pwc-archive/papers-with-abstracts huggingface dataset:
Aircraft Landing Time Prediction with Deep Learning on Trajectory Images
Authors: Liping Huang, Sheng Zhang, YiCheng Zhang, Yi Zhang, Yifang Yin
Date: 2024-01-02 00:00:00
Tasks: Deep Learning

Aircraft landing time (ALT) prediction is crucial for air traffic management, especially for arrival aircraft sequencing on the runway. In this study, a trajectory image-based deep learning method is proposed to predict ALTs for the aircraft entering the research airspace that covers the Terminal Maneuvering Area (TMA). Specifically, the trajectories of all airborne arrival aircraft within the temporal capture window are used to generate an image with the target aircraft trajectory labeled as red and all background aircraft trajectory labeled as blue. The trajectory images contain various information, including the aircraft position, speed, heading, relative distances, and arrival traffic flows. It enables us to use state-of-the-art deep convolution neural networks for ALT modeling. We also use real-time runway usage obtained from the trajectory data and the external information such as aircraft types and weather conditions as additional inputs. Moreover, a convolution neural network (CNN) based module is designed for automatic holding-related featurizing, which takes the trajectory images, the leading aircraft holding status, and their time and speed gap at the research airspace boundary as its inputs. Its output is further fed into the final end-to-end ALT prediction. The proposed ALT prediction approach is applied to Singapore Changi Airport (ICAO Code: WSSS) using one-month Automatic Dependent Surveillance-Broadcast (ADS-B) data from November 1 to November 30, 2022. Experimental results show that by integrating the holding featurization, we can reduce the mean absolute error (MAE) from 82.23 seconds to 43.96 seconds, and achieve an average accuracy of 96.1\%, with 79.4\% of the predictions errors being less than 60 seconds.
Constrained Online Two-stage Stochastic Optimization: Algorithm with (and without) Predictions
Authors: Piao Hu, Jiashuo Jiang, Guodong Lyu, Hao Su
Date: 2024-01-02 00:00:00
Tasks: Stochastic Optimization

We consider an online two-stage stochastic optimization with long-term constraints over a finite horizon of $T$ periods. At each period, we take the first-stage action, observe a model parameter realization and then take the second-stage action from a feasible set that depends both on the first-stage decision and the model parameter. We aim to minimize the cumulative objective value while guaranteeing that the long-term average second-stage decision belongs to a set. We develop online algorithms for the online two-stage problem from adversarial learning algorithms. Also, the regret bound of our algorithm can be reduced to the regret bound of embedded adversarial learning algorithms. Based on this framework, we obtain new results under various settings. When the model parameters are drawn from unknown non-stationary distributions and we are given machine-learned predictions of the distributions, we develop a new algorithm from our framework with a regret $O(W_T+\sqrt{T})$, where $W_T$ measures the total inaccuracy of the machine-learned predictions. We then develop another algorithm that works when no machine-learned predictions are given and show the performances.
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning
Authors: Mehdi Hennequin, Khalid Benabdeslem, Haytham Elghazel
Date: 2024-01-02 00:00:00
Tasks: Domain Adaptation, Generalization Bounds, MULTI-VIEW LEARNING

This paper presents a series of new results for domain adaptation in the multi-view learning setting. The incorporation of multiple views in the domain adaptation was paid little attention in the previous studies. In this way, we propose an analysis of generalization bounds with Pac-Bayesian theory to consolidate the two paradigms, which are currently treated separately. Firstly, building on previous work by Germain et al., we adapt the distance between distribution proposed by Germain et al. for domain adaptation with the concept of multi-view learning. Thus, we introduce a novel distance that is tailored for the multi-view domain adaptation setting. Then, we give Pac-Bayesian bounds for estimating the introduced divergence. Finally, we compare the different new bounds with the previous studies.
Sharp Analysis of Power Iteration for Tensor PCA
Authors: Yuchen Wu, Kangjie Zhou
Date: 2024-01-02 00:00:00

We investigate the power iteration algorithm for the tensor PCA model introduced in Richard and Montanari (2014). Previous work studying the properties of tensor power iteration is either limited to a constant number of iterations, or requires a non-trivial data-independent initialization. In this paper, we move beyond these limitations and analyze the dynamics of randomly initialized tensor power iteration up to polynomially many steps. Our contributions are threefold: First, we establish sharp bounds on the number of iterations required for power method to converge to the planted signal, for a broad range of the signal-to-noise ratios. Second, our analysis reveals that the actual algorithmic threshold for power iteration is smaller than the one conjectured in literature by a polylog(n) factor, where n is the ambient dimension. Finally, we propose a simple and effective stopping criterion for power iteration, which provably outputs a solution that is highly correlated with the true signal. Extensive numerical experiments verify our theoretical results.
CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation
Authors: Nelly Elsayed, Zag ElSayed, Murat Ozer
Date: 2024-01-02 00:00:00
Tasks: Chatbot

