pwc-archive/papers-with-abstracts huggingface dataset:
Observer-Feedback-Feedforward Controller Structures in Reinforcement Learning
Authors: Ruoqi Zhang, Per Mattson, Torbjörn Wigren
Date: 2023-04-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

The paper proposes the use of structured neural networks for reinforcement learning based nonlinear adaptive control. The focus is on partially observable systems, with separate neural networks for the state and feedforward observer and the state feedback and feedforward controller. The observer dynamics are modelled by recurrent neural networks while a standard network is used for the controller. As discussed in the paper, this leads to a separation of the observer dynamics to the recurrent neural network part, and the state feedback to the feedback and feedforward network. The structured approach reduces the computational complexity and gives the reinforcement learning based controller an {\em understandable} structure as compared to when one single neural network is used. As shown by simulation the proposed structure has the additional and main advantage that the training becomes significantly faster. Two ways to include feedforward structure are presented, one related to state feedback control and one related to classical feedforward control. The latter method introduces further structure with a separate recurrent neural network that processes only the measured disturbance. When evaluated with simulation on a nonlinear cascaded double tank process, the method with most structure performs the best, with excellent feedforward disturbance rejection gains.
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces
Authors: Shuhei Watanabe, Archit Bansal, Frank Hutter
Date: 2023-04-20 00:00:00
Tasks: Hyperparameter Optimization

The recent rise in popularity of Hyperparameter Optimization (HPO) for deep learning has highlighted the role that good hyperparameter (HP) space design can play in training strong models. In turn, designing a good HP space is critically dependent on understanding the role of different HPs. This motivates research on HP Importance (HPI), e.g., with the popular method of functional ANOVA (f-ANOVA). However, the original f-ANOVA formulation is inapplicable to the subspaces most relevant to algorithm designers, such as those defined by top performance. To overcome this issue, we derive a novel formulation of f-ANOVA for arbitrary subspaces and propose an algorithm that uses Pearson divergence (PED) to enable a closed-form calculation of HPI. We demonstrate that this new algorithm, dubbed PED-ANOVA, is able to successfully identify important HPs in different subspaces while also being extremely computationally efficient.
Hotelling Deflation on Large Symmetric Spiked Tensors
Authors: Mohamed El Amine Seddik, José Henrique de Morais Goulart, Maxime Guillaud
Date: 2023-04-20 00:00:00

This paper studies the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise. Specifically, we provide a precise characterization of the large-dimensional performance of deflation in terms of the alignments of the vectors obtained by successive rank-1 approximation and of their estimated weights, assuming non-trivial (fixed) correlations among spike components. Our analysis allows an understanding of the deflation mechanism in the presence of noise and can be exploited for designing more efficient signal estimation methods.
Filter-Aware Model-Predictive Control
Authors: Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt, Justin Bayer
Date: 2023-04-20 00:00:00
Tasks: model, Model Predictive Control, Visual Navigation

Partially-observable problems pose a trade-off between reducing costs and gathering information. They can be solved optimally by planning in belief space, but that is often prohibitively expensive. Model-predictive control (MPC) takes the alternative approach of using a state estimator to form a belief over the state, and then plan in state space. This ignores potential future observations during planning and, as a result, cannot actively increase or preserve the certainty of its own state estimate. We find a middle-ground between planning in belief space and completely ignoring its dynamics by only reasoning about its future accuracy. Our approach, filter-aware MPC, penalises the loss of information by what we call "trackability", the expected error of the state estimator. We show that model-based simulation allows condensing trackability into a neural network, which allows fast planning. In experiments involving visual navigation, realistic every-day environments and a two-link robot arm, we show that filter-aware MPC vastly improves regular MPC.
Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation
Authors: Fanny Lehmann, Filippo Gatti, Michaël Bertin, Didier Clouteau
Date: 2023-04-20 00:00:00

With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations. Traditional neural networks, such as Convolutional Neural Networks (CNN) or Physics-Informed Neural Networks (PINN), are restricted to the prediction of solutions in a predefined configuration. With neural operators, one can learn the general solution of Partial Differential Equations, such as the elastic wave equation, with varying parameters. There have been very few applications of neural operators in seismology. All of them were limited to two-dimensional settings, although the importance of three-dimensional (3D) effects is well known. In this work, we apply the Fourier Neural Operator (FNO) to predict ground motion time series from a 3D geological description. We used a high-fidelity simulation code, SEM3D, to build an extensive database of ground motions generated by 30,000 different geologies. With this database, we show that the FNO can produce accurate ground motion even when the underlying geology exhibits large heterogeneities. Intensity measures at moderate and large periods are especially well reproduced. We present the first seismological application of Fourier Neural Operators in 3D. Thanks to the generalizability of our database, we believe that our model can be used to assess the influence of geological features such as sedimentary basins on ground motion, which is paramount to evaluating site effects.
The impact of the AI revolution on asset management
Authors: Michael Kopp
Date: 2023-04-20 00:00:00
Tasks: Asset Management, Deep Learning, Management

