pwc-archive/papers-with-abstracts huggingface dataset:
Residual Aligner-based Network (RAN): Motion-separable structure for coarse-to-fine discontinuous deformable registration
Authors: Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartłomiej W. Papież
Date: 2023-11-21 00:00:00
Proceeding: Medical Image Analysis 2023 11
Tasks: Computed Tomography (CT), Deformable Medical Image Registration, Image Registration, Medical Image Registration, Unsupervised Image Registration

Deformable image registration, the estimation of the spatial transformation between different images, is an important task in medical imaging. Deep learning techniques have been shown to perform 3D image registration efficiently. However, current registration strategies often only focus on the deformation smoothness, which leads to the ignorance of complicated motion patterns (e.g., separate or sliding motions), especially for the intersection of organs. Thus, the performance when dealing with the discontinuous motions of multiple nearby objects is limited, causing undesired predictive outcomes in clinical usage, such as misidentification and mislocalization of lesions or other abnormalities. Consequently, we proposed a novel registration method to address this issue: a new Motion Separable backbone is exploited to capture the separate motion, with a theoretical analysis of the upper bound of the motions’ discontinuity provided. In addition, a novel Residual Aligner module was used to disentangle and refine the predicted motions across the multiple neighboring objects/organs. We evaluate our method, Residual Aligner-based Network (RAN), on abdominal Computed Tomography (CT) scans and it has shown to achieve one of the most accurate unsupervised inter-subject registration for the 9 organs, with the highest-ranked registration of the veins (Dice Similarity Coefficient (%)/Average surface distance (mm): 62%/4.9mm for the vena cava and 34%/7.9mm for the portal and splenic vein), with a smaller model structure and less computation compared to state-of-the-art methods. Furthermore, when applied to lung CT, the RAN achieves comparable results to the best-ranked networks (94%/3.0mm), also with fewer parameters and less computation.
Learning Geometric Transformation for Point Cloud Completion
Authors: Shengping Zhang, Xianzhu Liu, Haozhe Xie, Liqiang Nie, Huiyu Zhou, DaCheng Tao, Xuelong Li
Date: 2023-06-08 00:00:00
Proceeding: International Journal of Computer Vision 2023 6
Tasks: Decoder, global-optimization, Point Cloud Completion

Point cloud completion aims to estimate the missing shape from a partial point cloud. Existing encoder-decoder based generative models usually reconstruct the complete point cloud from the learned distribution of the shape prior, which may lead to distortion of geometric details (such as sharp structures and structures without smooth surfaces) due to the information loss of the latent space embedding. To address this problem, we formulate point cloud completion as a geometric transformation problem and propose a simple yet effective geometric transformation network (GTNet). It exploits the repetitive geometric structures in common 3D objects to recover the complete shapes, which contains three sub-networks: geometric patch network, structure transformation network, and detail refinement network. Specifically, the geometric patch network iteratively discovers repetitive geometric structures that are related or similar to the missing parts. Then, the structure transformation network uses the discovered geometric structures to complete the corresponding missing parts by learning their spatial transformations such as symmetry, rotation, translation, and uniform scaling. Finally, the detail refinement network performs global optimization to eliminate unnatural structures. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods on the Shape-Net55-34, MVP, PCN, and KITTI datasets. Models and code will be available at https://github.com/ivislabhit/GTNet.
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
Authors: Jialong Wu, Haoyu Ma, Chaoyi Deng, Mingsheng Long
Date: 2023-05-29 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Autonomous Driving, Decoder, Model-based Reinforcement Learning, Transfer Learning, Unsupervised Pre-training, World Knowledge

Unsupervised pre-training methods utilizing large and diverse datasets have achieved tremendous success across a range of domains. Recent work has investigated such unsupervised pre-training methods for model-based reinforcement learning (MBRL) but is limited to domain-specific or simulated data. In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of downstream visual control tasks. However, in-the-wild videos are complicated with various contextual factors, such as intricate backgrounds and textured appearance, which precludes a world model from extracting shared world knowledge to generalize better. To tackle this issue, we introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling to overcome the complexity and diversity of in-the-wild videos and facilitate knowledge transfer between distinct scenes. Specifically, a contextualized extension of the latent dynamics model is elaborately realized by incorporating a context encoder to retain contextual information and empower the image decoder, which encourages the latent dynamics model to concentrate on essential temporal variations. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of MBRL in various domains, including robotic manipulation, locomotion, and autonomous driving. Code is available at this repository: https://github.com/thuml/ContextWM.
Cooperative Path Planning of UAVs & UGVs for a Persistent Surveillance Task in Urban Environments
Authors: Yu Wu, Shaobo Wu, and Xinting Hu
Date: 2021-05-15 00:00:00
Proceeding: IEEE Internet of Things Journal 2021 5

