pwc-archive/papers-with-abstracts huggingface dataset:
MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer.
Authors: Ghazi Gharsallah, Georges Kaddoum
Date: 2024-08-30 00:00:00
Proceeding: IEEE Open Journal of the Communications Society 2024 8
Tasks: Beam Prediction, Intelligent Communication, Multimodal Deep Learning, Sensor Fusion

Advancements in sixth-generation (6G) networks, coupled with the evolution of multimodal sensing in vehicle-to-everything (V2X) networks, have opened avenues for transformative research into multimodal-based artificial intelligence (AI) applications for wireless communication and network management. However, this promising research direction is often constrained by the limited availability of suitable datasets. In response, this paper introduces a comprehensive configurable co-simulation framework that integrates the state-of-the-art CARLA and Sionna simulators to generate a multimodal multi-view V2X (MVX) dataset. We present novel AI-based models to predict future line-of-sight (LoS) blockages and optimal beam direction as well as an innovative antenna position optimization (APO) solution, all of which are underpinned by the multimodal dataset MVX. Our framework capitalizes on collaborative perception and significantly enhances V2X communication by integrating LiDAR and wireless data. Thorough evaluations demonstrate that our collaborative perception approach outperforms traditional methods of both beam and blockage prediction in terms of accuracy and efficiency. Additionally, we evaluate the importance of infrastructural elements in V2X systems and conduct a computational study to illustrate that our framework is suitable for various operational scenarios and can be used as a digital twin solution. This work not only contributes to the field of V2X wireless communications by providing a versatile framework for network management but also sets the stage for future research on multi-sensor fusion in AI applications for V2X wireless communication environments to enhance the efficiency and resilience of future 6G networks
Flight Testing of Latch Valve with Lightweight LV-Servo Direct Drive Mechanism
Authors: Hao-Che Huang, Chih-Shin Chang, Jui-Cheng Hsu, Shih-Sin Wei
Date: 2024-09-13 00:00:00

In the field of rocket technology, the latch valve assumes a pivotal role in regulating the flow of fuel gases and liquids to ensure the requisite energy supply. This project endeavors to innovate by replacing the conventional step motor mechanism with a servo motor for latch valve control. The selected servo motor, boasting a more compact form factor and reduced mass, aligns seamlessly with the project's overarching objectives. While servo motors offer myriad advantages, it is imperative to acknowledge and address the constraints of their maximum output torque to guarantee the latch valve's reliable operation. Furthermore, as a rocket ascends, it encounters significant fluctuations in internal temperature and pressure. Consequently, rigorous environmental testing becomes paramount to validate the servo motor's performance under these dynamic conditions, thus ensuring the latch valve's unwavering functionality. The primary focus of this project is the design and testing of the mechanism's performance in simulated rocket environments, achieved through the implementation of the servo motor for latch valve control. The results reveal that the servo motor demonstrated its effectiveness and reliability in controlling the latch valve under the rigorous environmental conditions of rocket flight.
Global and Distributed Reproduction Numbers of a Multilayer SIR Model with an Infrastructure Network
Authors: José I. Caiza, Junjie Qin, Philip E. Paré
Date: 2024-09-12 00:00:00

In this paper, we propose an SIR spread model in a population network coupled with an infrastructure network that has a pathogen spreading in it. We develop a threshold condition to characterize the monotonicity and peak time of a weighted average of the infection states in terms of the global (network-wide) effective reproduction number. We further define the distributed reproduction numbers (DRNs) of each node in the multilayer network which are used to provide local threshold conditions for the dynamical behavior of each entity. Furthermore, we leverage the DRNs to predict the global behavior based on the node-level assumptions. We use both analytical and simulation results to illustrate that the DRNs allow a more accurate analysis of the networked spreading process than the global effective reproduction number.
DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks
Authors: Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar
Date: 2018-04-26 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2018 4
Tasks: Survival Analysis

Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, medicine and many other areas. A fundamental problem is to understand the relationship between the covariates and the (distribution of) survival times (times-to-event). Much of the previous work has approached the problem by viewing the survival time as the first hitting time of a stochastic process, assuming a specific form for the underlying stochastic process, using available data to learn the relationship between the covariates and the parameters of the model, and then deducing the relationship between covariates and the distribution of first hitting times (the risk). However, previous models rely on strong parametric assumptions that are often violated. This paper proposes a very different ap- proach to survival analysis, DeepHit, that uses a deep neural network to learn the distribution of survival times directly. DeepHit makes no assumptions about the underlying stochas- tic process and allows for the possibility that the relationship between covariates and risk(s) changes over time. Most impor- tantly, DeepHit smoothly handles competing risks; i.e. settings in which there is more than one possible event of interest. Comparisons with previous models on the basis of real and synthetic datasets demonstrate that DeepHit achieves large and statistically significant performance improvements over previous state-of-the-art methods.
mage based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization
Authors: Pedro Mateus, Leroy Volmer, Leonard Wee, Hugo J. W. L. Aerts, Frank Hoebers, Andre Dekker, Inigo Bermejo
Date: 2023-10-24 00:00:00
Proceeding: Scientific Reports 2023 10
Tasks: Medical Image Analysis, Model Selection, Prognosis

