pwc-archive/papers-with-abstracts huggingface dataset:
Multi model LSTM architecture for Track Association based on Automatic Identification System Data
Authors: Md Asif Bin Syed, Imtiaz Ahmed
Date: 2023-04-04 00:00:00

For decades, track association has been a challenging problem in marine surveillance, which involves the identification and association of vessel observations over time. However, the Automatic Identification System (AIS) has provided a new opportunity for researchers to tackle this problem by offering a large database of dynamic and geo-spatial information of marine vessels. With the availability of such large databases, researchers can now develop sophisticated models and algorithms that leverage the increased availability of data to address the track association challenge effectively. Furthermore, with the advent of deep learning, track association can now be approached as a data-intensive problem. In this study, we propose a Long Short-Term Memory (LSTM) based multi-model framework for track association. LSTM is a recurrent neural network architecture that is capable of processing multivariate temporal data collected over time in a sequential manner, enabling it to predict current vessel locations from historical observations. Based on these predictions, a geodesic distance based similarity metric is then utilized to associate the unclassified observations to their true tracks (vessels). We evaluate the performance of our approach using standard performance metrics, such as precision, recall, and F1 score, which provide a comprehensive summary of the accuracy of the proposed framework.
Blockwise Compression of Transformer-based Models without Retraining
Authors: Gaochen Dong, Wei Chen
Date: 2023-04-04 00:00:00
Tasks: Quantization

Transformer-based models, exemplified by GPT-3, ChatGPT, and GPT-4, have recently garnered considerable attention in both academia and industry due to their promising performance in general language tasks. Nevertheless, these models typically involve computationally encoding processes, and in some cases, decoding processes as well, both of which are fundamentally large-scale matrix multiplication. These operations bring the inevitable challenges of massive computation resources and huge memory footprint, usually requiring at least 10^23 FLOPs and hundreds of gigabytes, respectively. A common method to address this issue is to reduce the computational and memory requirements by applying layerwise quantization to the transformer, replacing the usual fp32 data type with a low-bit equivalent. Unfortunately, this method often leads to decreased model accuracy and necessitates time-consuming retraining. Such retraining not only requires fine-tuning skills but also substantial computational resources, posing challenges for users. To specifically tackle these issues, we propose BCT, a framework of blockwise compression for transformers without retraining, aiming to facilitate model deployment. Unlike layerwise compression methods, BCT achieves finer compression of the entire transformer by operating blockwise. This method mitigates data distribution deviation caused by quantization, eliminating the requirement for retraining. BCT effectively compresses all components of the model, including but not limited to the embedding, matrix multiplication, GELU, Softmax, layer normalization, and intermediate results. In a case study, an efficient model is compressed by BCT achieving up to 7.988x compression. Subsequently, we also evaluate it on several General Language Understanding Evaluation (GLUE) datasets.
Clustering Validation with The Area Under Precision-Recall Curves
Authors: Pablo Andretta Jaskowiak, Ivan Gesteira Costa
Date: 2023-04-04 00:00:00
Tasks: Clustering

Confusion matrices and derived metrics provide a comprehensive framework for the evaluation of model performance in machine learning. These are well-known and extensively employed in the supervised learning domain, particularly classification. Surprisingly, such a framework has not been fully explored in the context of clustering validation. Indeed, just recently such a gap has been bridged with the introduction of the Area Under the ROC Curve for Clustering (AUCC), an internal/relative Clustering Validation Index (CVI) that allows for clustering validation in real application scenarios. In this work we explore the Area Under Precision-Recall Curve (and related metrics) in the context of clustering validation. We show that these are not only appropriate as CVIs, but should also be preferred in the presence of cluster imbalance. We perform a comprehensive evaluation of proposed and state-of-art CVIs on real and simulated data sets. Our observations corroborate towards an unified validation framework for supervised and unsupervised learning, given that they are consistent with existing guidelines established for the evaluation of supervised learning models.
A Deep Multi-Modal Cyber-Attack Detection in Industrial Control Systems
Authors: Sepideh Bahadoripour, Ethan MacDonald, Hadis Karimipour
Date: 2023-04-04 00:00:00
Tasks: Cyber Attack Detection

