutionary algorithm. In this survey, we first give a formal definition of the gradient-based bi-level optimization. Next, we delineate criteria to determine if a research problem is apt for bi-level optimization and provide a practical guide on structuring such problems into a bi-level optimization framework, a feature particularly beneficial for those new to this domain. More specifically, there are two formulations: the single-task formulation to optimize hyperparameters such as regularization parameters and the distilled data, and the multi-task formulation to extract meta-knowledge such as the model initialization. With a bi-level formulation, we then discuss four bi-level optimization solvers to update the outer variable including explicit gradient update, proxy update, implicit function update, and closed-form update. Finally, we wrap up the survey by highlighting two prospective future directions: (1) Effective Data Optimization for Science examined through the lens of task formulation. (2) Accurate Explicit Proxy Update analyzed from an optimization standpoint.
A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues
Authors: Jason Armitage, Leonardo Impett, Rico Sennrich
Date: 2022-07-24 00:00:00
Tasks: cross-modal alignment, Trajectory Planning, Vision and Language Navigation

In a busy city street, a pedestrian surrounded by distractions can pick out a single sign if it is relevant to their route. Artificial agents in outdoor Vision-and-Language Navigation (VLN) are also confronted with detecting supervisory signal on environment features and location in inputs. To boost the prominence of relevant features in transformer-based architectures without costly preprocessing and pretraining, we take inspiration from priority maps - a mechanism described in neuropsychological studies. We implement a novel priority map module and pretrain on auxiliary tasks using low-sample datasets with high-level representations of routes and environment-related references to urban features. A hierarchical process of trajectory planning - with subsequent parameterised visual boost filtering on visual inputs and prediction of corresponding textual spans - addresses the core challenges of cross-modal alignment and feature-level localisation. The priority map module is integrated into a feature-location framework that doubles the task completion rates of standalone transformers and attains state-of-the-art performance on the Touchdown benchmark for VLN. Code and data are referenced in Appendix C.
Towards an Improved Understanding of Software Vulnerability Assessment Using Data-Driven Approaches
Authors: Triet H. M. Le
Date: 2022-07-24 00:00:00

The thesis advances the field of software security by providing knowledge and automation support for software vulnerability assessment using data-driven approaches. Software vulnerability assessment provides important and multifaceted information to prevent and mitigate dangerous cyber-attacks in the wild. The key contributions include a systematisation of knowledge, along with a suite of novel data-driven techniques and practical recommendations for researchers and practitioners in the area. The thesis results help improve the understanding and inform the practice of assessing ever-increasing vulnerabilities in real-world software systems. This in turn enables more thorough and timely fixing prioritisation and planning of these critical security issues.
HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry
Authors: Xinyu Li
Date: 2022-07-24 00:00:00
Tasks: Classification, Sound Classification

Machine sound classification has been one of the fundamental tasks of music technology. A major branch of sound classification is the classification of music genres. However, though covering most genres of music, existing music genre datasets often do not contain fine-grained labels that indicate the detailed sub-genres of music. In consideration of the consistency of genres of songs in a mixtape or in a DJ (live) set, we have collected and annotated a dataset of house music that provide 4 sub-genre labels, namely future house, bass house, progressive house and melodic house. Experiments show that our annotations well exhibit the characteristics of different categories. Also, we have built baseline models that classify the sub-genre based on the mel-spectrograms of a track, achieving strongly competitive results. Besides, we have put forward a few application scenarios of our dataset and baseline model, with a simulated sci-fi tunnel as a short demo built and rendered in a 3D modeling software, with the colors of the lights automated by the output of our model.
Distributed Robust Principal Component Analysis
Authors: Wenda Chu
Date: 2022-07-24 00:00:00

We study the robust principal component analysis (RPCA) problem in a distributed setting. The goal of RPCA is to find an underlying low-rank estimation for a raw data matrix when the data matrix is subject to the corruption of gross sparse errors. Previous studies have developed RPCA algorithms that provide stable solutions with fast convergence. However, these algorithms are typically hard to scale and cannot be implemented distributedly, due to the use of either SVD or large matrix multiplication. In this paper, we propose the first distributed robust principal analysis algorithm based on consensus factorization, dubbed DCF-PCA. We prove the convergence of DCF-PCA and evaluate DCF-PCA on various problem setting
BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare
Authors: Moirangthem Biken Singh, Ajay Pratap
Date: 2022-07-24 00:00:00
Tasks: Federated Learning, Privacy Preserving