In the past decade, there has been a sharp increase in publications describing applications of convolutional neural networks (CNNs) in medical image analysis. However, recent reviews have warned of the lack of reproducibility of most such studies, which has impeded closer examination of the models and, in turn, their implementation in healthcare. On the other hand, the performance of these models is highly dependent on decisions on architecture and image pre-processing. In this work, we assess the reproducibility of three studies that use CNNs for head and neck cancer outcome prediction by attempting to reproduce the published results. In addition, we propose a new network structure and assess the impact of image pre-processing and model selection criteria on performance. We used two publicly available datasets: one with 298 patients for training and validation and another with 137 patients from a different institute for testing. All three studies failed to report elements required to reproduce their results thoroughly, mainly the image pre-processing steps and the random seed. Our model either outperforms or achieves similar performance to the existing models with considerably fewer parameters. We also observed that the pre-processing efforts significantly impact the model’s performance and that some model selection criteria may lead to suboptimal models. Although there have been improvements in the reproducibility of deep learning models, our work suggests that wider implementation of reporting standards is required to avoid a reproducibility crisis.
KKT-Informed Neural Network
Authors: Carmine Delle Femine
Date: 2024-09-11 00:00:00

A neural network-based approach for solving parametric convex optimization problems is presented, where the network estimates the optimal points given a batch of input parameters. The network is trained by penalizing violations of the Karush-Kuhn-Tucker (KKT) conditions, ensuring that its predictions adhere to these optimality criteria. Additionally, since the bounds of the parameter space are known, training batches can be randomly generated without requiring external data. This method trades guaranteed optimality for significant improvements in speed, enabling parallel solving of a class of optimization problems.
Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown
Authors: Zimeng Fang, Chao Liang, Xue Zhou, Shuyuan Zhu, Xi Li
Date: 2024-09-14 00:00:00
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking, Video Object Tracking

Multi-object tracking (MOT) emerges as a pivotal and highly promising branch in the field of computer vision. Classical closed-vocabulary MOT (CV-MOT) methods aim to track objects of predefined categories. Recently, some open-vocabulary MOT (OV-MOT) methods have successfully addressed the problem of tracking unknown categories. However, we found that the CV-MOT and OV-MOT methods each struggle to excel in the tasks of the other. In this paper, we present a unified framework, Associate Everything Detected (AED), that simultaneously tackles CV-MOT and OV-MOT by integrating with any off-the-shelf detector and supports unknown categories. Different from existing tracking-by-detection MOT methods, AED gets rid of prior knowledge (e.g. motion cues) and relies solely on highly robust feature learning to handle complex trajectories in OV-MOT tasks while keeping excellent performance in CV-MOT tasks. Specifically, we model the association task as a similarity decoding problem and propose a sim-decoder with an association-centric learning mechanism. The sim-decoder calculates similarities in three aspects: spatial, temporal, and cross-clip. Subsequently, association-centric learning leverages these threefold similarities to ensure that the extracted features are appropriate for continuous tracking and robust enough to generalize to unknown categories. Compared with existing powerful OV-MOT and CV-MOT methods, AED achieves superior performance on TAO, SportsMOT, and DanceTrack without any prior knowledge. Our code is available at https://github.com/balabooooo/AED.
Keypoints-Integrated Instruction-Following Data Generation for Enhanced Human Pose Understanding in Multimodal Models
Authors: Dewen Zhang, Wangpeng An, Hayaru Shouno
Date: 2024-09-14 00:00:00
Tasks: Instruction Following

Current multimodal models are well-suited for general visual understanding tasks. However, they perform inadequately when handling complex visual tasks related to human poses and actions, primarily due to the lack of specialized instruction-following data. We introduce a new method for generating such data by integrating human keypoints with traditional visual features like captions and bounding boxes. Our approach produces datasets designed for fine-tuning models to excel in human-centric activities, focusing on three specific types: conversation, detailed description, and complex reasoning. We fine-tuned the LLaVA-7B model with this novel dataset, achieving significant improvements across various human pose-related tasks. Experimental results show an overall improvement of 21.18% compared to the original LLaVA-7B model. These findings demonstrate the effectiveness of keypoints-assisted data in enhancing multimodal models.
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning
Authors: Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
Date: 2024-09-14 00:00:00
Tasks: Bilevel Optimization, Distributed Computing, Fairness, Federated Learning