Recent progress in deep learning, a special form of machine learning, has led to remarkable capabilities machines can now be endowed with: they can read and understand free flowing text, reason and bargain with human counterparts, translate texts between languages, learn how to take decisions to maximize certain outcomes, etc. Today, machines have revolutionized the detection of cancer, the prediction of protein structures, the design of drugs, the control of nuclear fusion reactors etc. Although these capabilities are still in their infancy, it seems clear that their continued refinement and application will result in a technological impact on nearly all social and economic areas of human activity, the likes of which we have not seen before. In this article, I will share my view as to how AI will likely impact asset management in general and I will provide a mental framework that will equip readers with a simple criterion to assess whether and to what degree a given fund really exploits deep learning and whether a large disruption risk from deep learning exist.
Non-destructive Fault Diagnosis of Electronic Interconnects by Learning Signal Patterns of Reflection Coefficient in the Frequency Domain
Authors: Tae Yeob Kang, Haebom Lee, Sungho Suh
Date: 2023-04-20 00:00:00
Tasks: Diagnostic, Ensemble Learning, Fault Detection, Fault Diagnosis, Management, Time Series, Time Series Analysis

Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. Traditional methods, which rely on electronic signals as prognostic factors, often struggle to accurately identify the root causes of defects without resorting to destructive testing. Furthermore, these methods are vulnerable to noise interference, which can result in false alarms. To address these limitations, in this paper, we propose a novel, non-destructive approach for early fault detection and accurate diagnosis of interconnect defects, with improved noise resilience. Our approach uniquely utilizes the signal patterns of the reflection coefficient across a range of frequencies, enabling both root cause identification and severity assessment. This approach departs from conventional time-series analysis and effectively transforms the signal data into a format suitable for advanced learning algorithms. Additionally, we introduce a novel severity rating ensemble learning (SREL) approach, which enhances diagnostic accuracy and robustness in noisy environments. Experimental results demonstrate that the proposed method is effective for fault detection and diagnosis and has the potential to extend to real-world industrial applications.
Cyber Security in Smart Manufacturing (Threats, Landscapes Challenges)
Authors: Rahat Masum
Date: 2023-04-20 00:00:00
Tasks: Autonomous Vehicles

Industry 4.0 is a blend of the hyper-connected digital industry within two world of Information Technology (IT) and Operational Technology (OT). With this amalgamate opportunity, smart manufacturing involves production assets with the manufacturing equipment having its own intelligence, while the system-wide intelligence is provided by the cyber layer. However Smart manufacturing now becomes one of the prime targets of cyber threats due to vulnerabilities in the existing process of operation. Since smart manufacturing covers a vast area of production industries from cyber physical system to additive manufacturing, to autonomous vehicles, to cloud based IIoT (Industrial IoT), to robotic production, cyber threat stands out with this regard questioning about how to connect manufacturing resources by network, how to integrate a whole process chain for a factory production etc. Cybersecurity confidentiality, integrity and availability expose their essential existence for the proper operational thread model known as digital thread ensuring secure manufacturing. In this work, a literature survey is presented from the existing threat models, attack vectors and future challenges over the digital thread of smart manufacturing.
Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization
Authors: Philipp Müller
Date: 2023-04-20 00:00:00
Tasks: Indoor Localization

The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine. It determines the class membership of unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample. The choice of K has been the topic of various studies and proposed KNN-variants. Yet no variant has been proven to outperform all other variants. In this paper a KNN-variant is discussed which ensures that the K nearest neighbors are indeed close to the unlabelled sample and finds K along the way. The algorithm is tested and compared to the standard KNN in theoretical scenarios and for indoor localization based on ion-mobility spectrometry fingerprints. It achieves a higher classification accuracy than the KNN in the tests, while having the same computational demand.
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Date: 2023-04-20 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Out-of-Distribution Detection, Prediction

