pwc-archive/papers-with-abstracts huggingface dataset:
Image Data Hiding in Neural Compressed Latent Representations
Authors: Chen-Hsiu Huang, Ja-Ling Wu
Date: 2023-10-01 00:00:00
Tasks: Decoder

We propose an end-to-end learned image data hiding framework that embeds and extracts secrets in the latent representations of a generic neural compressor. By leveraging a perceptual loss function in conjunction with our proposed message encoder and decoder, our approach simultaneously achieves high image quality and high bit accuracy. Compared to existing techniques, our framework offers superior image secrecy and competitive watermarking robustness in the compressed domain while accelerating the embedding speed by over 50 times. These results demonstrate the potential of combining data hiding techniques and neural compression and offer new insights into developing neural compression techniques and their applications.
Towards Understanding Adversarial Transferability in Federated Learning
Authors: Yijiang Li, Ying Gao, Haohan Wang
Date: 2023-10-01 00:00:00
Tasks: Attribute, Federated Learning

We investigate a specific security risk in FL: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients but later switching to an adversarial role. They use their data, which was part of the training set, to train a substitute model and conduct transferable adversarial attacks against the federated model. This type of attack is subtle and hard to detect because these clients initially appear to be benign. The key question we address is: How robust is the FL system to such covert attacks, especially compared to traditional centralized learning systems? We empirically show that the proposed attack imposes a high security risk to current FL systems. By using only 3\% of the client's data, we achieve the highest attack rate of over 80\%. To further offer a full understanding of the challenges the FL system faces in transferable attacks, we provide a comprehensive analysis over the transfer robustness of FL across a spectrum of configurations. Surprisingly, FL systems show a higher level of robustness than their centralized counterparts, especially when both systems are equally good at handling regular, non-malicious data. We attribute this increased robustness to two main factors: 1) Decentralized Data Training: Each client trains the model on its own data, reducing the overall impact of any single malicious client. 2) Model Update Averaging: The updates from each client are averaged together, further diluting any malicious alterations. Both practical experiments and theoretical analysis support our conclusions. This research not only sheds light on the resilience of FL systems against hidden attacks but also raises important considerations for their future application and development.
Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinoma
Authors: Christiaan Viviers, Mark Ramaekers, Amaan Valiuddin, Terese Hellström, Nick Tasios, John van der Ven, Igor Jacobs, Lotte Ewals, Joost Nederend, Peter de With, Misha Luyer, Fons van der Sommen
Date: 2023-10-01 00:00:00
Tasks: Decision Making, Specificity

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with limited treatment options. This research proposes a workflow and deep learning-based segmentation models to automatically assess tumor-vessel involvement, a key factor in determining tumor resectability. Correct assessment of resectability is vital to determine treatment options. The proposed workflow involves processing CT scans to segment the tumor and vascular structures, analyzing spatial relationships and the extent of vascular involvement, which follows a similar way of working as expert radiologists in PDAC assessment. Three segmentation architectures (nnU-Net, 3D U-Net, and Probabilistic 3D U-Net) achieve a high accuracy in segmenting veins, arteries, and the tumor. The segmentations enable automated detection of tumor involvement with high accuracy (0.88 sensitivity and 0.86 specificity) and automated computation of the degree of tumor-vessel contact. Additionally, due to significant inter-observer variability in these important structures, we present the uncertainty captured by each of the models to further increase insights into the predicted involvement. This result provides clinicians with a clear indication of tumor-vessel involvement and may be used to facilitate more informed decision-making for surgical interventions. The proposed method offers a valuable tool for improving patient outcomes, personalized treatment strategies and survival rates in pancreatic cancer.
JPEG Information Regularized Deep Image Prior for Denoising
Authors: Tsukasa Takagi, Shinya Ishizaki, Shin-ichi Maeda
Date: 2023-10-02 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Inductive Bias

Image denoising is a representative image restoration task in computer vision. Recent progress of image denoising from only noisy images has attracted much attention. Deep image prior (DIP) demonstrated successful image denoising from only a noisy image by inductive bias of convolutional neural network architectures without any pre-training. The major challenge of DIP based image denoising is that DIP would completely recover the original noisy image unless applying early stopping. For early stopping without a ground-truth clean image, we propose to monitor JPEG file size of the recovered image during optimization as a proxy metric of noise levels in the recovered image. Our experiments show that the compressed image file size works as an effective metric for early stopping.
A Decentralized Cooperative Navigation Approach for Visual Homing Networks
Authors: Mohamed Rahouti, Damian Lyons, Senthil Kumar Jagatheesaperumal, Kaiqi Xiong
Date: 2023-10-02 00:00:00
Tasks: Visual Navigation