In distributed computing environments, collaborative machine learning enables multiple clients to train a global model collaboratively. To preserve privacy in such settings, a common technique is to utilize frequent updates and transmissions of model parameters. However, this results in high communication costs between the clients and the server. To tackle unfairness concerns in distributed environments, client-specific information (e.g., local dataset size or data-related fairness metrics) must be sent to the server to compute algorithmic quantities (e.g., aggregation weights), which leads to a potential leakage of client information. To address these challenges, we propose a two-stage strategy that promotes fair predictions, prevents client-data leakage, and reduces communication costs in certain scenarios without the need to pass information between clients and server iteratively. In the first stage, for each client, we use its local dataset to obtain a synthetic dataset by solving a bilevel optimization problem that aims to ensure that the ultimate global model yields fair predictions. In the second stage, we apply a method with differential privacy guarantees to the synthetic dataset from the first stage to obtain a second synthetic data. We then pass each client's second-stage synthetic dataset to the server, the collection of which is used to train the server model using conventional machine learning techniques (that no longer need to take fairness metrics or privacy into account). Thus, we eliminate the need to handle fairness-specific aggregation weights while preserving client privacy. Our approach requires only a single communication between the clients and the server (thus making it communication cost-effective), maintains data privacy, and promotes fairness. We present empirical evidence to demonstrate the advantages of our approach.
Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning
Authors: He Wang, Yang Xu, Zebin Wu, Zhihui Wei
Date: 2024-09-15 00:00:00

Hyperspectral and multispectral image fusion aims to generate high spectral and spatial resolution hyperspectral images (HR-HSI) by fusing high-resolution multispectral images (HR-MSI) and low-resolution hyperspectral images (LR-HSI). However, existing fusion methods encounter challenges such as unknown degradation parameters, incomplete exploitation of the correlation between high-dimensional structures and deep image features. To overcome these issues, in this article, an unsupervised blind fusion method for hyperspectral and multispectral images based on Tucker decomposition and spatial spectral manifold learning (DTDNML) is proposed. We design a novel deep Tucker decomposition network that maps LR-HSI and HR-MSI into a consistent feature space, achieving reconstruction through decoders with shared parameter. To better exploit and fuse spatial-spectral features in the data, we design a core tensor fusion network that incorporates a spatial spectral attention mechanism for aligning and fusing features at different scales. Furthermore, to enhance the capacity in capturing global information, a Laplacian-based spatial-spectral manifold constraints is introduced in shared-decoders. Sufficient experiments have validated that this method enhances the accuracy and efficiency of hyperspectral and multispectral fusion on different remote sensing datasets. The source code is available at https://github.com/Shawn-H-Wang/DTDNML.
KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition
Authors: Zhaoyu Chen, Xing Li, Qian Huang, Qiang Geng, Tianjin Yang, Shihao Han
Date: 2024-09-14 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Kolmogorov-Arnold Networks, Temporal Action Localization

Point cloud sequence-based 3D action recognition has achieved impressive performance and efficiency. However, existing point cloud sequence modeling methods cannot adequately balance the precision of limb micro-movements with the integrity of posture macro-structure, leading to the loss of crucial information cues in action inference. To overcome this limitation, we introduce D-Hyperpoint, a novel data type generated through a D-Hyperpoint Embedding module. D-Hyperpoint encapsulates both regional-momentary motion and global-static posture, effectively summarizing the unit human action at each moment. In addition, we present a D-Hyperpoint KANsMixer module, which is recursively applied to nested groupings of D-Hyperpoints to learn the action discrimination information and creatively integrates Kolmogorov-Arnold Networks (KAN) to enhance spatio-temporal interaction within D-Hyperpoints. Finally, we propose KAN-HyperpointNet, a spatio-temporal decoupled network architecture for 3D action recognition. Extensive experiments on two public datasets: MSR Action3D and NTU-RGB+D 60, demonstrate the state-of-the-art performance of our method.
Structural counterfactual analysis in macroeconomics: theory and inference
Authors: EnDong Wang
Date: 2024-09-15 00:00:00
Tasks: counterfactual