Large-scale pre-trained models have achieved remarkable success in many applications, but how to leverage them to improve the prediction reliability of downstream models is undesirably under-explored. Moreover, modern neural networks have been found to be poorly calibrated and make overconfident predictions regardless of inherent sample difficulty and data uncertainty. To address this issue, we propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. Pre-trained models that have been exposed to large-scale datasets and do not overfit the downstream training classes enable us to measure each training sample's difficulty via feature-space Gaussian modeling and relative Mahalanobis distance computation. Importantly, by adaptively penalizing overconfident prediction based on the sample difficulty, we simultaneously improve accuracy and uncertainty calibration across challenging benchmarks (e.g., +0.55% ACC and -3.7% ECE on ImageNet1k using ResNet34), consistently surpassing competitive baselines for reliable prediction. The improved uncertainty estimate further improves selective classification (abstaining from erroneous predictions) and out-of-distribution detection.
Automatic Procurement Fraud Detection with Machine Learning
Authors: Jin Bai, Tong Qiu
Date: 2023-04-20 00:00:00
Tasks: Fraud Detection

Although procurement fraud is always a critical problem in almost every free market, audit departments still have a strong reliance on reporting from informed sources when detecting them. With our generous cooperator, SF Express, sharing the access to the database related with procurements took place from 2015 to 2017 in their company, our team studies how machine learning techniques could help with the audition of one of the most profound crime among current chinese market, namely procurement frauds. By representing each procurement event as 9 specific features, we construct neural network models to identify suspicious procurements and classify their fraud types. Through testing our models over 50000 samples collected from the procurement database, we have proven that such models -- despite having space for improvements -- are useful in detecting procurement frauds.
Federated Compositional Deep AUC Maximization
Authors: Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
Date: 2023-04-20 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Federated Learning

Federated learning has attracted increasing attention due to the promise of balancing privacy and large-scale learning; numerous approaches have been proposed. However, most existing approaches focus on problems with balanced data, and prediction performance is far from satisfactory for many real-world applications where the number of samples in different classes is highly imbalanced. To address this challenging problem, we developed a novel federated learning method for imbalanced data by directly optimizing the area under curve (AUC) score. In particular, we formulate the AUC maximization problem as a federated compositional minimax optimization problem, develop a local stochastic compositional gradient descent ascent with momentum algorithm, and provide bounds on the computational and communication complexities of our algorithm. To the best of our knowledge, this is the first work to achieve such favorable theoretical results. Finally, extensive experimental results confirm the efficacy of our method.
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer
Authors: Shengxiang Hu, Guobing Zou, Song Yang, Shiyi Lin, Yanglan Gan, Bofeng Zhang
Date: 2023-04-20 00:00:00
Tasks: Dynamic Link Prediction, Graph Representation Learning, Link Prediction, Representation Learning

Dynamic graph representation learning has emerged as a crucial research area, driven by the growing need for analyzing time-evolving graph data in real-world applications. While recent approaches leveraging recurrent neural networks (RNNs) and graph neural networks (GNNs) have shown promise, they often fail to adequately capture the impact of temporal edge states on inter-node relationships, consequently overlooking the dynamic changes in node features induced by these evolving relationships. Furthermore, these methods suffer from GNNs' inherent over-smoothing problem, which hinders the extraction of global structural features. To address these challenges, we introduce the Recurrent Structure-reinforced Graph Transformer (RSGT), a novel framework for dynamic graph representation learning. It first designs a heuristic method to explicitly model edge temporal states by employing different edge types and weights based on the differences between consecutive snapshots, thereby integrating varying edge temporal states into the graph's topological structure. We then propose a structure-reinforced graph transformer that captures temporal node representations encoding both graph topology and evolving dynamics through a recurrent learning paradigm, enabling the extraction of both local and global structural features. Comprehensive experiments on four real-world datasets demonstrate RSGT's superior performance in discrete dynamic graph representation learning, consistently outperforming existing methods in dynamic link prediction tasks.
Improving Graph Neural Networks on Multi-node Tasks with Labeling Tricks
Authors: Xiyuan Wang, Pan Li, Muhan Zhang
Date: 2023-04-20 00:00:00
Tasks: Hyperedge Prediction, Link Prediction, Prediction, Representation Learning