Visual homing is a lightweight approach to visual navigation. Given the stored information of an initial 'home' location, the navigation task back to this location is achieved from any other location by comparing the stored home information to the current image and extracting a motion vector. A challenge that constrains the applicability of visual homing is that the home location must be within the robot's field of view to initiate the homing process. Thus, we propose a blockchain approach to visual navigation for a heterogeneous robot team over a wide area of visual navigation. Because it does not require map data structures, the approach is useful for robot platforms with a small computational footprint, and because it leverages current visual information, it supports a resilient and adaptive path selection. Further, we present a lightweight Proof-of-Work (PoW) mechanism for reaching consensus in the untrustworthy visual homing network.
A simple thinking about the application of the attention mechanism in medical ultrasound image segmentation task
Authors: Gongping Chen, Rui Wang, Xiaotao Yin, Liang Cui, Yu Dai
Date: 2023-10-02 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The AI-based assisted diagnosis programs have been widely investigated on medical ultrasound images. Complex scenario of ultrasound image, in which the coupled interference of internal and external factors is severe, brings a unique challenge for localize the object region automatically and precisely in ultrasound images. In this study, we seek to propose a more general and robust Benchmark Attention Adaptive Framework (BAAF) to assist doctors segment or diagnose lesions and tissues in ultrasound images more quickly and accurately. Different from existing attention schemes, the BAAF consists of a parallel hybrid attention module (PHAM) and an adaptive calibration mechanism (ACM). Specifically, BAAF first coarsely calibrates the input features from the channel and spatial dimensions, and then adaptively selects more robust lesion or tissue characterizations from the coarse-calibrated feature maps. The design of BAAF further optimizes the "what" and "where" focus and selection problems in CNNs and seeks to improve the segmentation accuracy of lesions or tissues in medical ultrasound images. The method is evaluated on four medical ultrasound segmentation tasks, and the adequate experimental results demonstrate the remarkable performance improvement over existing state-of-the-art methods. In addition, the comparison with existing attention mechanisms also demonstrates the superiority of BAAF. This work provides the possibility for automated medical ultrasound assisted diagnosis and reduces reliance on human accuracy and precision.
HyMNet: a Multimodal Deep Learning System for Hypertension Classification using Fundus Photographs and Cardiometabolic Risk Factors
Authors: Mohammed Baharoon, Hessa Almatar, Reema Alduhayan, Tariq Aldebasi, Badr Alahmadi, Yahya Bokhari, Mohammed Alawad, Ahmed Almazroa, Abdulrhman Aljouie
Date: 2023-10-02 00:00:00
Tasks: Hypertension detection, Multimodal Deep Learning

In recent years, deep learning has shown promise in predicting hypertension (HTN) from fundus images. However, most prior research has primarily focused on analyzing a single type of data, which may not capture the full complexity of HTN risk. To address this limitation, this study introduces a multimodal deep learning (MMDL) system, dubbed HyMNet, which combines fundus images and cardiometabolic risk factors, specifically age and gender, to improve hypertension detection capabilities. Our MMDL system uses RETFound, a foundation model pre-trained on 1.6 million retinal images, for the fundus path and a fully connected neural network for the age and gender path. The two paths are jointly trained by concatenating the feature vectors from each path that are then fed into a fusion network. The system was trained on 5,016 retinal images from 1,243 individuals collected from the Saudi Ministry of National Guard Health Affairs. The results show that the multimodal model that integrates fundus images along with age and gender outperforms the unimodal system trained solely on fundus photographs, with an F1 score of 0.771 [0.747, 0.796], and 0.745 [0.719, 0.772] for hypertension detection, respectively. Additionally, we studied the effect underlying diabetes mellitus has on the model's predictive ability, concluding that diabetes is used as a confounding variable for distinguishing hypertensive cases. Our code and model weights are publicly available at https://github.com/MohammedSB/HyMNet.
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation
Authors: Jiaxin Zhuang, Luyang Luo, Zhixuan Chen, Linshan Wu
Date: 2023-10-02 00:00:00
Tasks: Computational Efficiency, Organ Segmentation, Segmentation, Tumor Segmentation

Deep-learning (DL) based methods are playing an important role in the task of abdominal organs and tumors segmentation in CT scans. However, the large requirements of annotated datasets heavily limit its development. The FLARE23 challenge provides a large-scale dataset with both partially and fully annotated data, which also focuses on both segmentation accuracy and computational efficiency. In this study, we propose to use the strategy of Semi-Supervised Learning (SSL) and iterative pseudo labeling to address FLARE23. Initially, a deep model (nn-UNet) trained on datasets with complete organ annotations (about 220 scans) generates pseudo labels for the whole dataset. These pseudo labels are then employed to train a more powerful segmentation model. Employing the FLARE23 dataset, our approach achieves an average DSC score of 89.63% for organs and 46.07% for tumors on online validation leaderboard. For organ segmentation, We obtain 0.9007\% DSC and 0.9493\% NSD. For tumor segmentation, we obtain 0.3785% DSC and 0.2842% NSD. Our code is available at https://github.com/USTguy/Flare23.
Cross-adversarial local distribution regularization for semi-supervised medical image segmentation
Authors: Thanh Nguyen-Duc, Trung Le, Roland Bammer, He Zhao, Jianfei Cai, Dinh Phung
Date: 2023-10-02 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