Suicide is recognized as one of the most serious concerns in the modern society. Suicide causes tragedy that affects countries, communities, and families. There are many factors that lead to suicidal ideations. Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations. As technology usage has increased, people share and express their ideations digitally via social media, chatbots, and other digital platforms. In this paper, we proposed a novel, simple deep learning-based model to detect suicidal ideations in digital content, mainly focusing on chatbots as the primary data source. In addition, we provide a framework that employs the proposed suicide detection integration with a chatbot-based support system.
A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection
Authors: Thanh-Dung Le, Clara Macabiau, Kévin Albert, Philippe Jouvet, Rita Noumeir
Date: 2024-01-02 00:00:00
Tasks: Artifact Detection, Contrastive Learning, Self-Supervised Learning

Recent research at CHU Sainte Justine's Pediatric Critical Care Unit (PICU) has revealed that traditional machine learning methods, such as semi-supervised label propagation and K-nearest neighbors, outperform Transformer-based models in artifact detection from PPG signals, mainly when data is limited. This study addresses the underutilization of abundant unlabeled data by employing self-supervised learning (SSL) to extract latent features from these data, followed by fine-tuning on labeled data. Our experiments demonstrate that SSL significantly enhances the Transformer model's ability to learn representations, improving its robustness in artifact classification tasks. Among various SSL techniques, including masking, contrastive learning, and DINO (self-distillation with no labels)-contrastive learning exhibited the most stable and superior performance in small PPG datasets. Further, we delve into optimizing contrastive loss functions, which are crucial for contrastive SSL. Inspired by InfoNCE, we introduce a novel contrastive loss function that facilitates smoother training and better convergence, thereby enhancing performance in artifact classification. In summary, this study establishes the efficacy of SSL in leveraging unlabeled data, particularly in enhancing the capabilities of the Transformer model. This approach holds promise for broader applications in PICU environments, where annotated data is often limited.
Machine Learning Classification of Alzheimer's Disease Stages Using Cerebrospinal Fluid Biomarkers Alone
Authors: Vivek Kumar Tiwari, Premananda Indic, Shawana Tabassum
Date: 2024-01-02 00:00:00
Tasks: Binary Classification, regression

Early diagnosis of Alzheimer's disease is a challenge because the existing methodologies do not identify the patients in their preclinical stage, which can last up to a decade prior to the onset of clinical symptoms. Several research studies demonstrate the potential of cerebrospinal fluid biomarkers, amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease stages. In this work, we used machine learning models to classify different stages of Alzheimer's disease based on the cerebrospinal fluid biomarker levels alone. An electronic health record of patients from the National Alzheimer's Coordinating Centre database was analyzed and the patients were subdivided based on mini-mental state scores and clinical dementia ratings. Statistical and correlation analyses were performed to identify significant differences between the Alzheimer's stages. Afterward, machine learning classifiers including K-Nearest Neighbors, Ensemble Boosted Tree, Ensemble Bagged Tree, Support Vector Machine, Logistic Regression, and Naive Bayes classifiers were employed to classify the Alzheimer's disease stages. The results demonstrate that Ensemble Boosted Tree (84.4%) and Logistic Regression (73.4%) provide the highest accuracy for binary classification, while Ensemble Bagged Tree (75.4%) demonstrates better accuracy for multiclassification. The findings from this research are expected to help clinicians in making an informed decision regarding the early diagnosis of Alzheimer's from the cerebrospinal fluid biomarkers alone, monitoring of the disease progression, and implementation of appropriate intervention measures.
Facebook Report on Privacy of fNIRS data
Authors: Md Imran Hossen, Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Xiali Hei
Date: 2024-01-01 00:00:00
Tasks: Federated Learning, Privacy Preserving

The primary goal of this project is to develop privacy-preserving machine learning model training techniques for fNIRS data. This project will build a local model in a centralized setting with both differential privacy (DP) and certified robustness. It will also explore collaborative federated learning to train a shared model between multiple clients without sharing local fNIRS datasets. To prevent unintentional private information leakage of such clients' private datasets, we will also implement DP in the federated learning setting.
Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients
Authors: Alireza Rafiei, Ronald Moore, Tilendra Choudhary, Curtis Marshall, Geoffrey Smith, John D. Roback, Ravi M. Patel, Cassandra D. Josephson, Rishikesan Kamaleswaran
Date: 2024-01-01 00:00:00