There have been many applications of drones in
urban environments, such as delivery, rescue, and surveillance.
In a persistent surveillance task, the drones sometimes cannot
complete it independently when some regions are required to be
covered on the ground. For this purpose, unmanned aerial vehicles and unmanned ground vehicles (UAVs & UGVs) system is
introduced to perform such a task in this article, and the goal
is to generate the circular paths for the drones and the UGVs,
respectively, to minimize their travel time of realizing a complete coverage. First, the cooperative path planning problem of
UAVs & UGVs is formulated into a large-scale 0-1 optimization
problem, in which the on–off states of the discrete points are to
be optimized. Second, a hybrid algorithm integrating the estimation of distribution algorithm (EDA) and the genetic algorithm
(GA) algorithm is proposed to solve the problem. The advantages
of EDA and GA in the global and local search are fully taken
considering the demands in different phases of the iterative process. A simple sweep-based approach is employed to determine
the optimal sequence of passing the open points. Then, an online
local adjustment strategy is also applied to address the changes of
the requirements on covering the ground area. Simulation results
demonstrate that the UAVs & UGVs system can enhance the effi-
ciency of the task. The hybrid EDA-GA algorithm can greatly
improve the performance of EDA and GA in terms of the quality
and the stability of solutions. The online adjustment strategy is
effective to maintain a complete coverage while minimizing the
impact on the circular paths.
Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant
Authors: Hantao Lou, Jian-Qing Zheng, Xiaohang Leo Fang, Zhu Liang, Meihan Zhang, Yu Chen, Chunmei Wang, Xuetao Cao
Date: 2022-09-27 00:00:00
Proceeding: Cell Research 2022 9
Tasks: Antibody-antigen binding prediction, Deep Learning, Protein Function Prediction

The COVID-19 pandemic has been ongoing for nearly two and half years, and new variants of concern (VOCs) of SARS-CoV-2 continue to emerge, which urges the development of broadly neutralizing antibodies. Variants such as the delta (B.1.617.2 lineage) and Omicron (BA.1 and BA.2) were reported to exhibit immune evasion to some of the current therapeutic antibodies. The ever-evolving SARS-CoV-2 calls for rapid prediction of antibody binding to new variants and development of broadly neutralizing antibodies. Considering the application of deep learning in antibody engineering and optimization, we wonder whether the broadly reactive antibodies against SARS-CoV-2 variants can be rapidly designed and generated by deep learning. Here we report the development of an Atrous Convolution Neural Network (ACNN) based deep learning framework: cross-reactive B cell receptor network (XBCR-net) that can predict broadly reactive antibodies against SARS-CoV-2 and VOCs directly from single-cell BCR sequences. XBCR-net composes of two parts, the first part extracts the features relevant to the antibody–antigen interaction via three-branch ACNN, and the second part predicts the binding probability of the antibodies to antigens (14 different RBD sequences) by a residual structural Multi-Layer Perceptron. The performance of the ACNN-based XBCR-net prediction on SARS-CoV-2 binding was evaluated, showing significantly higher accuracy, precision and recall value than other frameworks.
A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2
Authors: Yiquan Wang, Meng Yuan, Huibin Lv, Jian Peng, Ian A. Wilson, Nicholas C. Wu
Date: 2022-05-25 00:00:00
Proceeding: Immunity 2022 5
Tasks: Antibody-antigen binding prediction

Global research to combat the COVID-19 pandemic has led to the isolation and characterization of thousands of human antibodies to the SARS-CoV-2 spike protein, providing an unprecedented opportunity to study the antibody response to a single antigen. Using the information derived from 88 research publications and 13 patents, we assembled a dataset of ~8,000 human antibodies to the SARS-CoV-2 spike protein from >200 donors. By analyzing immunoglobulin V and D gene usages, complementarity-determining region H3 sequences, and somatic hypermutations, we demonstrated that the common (public) responses to different domains of the spike protein were quite different. We further used these sequences to train a deep-learning model to accurately distinguish between the human antibodies to SARS-CoV-2 spike protein and those to influenza hemagglutinin protein. Overall, this study provides an informative resource for antibody research and enhances our molecular understanding of public antibody responses.
Evaluation framework for algorithms segmenting short axis cardiac MRI.
Authors: Radau Perry, Lu Yingli, Connelly Kim, Paul Gideon, Dick Alexander J, Wright Graham A
Date: 2009-07-09 00:00:00
Proceeding: The MIDAS Journal 2009 7
Tasks: Cardiac Segmentation, LV Segmentation, Segmentation