We propose a structural model-free methodology to analyze two types of macroeconomic counterfactuals related to policy path deviation: hypothetical trajectory and policy intervention. Our model-free approach is built on a structural vector moving-average (SVMA) model that relies solely on the identification of policy shocks, thereby eliminating the need to specify an entire structural model. Analytical solutions are derived for the counterfactual parameters, and statistical inference for these parameter estimates is provided using the Delta method. By utilizing external instruments, we introduce a projection-based method for the identification, estimation, and inference of these parameters. This approach connects our counterfactual analysis with the Local Projection literature. A simulation-based approach with nonlinear model is provided to add in addressing Lucas' critique. The innovative model-free methodology is applied in three counterfactual studies on the U.S. monetary policy: (1) a historical scenario analysis for a hypothetical interest rate path in the post-pandemic era, (2) a future scenario analysis under either hawkish or dovish interest rate policy, and (3) an evaluation of the policy intervention effect of an oil price shock by zeroing out the systematic responses of the interest rate.
Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation
Authors: Hui Yi Leong, Yi Fan Gao, Ji Shuai, Yang Zhang, Uktu Pamuksuz
Date: 2024-09-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Scientific research indicates that for every hour spent in direct patient care, physicians spend nearly two additional hours on administrative tasks, particularly on electronic health records (EHRs) and desk work. This excessive administrative burden not only reduces the time available for patient care but also contributes to physician burnout and inefficiencies in healthcare delivery. To address these challenges, this study introduces MediGen, a fine-tuned large language model (LLM) designed to automate the generation of medical reports from medical dialogues. By leveraging state-of-the-art methodologies for fine-tuning open-source pretrained models, including LLaMA3-8B, MediGen achieves high accuracy in transcribing and summarizing clinical interactions. The fine-tuned LLaMA3-8B model demonstrated promising results, achieving a ROUGE score of 58% and a BERTScore-F1 of 72%, indicating its effectiveness in generating accurate and clinically relevant medical reports. These findings suggest that MediGen has the potential to significantly reduce the administrative workload on physicians, improving both healthcare efficiency and physician well-being.
Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation
Authors: Carlos Soto, Matthew Reimherr, Aleksandra Slavkovic, Mark Shriver
Date: 2024-09-10 00:00:00

In this paper we consider the problem of releasing a Gaussian Differentially Private (GDP) 3D human face. The human face is a complex structure with many features and inherently tied to one's identity. Protecting this data, in a formally private way, is important yet challenging given the dimensionality of the problem. We extend approximate DP techniques for functional data to the GDP framework. We further propose a novel representation, face radial curves, of a 3D face as a set of functions and then utilize our proposed GDP functional data mechanism. To preserve the shape of the face while injecting noise we rely on tools from shape analysis for our novel representation of the face. We show that our method preserves the shape of the average face and injects less noise than traditional methods for the same privacy budget. Our mechanism consists of two primary components, the first is generally applicable to function value summaries (as are commonly found in nonparametric statistics or functional data analysis) while the second is general to disk-like surfaces and hence more applicable than just to human faces.
Astrometric Binary Classification Via Artificial Neural Networks
Authors: Joe Smith
Date: 2024-09-15 00:00:00
Tasks: Binary Classification, Classification

With nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates have risen significantly. Due to the surplus of astrometric data, the current computational methods employed to inspect these astrometric binary candidates are both computationally expensive and cannot be executed in a reasonable time frame. In light of this, a machine learning (ML) technique to automatically classify whether a set of stars belong to an astrometric binary pair via an artificial neural network (ANN) is proposed. Using data from Gaia DR3, the ANN was trained and tested on 1.5 million highly probable true and visual binaries, considering the proper motions, parallaxes, and angular and physical separations as features. The ANN achieves high classification scores, with an accuracy of 99.3%, a precision rate of 0.988, a recall rate of 0.991, and an AUC of 0.999, indicating that the utilized ML technique is a highly effective method for classifying astrometric binaries. Thus, the proposed ANN is a promising alternative to the existing methods for the classification of astrometric binaries.
AceParse: A Comprehensive Dataset with Diverse Structured Texts for Academic Literature Parsing
Authors: Huawei Ji, Cheng Deng, Bo Xue, Zhouyang Jin, Jiaxin Ding, Xiaoying Gan, Luoyi Fu, Xinbing Wang, Chenghu Zhou
Date: 2024-09-16 00:00:00

With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality. Academic literature, as one of the crucial types, is predominantly stored in PDF formats and needs to be parsed into texts before further processing. However, parsing diverse structured texts in academic literature remains challenging due to the lack of datasets that cover various text structures. In this paper, we introduce AceParse, the first comprehensive dataset designed to support the parsing of a wide range of structured texts, including formulas, tables, lists, algorithms, and sentences with embedded mathematical expressions. Based on AceParse, we fine-tuned a multimodal model, named AceParser, which accurately parses various structured texts within academic literature. This model outperforms the previous state-of-the-art by 4.1% in terms of F1 score and by 5% in Jaccard Similarity, demonstrating the potential of multimodal models in academic literature parsing. Our dataset is available at https://github.com/JHW5981/AceParse.