Objective: Blood transfusions, crucial in managing anemia and coagulopathy in ICU settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily targeted a particular patient demographic with unique medical conditions and focused on a single type of blood transfusion. This study aims to develop an advanced machine learning-based model to predict the probability of transfusion necessity over the next 24 hours for a diverse range of non-traumatic ICU patients. Methods: We conducted a retrospective cohort study on 72,072 adult non-traumatic ICU patients admitted to a high-volume US metropolitan academic hospital between 2016 and 2020. We developed a meta-learner and various machine learning models to serve as predictors, training them annually with four-year data and evaluating on the fifth, unseen year, iteratively over five years. Results: The experimental results revealed that the meta-model surpasses the other models in different development scenarios. It achieved notable performance metrics, including an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.97, an accuracy rate of 0.93, and an F1-score of 0.89 in the best scenario. Conclusion: This study pioneers the use of machine learning models for predicting blood transfusion needs in a diverse cohort of critically ill patients. The findings of this evaluation confirm that our model not only predicts transfusion requirements effectively but also identifies key biomarkers for making transfusion decisions.
Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective
Authors: Din-Yin Hsieh, Chi-Hua Wang, Guang Cheng
Date: 2024-01-01 00:00:00
Tasks: Fraud Detection, Time Series

Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges. This paper addresses these challenges, focusing on attaining both high fidelity to actual data and optimal utility for machine learning tasks. We introduce five pre-processing schemas to enhance the training of the Conditional Probabilistic Auto-Regressive Model (CPAR), demonstrating incremental improvements in the synthetic data's fidelity and utility. Upon achieving satisfactory fidelity levels, our attention shifts to training fraud detection models tailored for time-series data, evaluating the utility of the synthetic data. Our findings offer valuable insights and practical guidelines for synthetic data practitioners in the finance sector, transitioning from real to synthetic datasets for training purposes, and illuminating broader methodologies for synthesizing credit card transaction time series.
Families of costs with zero and nonnegative MTW tensor in optimal transport
Authors: Du Nguyen
Date: 2024-01-01 00:00:00
Tasks: Form

We compute explicitly the MTW tensor (or cross curvature) for the optimal transport problem on $\mathbb{R}^n$ with a cost function of form $\mathsf{c}(x, y) = \mathsf{u}(x^{\mathfrak{t}}y)$, where $\mathsf{u}$ is a scalar function with inverse $\mathsf{s}$, $x^{\ft}y$ is a nondegenerate bilinear pairing of vectors $x, y$ belonging to an open subset of $\mathbb{R}^n$. The condition that the MTW-tensor vanishes on null vectors under the Kim-McCann metric is a fourth-order nonlinear ODE, which could be reduced to a linear ODE of the form $\mathsf{s}^{(2)} - S\mathsf{s}^{(1)} + P\mathsf{s} = 0$ with constant coefficients $P$ and $S$. The resulting inverse functions include {\it Lambert} and {\it generalized inverse hyperbolic\slash trigonometric} functions. The square Euclidean metric and $\log$-type costs are equivalent to instances of these solutions. The optimal map for the family is also explicit. For cost functions of a similar form on a hyperboloid model of the hyperbolic space and unit sphere, we also express this tensor in terms of algebraic expressions in derivatives of $\mathsf{s}$ using the Gauss-Codazzi equation, obtaining new families of strictly regular costs for these manifolds, including new families of {\it power function costs}. We analyze the $\sinh$-type hyperbolic cost, providing examples of $\mathsf{c}$-convex functions and divergence.
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
Authors: Bálint Máté, François Fleuret
Date: 2024-01-01 00:00:00
Tasks: Operator learning

We consider the problem of sampling lattice field configurations on a lattice from the Boltzmann distribution corresponding to some action. Since such densities arise as approximationw of an underlying functional density, we frame the task as an instance of operator learning. We propose to approximate a time-dependent neural operator whose time integral provides a mapping between the functional distributions of the free and target theories. Once a particular lattice is chosen, the neural operator can be discretized to a finite-dimensional, time-dependent vector field which in turn induces a continuous normalizing flow between finite dimensional distributions over the chosen lattice. This flow can then be trained to be a diffeormorphism between the discretized free and target theories on the chosen lattice, and, by construction, can be evaluated on different discretizations of spacetime. We experimentally validate the proposal on the 2-dimensional $\phi^4$-theory to explore to what extent such operator-based flow architectures generalize to lattice sizes they were not trained on, and show that pretraining on smaller lattices can lead to a speedup over training directly on the target lattice size.
Graph-Convolutional Autoencoder Ensembles for the Humanities, Illustrated with a Study of the American Slave Trade
Authors: Tom Lippincott
Date: 2024-01-01 00:00:00