Medical semi-supervised segmentation is a technique where a model is trained to segment objects of interest in medical images with limited annotated data. Existing semi-supervised segmentation methods are usually based on the smoothness assumption. This assumption implies that the model output distributions of two similar data samples are encouraged to be invariant. In other words, the smoothness assumption states that similar samples (e.g., adding small perturbations to an image) should have similar outputs. In this paper, we introduce a novel cross-adversarial local distribution (Cross-ALD) regularization to further enhance the smoothness assumption for semi-supervised medical image segmentation task. We conducted comprehensive experiments that the Cross-ALD archives state-of-the-art performance against many recent methods on the public LA and ACDC datasets.
Towards Robust Cardiac Segmentation using Graph Convolutional Networks
Authors: Gilles Van De Vyver, Sarina Thomas, Guy Ben-Yosef, Sindre Hellum Olaisen, Håvard Dalen, Lasse Løvstakken, Erik Smistad
Date: 2023-10-02 00:00:00
Tasks: Anatomy, Cardiac Segmentation, Segmentation

Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical segmentation and can segment cardiac structures in real-time with average errors comparable to inter-observer variability. However, this architecture still generates large outliers that are often anatomically incorrect. This work uses the concept of graph convolutional neural networks that predict the contour points of the structures of interest instead of labeling each pixel. We propose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset. Additionally, this work contributes with an ablation study on the graph convolutional architecture and an evaluation of clinical measurements on the clinical HUNT4 dataset. Finally, we propose to use the inter-model agreement of the U-Net and the graph network as a predictor of both the input and segmentation quality. We show this predictor can detect out-of-distribution and unsuitable input images in real-time. Source code is available online: https://github.com/gillesvntnu/GCN_multistructure
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device
Authors: Ties van Rozendaal, Tushar Singhal, Hoang Le, Guillaume Sautiere, Amir Said, Krishna Buska, Anjuman Raha, Dimitris Kalatzis, Hitarth Mehta, Frank Mayer, Liang Zhang, Markus Nagel, Auke Wiggers
Date: 2023-10-02 00:00:00
Tasks: Decoder, GPU, Motion Compensation, Quantization, Video Compression

Neural video codecs have recently become competitive with standard codecs such as HEVC in the low-delay setting. However, most neural codecs are large floating-point networks that use pixel-dense warping operations for temporal modeling, making them too computationally expensive for deployment on mobile devices. Recent work has demonstrated that running a neural decoder in real time on mobile is feasible, but shows this only for 720p RGB video. This work presents the first neural video codec that decodes 1080p YUV420 video in real time on a mobile device. Our codec relies on two major contributions. First, we design an efficient codec that uses a block-based motion compensation algorithm available on the warping core of the mobile accelerator, and we show how to quantize this model to integer precision. Second, we implement a fast decoder pipeline that concurrently runs neural network components on the neural signal processor, parallel entropy coding on the mobile GPU, and warping on the warping core. Our codec outperforms the previous on-device codec by a large margin with up to 48% BD-rate savings, while reducing the MAC count on the receiver side by $10 \times$. We perform a careful ablation to demonstrate the effect of the introduced motion compensation scheme, and ablate the effect of model quantization.
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Authors: Shengyi Huang, Jiayi Weng, Rujikorn Charakorn, Min Lin, Zhongwen Xu, Santiago Ontañón
Date: 2023-09-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time. Despite recent progress in the field, reproducibility issues have not been sufficiently explored. This paper first shows that the typical actor-learner framework can have reproducibility issues even if hyperparameters are controlled. We then introduce Cleanba, a new open-source platform for distributed DRL that proposes a highly reproducible architecture. Cleanba implements highly optimized distributed variants of PPO and IMPALA. Our Atari experiments show that these variants can obtain equivalent or higher scores than strong IMPALA baselines in moolib and torchbeast and PPO baseline in CleanRL. However, Cleanba variants present 1) shorter training time and 2) more reproducible learning curves in different hardware settings. Cleanba's source code is available at \url{https://github.com/vwxyzjn/cleanba}
Machine Learning Clifford invariants of ADE Coxeter elements
Authors: Siqi Chen, Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst, Dmitrii Riabchenko
Date: 2023-09-29 00:00:00