The growing number of cyber-attacks against Industrial Control Systems (ICS) in recent years has elevated security concerns due to the potential catastrophic impact. Considering the complex nature of ICS, detecting a cyber-attack in them is extremely challenging and requires advanced methods that can harness multiple data modalities. This research utilizes network and sensor modality data from ICS processed with a deep multi-modal cyber-attack detection model for ICS. Results using the Secure Water Treatment (SWaT) system show that the proposed model can outperform existing single modality models and recent works in the literature by achieving 0.99 precision, 0.98 recall, and 0.98 f-measure, which shows the effectiveness of using both modalities in a combined model for detecting cyber-attacks.
Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field
Authors: Xianzhong Ding, Wan Du
Date: 2023-04-04 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers, but do not account for future soil moisture loss. Predicting soil moisture loss is challenging because it is influenced by numerous factors, including soil texture, weather conditions, and plant characteristics. This paper proposes a solution to improve irrigation efficiency, which is called DRLIC. DRLIC is a sophisticated irrigation system that uses deep reinforcement learning (DRL) to optimize its performance. The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss. We introduce an irrigation reward function that enables our control agent to learn from previous experiences. However, there may be instances where the output of our DRL control agent is unsafe, such as irrigating too much or too little water. To avoid damaging the health of the plants, we implement a safety mechanism that employs a soil moisture predictor to estimate the performance of each action. If the predicted outcome is deemed unsafe, we perform a relatively-conservative action instead. To demonstrate the real-world application of our approach, we developed an irrigation system that comprises sprinklers, sensing and control nodes, and a wireless network. We evaluate the performance of DRLIC by deploying it in a testbed consisting of six almond trees. During a 15-day in-field experiment, we compared the water consumption of DRLIC with a widely-used irrigation scheme. Our results indicate that DRLIC outperformed the traditional irrigation method by achieving a water savings of up to 9.52%.
Learning from data with structured missingness
Authors: Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti, Chris Holmes, Ryan Copping, Niels Hagenbuch, Stefanie Biedermann, Jack Noonan, Brieuc Lehmann, Aditi Shenvi, Xuan Vinh Doan, David Leslie, Ginestra Bianconi, Ruben Sanchez-Garcia, Alisha Davies, Maxine Mackintosh, Eleni-Rosalina Andrinopoulou, Anahid Basiri, Chris Harbron, Ben D. MacArthur
Date: 2023-04-04 00:00:00
Tasks: Missing Values

Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such `structured missingness' raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here, we outline the current literature and propose a set of grand challenges in learning from data with structured missingness.
Conformalized Unconditional Quantile Regression
Authors: Ahmed M. Alaa, Zeshan Hussain, David Sontag
Date: 2023-04-04 00:00:00
Tasks: Conformal Prediction, Econometrics, quantile regression, regression

We develop a predictive inference procedure that combines conformal prediction (CP) with unconditional quantile regression (QR) -- a commonly used tool in econometrics that involves regressing the recentered influence function (RIF) of the quantile functional over input covariates. Unlike the more widely-known conditional QR, unconditional QR explicitly captures the impact of changes in covariate distribution on the quantiles of the marginal distribution of outcomes. Leveraging this property, our procedure issues adaptive predictive intervals with localized frequentist coverage guarantees. It operates by fitting a machine learning model for the RIFs using training data, and then applying the CP procedure for any test covariate with respect to a ``hypothetical'' covariate distribution localized around the new instance. Experiments show that our procedure is adaptive to heteroscedasticity, provides transparent coverage guarantees that are relevant to the test instance at hand, and performs competitively with existing methods in terms of efficiency.
An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees
Authors: Ling Zhang, Daniel Tabas, Baosen Zhang
Date: 2023-04-03 00:00:00

In this paper, we consider the scenario-based two-stage stochastic DC optimal power flow (OPF) problem for optimal and reliable dispatch when the load is facing uncertainty. Although this problem is a linear program, it remains computationally challenging to solve due to the large number of scenarios needed to accurately represent the uncertainties. To mitigate the computational issues, many techniques have been proposed to approximate the second-stage decisions so they can be dealt more efficiently. The challenge of finding good policies to approximate the second-stage decisions is that these solutions need to be feasible, which has been difficult to achieve with existing policies. To address these challenges, this paper proposes a learning method to solve the two-stage problem in a more efficient and optimal way. A technique called the gauge map is incorporated into the learning architecture design to guarantee the learned solutions' feasibility to the network constraints. Namely, we can design policies that are feed forward functions and only output feasible solutions. Simulation results on standard IEEE systems show that, compared to iterative solvers and the widely used affine policy, our proposed method not only learns solutions of good quality but also accelerates the computation by orders of magnitude.
Learning with augmented target information: An alternative theory of Feedback Alignment
Authors: Huzi Cheng, Joshua W. Brown
Date: 2023-04-03 00:00:00