The motivation of the segmentation challenge is to quantitatively analyze global and regional cardiac function from cine magnetic resonance (MR) images, clinical parameters such as ejection fraction (EF), left ventricle myocardium mass (MM), and stroke volume (SV) are required. Calculations of these parameters depend upon accurate delineation of endocardial and epicardial contours of the left ventricle (LV). Manual delineation is time-consuming and tedious and has high inter-observer variability. Thus, fully automatic LV segmentation is desirable. The automatic segmentation of the LV in cine MR typically faces four challenges: 1) the overlap between the intensity distributions within the cardiac regions; 2) the lack of edge information; 3) the shape variability of the endocardial and epicardial contours across slices and phases; and 4) the inter-subject variability of these factors. A number of methods have been proposed for (semi-) automatic LV segmentation, including using a probability atlas [1], dynamic programming [2-3], fuzzy clustering [4], a deformable model [5], an active appearance model [6], a variational and level set [7-10], graph cuts [11-12] and an image-driven approach [13]. For a complete review of recent literature describing cardiac segmentation techniques, see [14]. Although the segmentation results have improved, accurate LV segmentation is still acknowledged as a difficult problem. The goals of this contest are to compare LV segmentation methods by providing an evaluation system, and a database of images and expert contours. Comparing segmentation results across research studies can be difficult due to unspecified differences in the method or implementation of evaluation metrics. This contest will provide open-source code for contour evaluation. Furthermore, the database will provide a set of images such that confounding segmentation differences due to image quality or pathology could be eliminated.
Domain Adaptive Graph Infomax via Conditional Adversarial Networks
Authors: Jiaren Xiao, Quanyu Dai, Xiaochen Xie, Qi Dou, Ka-Wai Kwok, James Lam
Date: 2023-01-01 00:00:00
Proceeding: IEEE Transactions on Network Science and Engineering 2023 1
Tasks: Node Classification

The emerging graph neural networks (GNNs) have demonstrated impressive performance on the node classification problem in complex networks. However, existing GNNs are mainly devised to classify nodes in a (partially) labeled graph. To classify nodes in a newly-collected unlabeled graph, it is desirable to transfer label information from an existing labeled graph. To address this cross-graph node classification problem, we propose a graph infomax method that is domain adaptive. Node representations are computed through neighborhood aggregation. Mutual information is maximized between node representations and global summaries, encouraging node representations to encode the global structural information. Conditional adversarial networks are employed to reduce the domain discrepancy by aligning the multimodal distributions of node representations. Experimental results in real-world datasets validate the performance of our method in comparison with the state-of-the-art baselines.
Probabilistic Generating Circuits -- Demystified
Authors: Sanyam Agarwal, Markus Bläser
Date: 2024-03-04 00:00:00
Tasks: Point Processes

Zhang et al. (ICML 2021, PLMR 139, pp. 12447-1245) introduced probabilistic generating circuits (PGCs) as a probabilistic model to unify probabilistic circuits (PCs) and determinantal point processes (DPPs). At a first glance, PGCs store a distribution in a very different way, they compute the probability generating polynomial instead of the probability mass function and it seems that this is the main reason why PGCs are more powerful than PCs or DPPs. However, PGCs also allow for negative weights, whereas classical PCs assume that all weights are nonnegative. One of the main insights of our paper is that the negative weights are responsible for the power of PGCs and not the different representation. PGCs are PCs in disguise, in particular, we show how to transform any PGC into a PC with negative weights with only polynomial blowup. PGCs were defined by Zhang et al. only for binary random variables. As our second main result, we show that there is a good reason for this: we prove that PGCs for categorial variables with larger image size do not support tractable marginalization unless NP = P. On the other hand, we show that we can model categorial variables with larger image size as PC with negative weights computing set-multilinear polynomials. These allow for tractable marginalization. In this sense, PCs with negative weights strictly subsume PGCs.
An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids
Authors: Mehdi Jabbari Zideh, Mohammad Reza Khalghani, Sarika Khushalani Solanki
Date: 2024-03-31 00:00:00
Tasks: Cyber Attack Detection