This paper proposes Federated Learning (FL) based smart healthcare system where Medical Centers (MCs) train the local model using the data collected from patients and send the model weights to the miners in a blockchain-based robust framework without sharing raw data, keeping privacy preservation into deliberation. We formulate an optimization problem by maximizing the utility and minimizing the loss function considering energy consumption and FL process delay of MCs for learning effective models on distributed healthcare data underlying a blockchain-based framework. We propose a solution in two stages: first, offer a stable matching-based association algorithm to maximize the utility of both miners and MCs and then solve loss minimization using Stochastic Gradient Descent (SGD) algorithm employing FL under Differential Privacy (DP) and blockchain technology. Moreover, we incorporate blockchain technology to provide tempered resistant and decentralized model weight sharing in the proposed FL-based framework. The effectiveness of the proposed model is shown through simulation on real-world healthcare data comparing other state-of-the-art techniques.
OCTAL: Graph Representation Learning for LTL Model Checking
Authors: Prasita Mukherjee, Haoteng Yin, Susheel Suresh, Tiark Rompf
Date: 2022-07-24 00:00:00
Tasks: Binary Classification, Graph Representation Learning, model, Representation Learning

Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, still suffer from the state space explosion problem that makes them impractical for large scale systems and/or specifications. In this paper, we propose to use graph representation learning (GRL) for solving linear temporal logic (LTL) model checking, where the system and the specification are expressed by a B\"uchi automaton and an LTL formula respectively. A novel GRL-based framework OCTAL, is designed to learn the representation of the graph-structured system and specification, which reduces the model checking problem to binary classification in the latent space. The empirical experiments show that OCTAL achieves comparable accuracy against canonical SOTA model checkers on three different datasets, with up to $5\times$ overall speedup and above $63\times$ for satisfiability checking alone.
Reliable amortized variational inference with physics-based latent distribution correction
Authors: Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco, Felix J. Herrmann
Date: 2022-07-24 00:00:00
Tasks: Bayesian Inference, Seismic Imaging, Variational Inference

Bayesian inference for high-dimensional inverse problems is computationally costly and requires selecting a suitable prior distribution. Amortized variational inference addresses these challenges via a neural network that approximates the posterior distribution not only for one instance of data, but a distribution of data pertaining to a specific inverse problem. During inference, the neural network -- in our case a conditional normalizing flow -- provides posterior samples at virtually no cost. However, the accuracy of amortized variational inference relies on the availability of high-fidelity training data, which seldom exists in geophysical inverse problems due to the Earth's heterogeneity. In addition, the network is prone to errors if evaluated over out-of-distribution data. As such, we propose to increase the resilience of amortized variational inference in the presence of moderate data distribution shifts. We achieve this via a correction to the latent distribution that improves the posterior distribution approximation for the data at hand. The correction involves relaxing the standard Gaussian assumption on the latent distribution and parameterizing it via a Gaussian distribution with an unknown mean and (diagonal) covariance. These unknowns are then estimated by minimizing the Kullback-Leibler divergence between the corrected and the (physics-based) true posterior distributions. While generic and applicable to other inverse problems, by means of a linearized seismic imaging example, we show that our correction step improves the robustness of amortized variational inference with respect to changes in the number of seismic sources, noise variance, and shifts in the prior distribution. This approach provides a seismic image with limited artifacts and an assessment of its uncertainty at approximately the same cost as five reverse-time migrations.
Prediction Intervals in the Beta Autoregressive Moving Average Model
Authors: B. G. Palm, F. M. Bayer, R. J. Cintra
Date: 2022-07-24 00:00:00
Tasks: Prediction, Prediction Intervals

In this paper, we propose five prediction intervals for the beta autoregressive moving average model. This model is suitable for modeling and forecasting variables that assume values in the interval $(0,1)$. Two of the proposed prediction intervals are based on approximations considering the normal distribution and the quantile function of the beta distribution. We also consider bootstrap-based prediction intervals, namely: (i) bootstrap prediction errors (BPE) interval; (ii) bias-corrected and acceleration (BCa) prediction interval; and (iii) percentile prediction interval based on the quantiles of the bootstrap-predicted values for two different bootstrapping schemes. The proposed prediction intervals were evaluated according to Monte Carlo simulations. The BCa prediction interval offered the best performance among the evaluated intervals, showing lower coverage rate distortion and small average length. We applied our methodology for predicting the water level of the Cantareira water supply system in S\~ao Paulo, Brazil.