While error backpropagation (BP) has dominated the training of nearly all modern neural networks for a long time, it suffers from several biological plausibility issues such as the symmetric weight requirement and synchronous updates. Feedback Alignment (FA) was proposed as an alternative to BP to address those dilemmas and has been demonstrated to be effective on various tasks and network architectures. Despite its simplicity and effectiveness, a satisfying explanation of how FA works across different architectures is still lacking. Here we propose a novel, architecture-agnostic theory of how FA works through the lens of information theory: Instead of approximating gradients calculated by BP with the same parameter, FA learns effective representations by embedding target information into neural networks to be trained. We show this through the analysis of FA dynamics in idealized settings and then via a series of experiments. Based on the implications of this theory, we designed three variants of FA and show their comparable performance on several tasks. These variants also account for some phenomena and theories in neuroscience such as predictive coding and representational drift.
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation
Authors: Shota Hozumi, Kentaro Kutsukake, Kota Matsui, Syunya Kusakawa, Toru Ujihara, Ichiro Takeuchi
Date: 2023-04-03 00:00:00
Tasks: Active Learning, Transfer Learning

In material characterization, identifying defective areas on a material surface is fundamental. The conventional approach involves measuring the relevant physical properties point-by-point at the predetermined mesh grid points on the surface and determining the area at which the property does not reach the desired level. To identify defective areas more efficiently, we propose adaptive mapping methods in which measurement resources are used preferentially to detect the boundaries of defective areas. We interpret this problem as an active-learning (AL) of the level set estimation (LSE) problem. The goal of AL-based LSE is to determine the level set of the physical property function defined on the surface with as small number of measurements as possible. Furthermore, to handle the situations in which materials with similar specifications are repeatedly produced, we introduce a transfer learning approach so that the information of previously produced materials can be effectively utilized. As a proof-of-concept, we applied the proposed methods to the red-zone estimation problem of silicon wafers and demonstrated that we could identify the defective areas with significantly lower measurement costs than those of conventional methods.
Learning Personalized Models with Clustered System Identification
Authors: Leonardo F. Toso, Han Wang, James Anderson
Date: 2023-04-03 00:00:00

We address the problem of learning linear system models from observing multiple trajectories from different system dynamics. This framework encompasses a collaborative scenario where several systems seeking to estimate their dynamics are partitioned into clusters according to their system similarity. Thus, the systems within the same cluster can benefit from the observations made by the others. Considering this framework, we present an algorithm where each system alternately estimates its cluster identity and performs an estimation of its dynamics. This is then aggregated to update the model of each cluster. We show that under mild assumptions, our algorithm correctly estimates the cluster identities and achieves an approximate sample complexity that scales inversely with the number of systems in the cluster, thus facilitating a more efficient and personalized system identification process.
Counterfactual Learning on Graphs: A Survey
Authors: Zhimeng Guo, Teng Xiao, Zongyu Wu, Charu Aggarwal, Hui Liu, Suhang Wang
Date: 2023-04-03 00:00:00
Tasks: counterfactual, Fairness, Link Prediction, Representation Learning, Survey

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs. To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning. We divide existing methods into four categories based on problems studied. For each category, we provide background and motivating examples, a general framework summarizing existing works and a detailed review of these works. We point out promising future research directions at the intersection of graph-structured data, counterfactual learning, and real-world applications. To offer a comprehensive view of resources for future studies, we compile a collection of open-source implementations, public datasets, and commonly-used evaluation metrics. This survey aims to serve as a ``one-stop-shop'' for building a unified understanding of graph counterfactual learning categories and current resources. We also maintain a repository for papers and resources and will keep updating the repository https://github.com/TimeLovercc/Awesome-Graph-Causal-Learning.
Faulty Branch Identification in Passive Optical Networks using Machine Learning
Authors: Khouloud Abdelli, Carsten Tropschug, Helmut Griesser, Stephan Pachnicke
Date: 2023-04-03 00:00:00