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other unsupervised learning methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.
Dynamic Resource Allocation with Karma: An Experimental Study
Authors: Ezzat Elokda, Heinrich Nax, Saverio Bolognani, Florian Dörfler
Date: 2024-04-03 00:00:00
Tasks: Fairness

A system of non-tradable credits that flow between individuals like karma, hence proposed under that name, is a mechanism for repeated resource allocation that comes with attractive efficiency and fairness properties, in theory. In this study, we test karma in an online experiment in which human subjects repeatedly compete for a resource with time-varying and stochastic individual preferences or urgency to acquire the resource. We confirm that karma has significant and sustained welfare benefits even in a population with no prior training. We identify mechanism usage in contexts with sporadic high urgency, more so than with frequent moderate urgency, and implemented as a simple (binary) karma bidding scheme as particularly effective for welfare improvements: relatively larger aggregate efficiency gains are realized that are (almost) Pareto superior. These findings provide guidance for further testing and for future implementation plans of such mechanisms in the real world.
An early warning system for emerging markets
Authors: Artem Kraevskiy, Artem Prokhorov, Evgeniy Sokolovskiy
Date: 2024-04-04 00:00:00
Tasks: Change Point Detection, Time Series

Financial markets of emerging economies are vulnerable to extreme and cascading information spillovers, surges, sudden stops and reversals. With this in mind, we develop a new online early warning system (EWS) to detect what is referred to as `concept drift' in machine learning, as a `regime shift' in economics and as a `change-point' in statistics. The system explores nonlinearities in financial information flows and remains robust to heavy tails and dependence of extremes. The key component is the use of conditional entropy, which captures shifts in various channels of information transmission, not only in conditional mean or variance. We design a baseline method, and adapt it to a modern high-dimensional setting through the use of random forests and copulas. We show the relevance of each system component to the analysis of emerging markets. The new approach detects significant shifts where conventional methods fail. We explore when this happens using simulations and we provide two illustrations when the methods generate meaningful warnings. The ability to detect changes early helps improve resilience in emerging markets against shocks and provides new economic and financial insights into their operation.
Ground-to-UAV sub-Terahertz channel measurement and modeling
Authors: Da Li, Peian Li, Jiabiao Zhao, Jianjian Liang, Jiacheng Liu, Guohao Liu, Yuanshuai Lei, Wenbo Liu, Jianqin Deng, Fuyong Liu, Jianjun Ma
Date: 2024-04-03 00:00:00

Unmanned Aerial Vehicle (UAV) assisted terahertz (THz) wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analysis and modeling specific to THz wireless channels leveraging UAVs remain under explored. This work delves into a ground-to-UAV channel at 140 GHz, with a specific focus on the influence of UAV hovering behavior on channel performance. Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance. Our findings highlight the minimal impact of UAV orientation adjustments on channel performance and underscore the diminishing necessity for precise alignment between UAVs and ground stations as beamwidth increases.
Joint Optimization on Uplink OFDMA and MU-MIMO for IEEE 802.11ax: Deep Hierarchical Reinforcement Learning Approach
Authors: Hyeonho Noh, Harim Lee, Hyun Jong Yang
Date: 2024-04-03 00:00:00
Tasks: Hierarchical Reinforcement Learning, Scheduling

This letter tackles a joint user scheduling, frequency resource allocation (USRA), multi-input-multi-output mode selection (MIMO MS) between single-user MIMO and multi-user (MU) MIMO, and MU-MIMO user selection problem, integrating uplink orthogonal frequency division multiple access (OFDMA) in IEEE 802.11ax. Specifically, we focus on \textit{unsaturated traffic conditions} where users' data demands fluctuate. In unsaturated traffic conditions, considering packet volumes per user introduces a combinatorial problem, requiring the simultaneous optimization of MU-MIMO user selection and RA along the time-frequency-space axis. Consequently, dealing with the combinatorial nature of this problem, characterized by a large cardinality of unknown variables, poses a challenge that conventional optimization methods find nearly impossible to address. In response, this letter proposes an approach with deep hierarchical reinforcement learning (DHRL) to solve the joint problem. Rather than simply adopting off-the-shelf DHRL, we \textit{tailor} the DHRL to the joint USRA and MS problem, thereby significantly improving the convergence speed and throughput. Extensive simulation results show that the proposed algorithm achieves significantly improved throughput compared to the existing schemes under various unsaturated traffic conditions.