There has been recent interest in novel Clifford geometric invariants of linear transformations. This motivates the investigation of such invariants for a certain type of geometric transformation of interest in the context of root systems, reflection groups, Lie groups and Lie algebras: the Coxeter transformations. We perform exhaustive calculations of all Coxeter transformations for $A_8$, $D_8$ and $E_8$ for a choice of basis of simple roots and compute their invariants, using high-performance computing. This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning. In this paper we focus on neural network classification and principal component analysis. Since the output -- the invariants -- is fully determined by the choice of simple roots and the permutation order of the corresponding reflections in the Coxeter element, we expect huge degeneracy in the mapping. This provides the perfect setup for machine learning, and indeed we see that the datasets can be machine learned to very high accuracy. This paper is a pump-priming study in experimental mathematics using Clifford algebras, showing that such Clifford algebraic datasets are amenable to machine learning, and shedding light on relationships between these novel and other well-known geometric invariants and also giving rise to analytic results.
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym
Authors: Elena Raponi, Nathanael Rakotonirina Carraz, Jérémy Rapin, Carola Doerr, Olivier Teytaud
Date: 2023-09-29 00:00:00
Tasks: Bayesian Optimization, Benchmarking, Hyperparameter Optimization, OpenAI Gym

The growing ubiquity of machine learning (ML) has led it to enter various areas of computer science, including black-box optimization (BBO). Recent research is particularly concerned with Bayesian optimization (BO). BO-based algorithms are popular in the ML community, as they are used for hyperparameter optimization and more generally for algorithm configuration. However, their efficiency decreases as the dimensionality of the problem and the budget of evaluations increase. Meanwhile, derivative-free optimization methods have evolved independently in the optimization community. Therefore, we urge to understand whether cross-fertilization is possible between the two communities, ML and BBO, i.e., whether algorithms that are heavily used in ML also work well in BBO and vice versa. Comparative experiments often involve rather small benchmarks and show visible problems in the experimental setup, such as poor initialization of baselines, overfitting due to problem-specific setting of hyperparameters, and low statistical significance. With this paper, we update and extend a comparative study presented by Hutter et al. in 2013. We compare BBO tools for ML with more classical heuristics, first on the well-known BBOB benchmark suite from the COCO environment and then on Direct Policy Search for OpenAI Gym, a reinforcement learning benchmark. Our results confirm that BO-based optimizers perform well on both benchmarks when budgets are limited, albeit with a higher computational cost, while they are often outperformed by algorithms from other families when the evaluation budget becomes larger. We also show that some algorithms from the BBO community perform surprisingly well on ML tasks.
Latent Space Symmetry Discovery
Authors: Jianke Yang, Nima Dehmamy, Robin Walters, Rose Yu
Date: 2023-09-29 00:00:00
Tasks: Equation Discovery

Equivariant neural networks require explicit knowledge of the symmetry group. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivariance from data. However, existing symmetry discovery methods are limited to simple linear symmetries and cannot handle the complexity of real-world data. We propose a novel generative model, Latent LieGAN (LaLiGAN), which can discover symmetries of nonlinear group actions. It learns a mapping from the data space to a latent space where the symmetries become linear and simultaneously discovers symmetries in the latent space. Theoretically, we show that our model can express nonlinear symmetries under some conditions about the group action. Experimentally, we demonstrate that our method can accurately discover the intrinsic symmetry in high-dimensional dynamical systems. LaLiGAN also results in a well-structured latent space that is useful for downstream tasks including equation discovery and long-term forecasting.
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
Authors: Samiul Alam, Tuo Zhang, Tiantian Feng, Hui Shen, Zhichao Cao, Dong Zhao, JeongGil Ko, Kiran Somasundaram, Shrikanth S. Narayanan, Salman Avestimehr, Mi Zhang
Date: 2023-09-29 00:00:00
Tasks: Benchmarking, Federated Learning

There is a significant relevance of federated learning (FL) in the realm of Artificial Intelligence of Things (AIoT). However, most existing FL works do not use datasets collected from authentic IoT devices and thus do not capture unique modalities and inherent challenges of IoT data. To fill this critical gap, in this work, we introduce FedAIoT, an FL benchmark for AIoT. FedAIoT includes eight datasets collected from a wide range of IoT devices. These datasets cover unique IoT modalities and target representative applications of AIoT. FedAIoT also includes a unified end-to-end FL framework for AIoT that simplifies benchmarking the performance of the datasets. Our benchmark results shed light on the opportunities and challenges of FL for AIoT. We hope FedAIoT could serve as an invaluable resource to foster advancements in the important field of FL for AIoT. The repository of FedAIoT is maintained at https://github.com/AIoT-MLSys-Lab/FedAIoT.