Passive optical networks (PONs) have become a promising broadband access network solution. To ensure a reliable transmission, and to meet service level agreements, PON systems have to be monitored constantly in order to quickly identify and localize networks faults. Typically, a service disruption in a PON system is mainly due to fiber cuts and optical network unit (ONU) transmitter/receiver failures. When the ONUs are located at different distances from the optical line terminal (OLT), the faulty ONU or branch can be identified by analyzing the recorded optical time domain reflectometry (OTDR) traces. However, faulty branch isolation becomes very challenging when the reflections originating from two or more branches with similar length overlap, which makes it very hard to discriminate the faulty branches given the global backscattered signal. Recently, machine learning (ML) based approaches have shown great potential for managing optical faults in PON systems. Such techniques perform well when trained and tested with data derived from the same PON system. But their performance may severely degrade, if the PON system (adopted for the generation of the training data) has changed, e.g. by adding more branches or varying the length difference between two neighboring branches. etc. A re-training of the ML models has to be conducted for each network change, which can be time consuming. In this paper, to overcome the aforementioned issues, we propose a generic ML approach trained independently of the network architecture for identifying the faulty branch in PON systems given OTDR signals for the cases of branches with close lengths. Such an approach can be applied to an arbitrary PON system without requiring to be re-trained for each change of the network. The proposed approach is validated using experimental data derived from PON system.
Charting the Topography of the Neural Network Landscape with Thermal-Like Noise
Authors: Theo Jules, Gal Brener, Tal Kachman, Noam Levi, Yohai Bar-Sinai
Date: 2023-04-03 00:00:00

The training of neural networks is a complex, high-dimensional, non-convex and noisy optimization problem whose theoretical understanding is interesting both from an applicative perspective and for fundamental reasons. A core challenge is to understand the geometry and topography of the landscape that guides the optimization. In this work, we employ standard Statistical Mechanics methods, namely, phase-space exploration using Langevin dynamics, to study this landscape for an over-parameterized fully connected network performing a classification task on random data. Analyzing the fluctuation statistics, in analogy to thermal dynamics at a constant temperature, we infer a clear geometric description of the low-loss region. We find that it is a low-dimensional manifold whose dimension can be readily obtained from the fluctuations. Furthermore, this dimension is controlled by the number of data points that reside near the classification decision boundary. Importantly, we find that a quadratic approximation of the loss near the minimum is fundamentally inadequate due to the exponential nature of the decision boundary and the flatness of the low-loss region. This causes the dynamics to sample regions with higher curvature at higher temperatures, while producing quadratic-like statistics at any given temperature. We explain this behavior by a simplified loss model which is analytically tractable and reproduces the observed fluctuation statistics.
Classification of integers based on residue classes via modern deep learning algorithms
Authors: Da Wu, Jingye Yang, Mian Umair Ahsan, Kai Wang
Date: 2023-04-03 00:00:00
Tasks: AutoML, Feature Engineering

Judging whether an integer can be divided by prime numbers such as 2 or 3 may appear trivial to human beings, but can be less straightforward for computers. Here, we tested multiple deep learning architectures and feature engineering approaches on classifying integers based on their residues when divided by small prime numbers. We found that the ability of classification critically depends on the feature space. We also evaluated Automated Machine Learning (AutoML) platforms from Amazon, Google and Microsoft, and found that they failed on this task without appropriately engineered features. Furthermore, we introduced a method that utilizes linear regression on Fourier series basis vectors, and demonstrated its effectiveness. Finally, we evaluated Large Language Models (LLMs) such as GPT-4, GPT-J, LLaMA and Falcon, and demonstrated their failures. In conclusion, feature engineering remains an important task to improve performance and increase interpretability of machine-learning models, even in the era of AutoML and LLMs.
Learning the Delay Using Neural Delay Differential Equations
Authors: Maria Oprea, Mark Walth, Robert Stephany, Gabriella Torres Nothaft, Arnaldo Rodriguez-Gonzalez, William Clark
Date: 2023-04-03 00:00:00
Tasks: Sensitivity

The intersection of machine learning and dynamical systems has generated considerable interest recently. Neural Ordinary Differential Equations (NODEs) represent a rich overlap between these fields. In this paper, we develop a continuous time neural network approach based on Delay Differential Equations (DDEs). Our model uses the adjoint sensitivity method to learn the model parameters and delay directly from data. Our approach is inspired by that of NODEs and extends earlier neural DDE models, which have assumed that the value of the delay is known a priori. We perform a sensitivity analysis on our proposed approach and demonstrate its ability to learn DDE parameters from benchmark systems. We conclude our discussion with potential future directions and applications.