In this paper, we provide a theory of using graph neural networks (GNNs) for \textit{multi-node representation learning}, where we are interested in learning a representation for a set of more than one node such as a link. Existing GNNs are mainly designed to learn single-node representations. When we want to learn a node-set representation involving multiple nodes, a common practice in previous works is to directly aggregate the single-node representations obtained by a GNN. In this paper, we show a fundamental limitation of such an approach, namely the inability to capture the dependence among multiple nodes in a node set, and argue that directly aggregating individual node representations fails to produce an effective joint representation for multiple nodes. A straightforward solution is to distinguish target nodes from others. Formalizing this idea, we propose \text{labeling trick}, which first labels nodes in the graph according to their relationships with the target node set before applying a GNN and then aggregates node representations obtained in the labeled graph for multi-node representations. The labeling trick also unifies a few previous successful works for multi-node representation learning, including SEAL, Distance Encoding, ID-GNN, and NBFNet. Besides node sets in graphs, we also extend labeling tricks to posets, subsets and hypergraphs. Experiments verify that the labeling trick technique can boost GNNs on various tasks, including undirected link prediction, directed link prediction, hyperedge prediction, and subgraph prediction. Our work explains the superior performance of previous node-labeling-based methods and establishes a theoretical foundation for using GNNs for multi-node representation learning.
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning
Authors: Joel Janek Dabrowski, Ashfaqur Rahman
Date: 2023-04-20 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Time Series

In this paper we present a novel application of detecting fruit picker activities based on time series data generated from wearable sensors. During harvesting, fruit pickers pick fruit into wearable bags and empty these bags into harvesting bins located in the orchard. Once full, these bins are quickly transported to a cooled pack house to improve the shelf life of picked fruits. For farmers and managers, the knowledge of when a picker bag is emptied is important for managing harvesting bins more effectively to minimise the time the picked fruit is left out in the heat (resulting in reduced shelf life). We propose a means to detect these bag-emptying events using human activity recognition with wearable sensors and machine learning methods. We develop a semi-supervised approach to labelling the data. A feature-based machine learning ensemble model and a deep recurrent convolutional neural network are developed and tested on a real-world dataset. When compared, the neural network achieves 86% detection accuracy.
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
Authors: Albert Musaelian, Anders Johansson, Simon Batzner, Boris Kozinsky
Date: 2023-04-20 00:00:00
Tasks: GPU

This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale. This is achieved through a combination of innovative model architecture, massive parallelization, and models and implementations optimized for efficient GPU utilization. The resulting Allegro architecture bridges the accuracy-speed tradeoff of atomistic simulations and enables description of dynamics in structures of unprecedented complexity at quantum fidelity. To illustrate the scalability of Allegro, we perform nanoseconds-long stable simulations of protein dynamics and scale up to a 44-million atom structure of a complete, all-atom, explicitly solvated HIV capsid on the Perlmutter supercomputer. We demonstrate excellent strong scaling up to 100 million atoms and 70% weak scaling to 5120 A100 GPUs.
Optimality of Robust Online Learning
Authors: Zheng-Chu Guo, Andreas Christmann, Lei Shi
Date: 2023-04-20 00:00:00
Tasks: regression

In this paper, we study an online learning algorithm with a robust loss function $\mathcal{L}_{\sigma}$ for regression over a reproducing kernel Hilbert space (RKHS). The loss function $\mathcal{L}_{\sigma}$ involving a scaling parameter $\sigma>0$ can cover a wide range of commonly used robust losses. The proposed algorithm is then a robust alternative for online least squares regression aiming to estimate the conditional mean function. For properly chosen $\sigma$ and step size, we show that the last iterate of this online algorithm can achieve optimal capacity independent convergence in the mean square distance. Moreover, if additional information on the underlying function space is known, we also establish optimal capacity dependent rates for strong convergence in RKHS. To the best of our knowledge, both of the two results are new to the existing literature of online learning.
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services
Authors: Hui Dou, Shanshan Zhu, Yiwen Zhang, Pengfei Chen, Zibin Zheng
Date: 2023-04-20 00:00:00
Tasks: Diversity

Hyper-parameters optimization (HPO) is vital for machine learning models. Besides model accuracy, other tuning intentions such as model training time and energy consumption are also worthy of attention from data analytic service providers. Hence, it is essential to take both model hyperparameters and system parameters into consideration to execute cross-layer multi-objective hyperparameter auto-tuning. Towards this challenging target, we propose HyperTuner in this paper. To address the formulated high-dimensional black-box multi-objective optimization problem, HyperTuner first conducts multi-objective parameter importance ranking with its MOPIR algorithm and then leverages the proposed ADUMBO algorithm to find the Pareto-optimal configuration set. During each iteration, ADUMBO selects the most promising configuration from the generated Pareto candidate set via maximizing a new well-designed metric, which can adaptively leverage the uncertainty as well as the predicted mean across all the surrogate models along with the iteration times. We evaluate HyperTuner on our local distributed TensorFlow cluster and experimental results show that it is always able to find a better Pareto configuration front superior in both convergence and diversity compared with the other four baseline algorithms. Besides, experiments with different training datasets, different optimization objectives and different machine learning platforms verify that HyperTuner can well adapt to various data analytic service scenarios.