We introduce a graph-aware autoencoder ensemble framework, with associated formalisms and tooling, designed to facilitate deep learning for scholarship in the humanities. By composing sub-architectures to produce a model isomorphic to a humanistic domain we maintain interpretability while providing function signatures for each sub-architectural choice, allowing both traditional and computational researchers to collaborate without disrupting established practices. We illustrate a practical application of our approach to a historical study of the American post-Atlantic slave trade, and make several specific technical contributions: a novel hybrid graph-convolutional autoencoder mechanism, batching policies for common graph topologies, and masking techniques for particular use-cases. The effectiveness of the framework for broadening participation of diverse domains is demonstrated by a growing suite of two dozen studies, both collaborations with humanists and established tasks from machine learning literature, spanning a variety of fields and data modalities. We make performance comparisons of several different architectural choices and conclude with an ambitious list of imminent next steps for this research.
A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL
Authors: Venkataraman Natarajan Iyer
Date: 2024-01-01 00:00:00
Tasks: Federated Learning

Federated Learning (FL) is a machine-learning approach enabling collaborative model training across multiple decentralized edge devices that hold local data samples, all without exchanging these samples. This collaborative process occurs under the supervision of a central server orchestrating the training or via a peer-to-peer network. The significance of FL is particularly pronounced in industries such as healthcare and finance, where data privacy holds paramount importance. However, training a model under the Federated learning setting brings forth several challenges, with one of the most prominent being the heterogeneity of data distribution among the edge devices. The data is typically non-independently and non-identically distributed (non-IID), thereby presenting challenges to model convergence. This report delves into the issues arising from non-IID and heterogeneous data and explores current algorithms designed to address these challenges.
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC
Authors: Jinglong Luo, Yehong Zhang, Zhuo Zhang, JiaQi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu
Date: 2024-01-01 00:00:00
Tasks: Knowledge Distillation, Privacy Preserving

With the growing use of Transformer models hosted on cloud platforms to offer inference services, privacy concerns are escalating, especially concerning sensitive data like investment plans and bank account details. Secure Multi-Party Computing (SMPC) emerges as a promising solution to protect the privacy of inference data and model parameters. However, the application of SMPC in Privacy-Preserving Inference (PPI) for Transformer models often leads to considerable slowdowns or declines in performance. This is largely due to the multitude of nonlinear operations in the Transformer architecture, which are not well-suited to SMPC and difficult to circumvent or optimize effectively. To address this concern, we introduce a comprehensive PPI framework called SecFormer to achieve fast and accurate PPI for Transformer models. We successfully eliminate the high-cost exponential and maximum operations in PPI without sacrificing model performance and develop a suite of efficient SMPC protocols by employing suitable numerical computation methods to boost other complex nonlinear functions in PPI, including GeLU, LayerNorm, and a redesigned Softmax. Our extensive experiments reveal that SecFormer outperforms MPCFormer in performance, showing improvements of $3.4\%$ and $24.7\%$ for BERT$_{\text{BASE}}$ and BERT$_{\text{LARGE}}$, respectively. In terms of efficiency, SecFormer is 3.57 and 3.58 times faster than PUMA for BERT$_{\text{BASE}}$ and BERT$_{\text{LARGE}}$, demonstrating its effectiveness and speed.
Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach
Authors: Shuang Li, Ziyuan Pu, Zhiyong Cui, Seunghyeon Lee, Xiucheng Guo, Dong Ngoduy
Date: 2024-01-01 00:00:00
Tasks: Causal Inference, counterfactual, Selection bias

Highway traffic crashes exert a considerable impact on both transportation systems and the economy. In this context, accurate and dependable emergency responses are crucial for effective traffic management. However, the influence of crashes on traffic status varies across diverse factors and may be biased due to selection bias. Therefore, there arises a necessity to accurately estimate the heterogeneous causal effects of crashes, thereby providing essential insights to facilitate individual-level emergency decision-making. This paper proposes a novel causal machine learning framework to estimate the causal effect of different types of crashes on highway speed. The Neyman-Rubin Causal Model (RCM) is employed to formulate this problem from a causal perspective. The Conditional Shapley Value Index (CSVI) is proposed based on causal graph theory to filter adverse variables, and the Structural Causal Model (SCM) is then adopted to define the statistical estimand for causal effects. The treatment effects are estimated by Doubly Robust Learning (DRL) methods, which combine doubly robust causal inference with classification and regression machine learning models. Experimental results from 4815 crashes on Highway Interstate 5 in Washington State reveal the heterogeneous treatment effects of crashes at varying distances and durations. The rear-end crashes cause more severe congestion and longer durations than other types of crashes, and the sideswipe crashes have the longest delayed impact. Additionally, the findings show that rear-end crashes affect traffic greater at night, while crash to objects has the most significant influence during peak hours. Statistical hypothesis tests, error metrics based on matched "counterfactual outcomes", and sensitive analyses are employed for assessment, and the results validate the accuracy and effectiveness of our method.