Reviewerly/exHarmony huggingface dataset:
CorLab-Net: Anatomical Dependency-Aware Point-Cloud Learning for Automatic Labeling of Coronary Arteries

Automatic coronary artery labeling is essential yet challenging step in coronary artery disease diagnosis for clinician. Previous methods typically overlooked rich relationships with heart chamber and also morphological features of coronary artery. In this paper, we propose a novel point-cloud learning method (called CorLab-Net), which comprehensively captures both inter-organ and intra-artery spatial dependencies as explicit guidance to assist the labeling of these challenging coronary vessels. Specifically, given a 3D point cloud extracted from the segmented coronary artery, our CorLab-Net improves artery labeling from three aspects: First, it encodes the inter-organ anatomical dependency between vessels and heart chambers (in terms of spatial distance field) to effectively locate the blood vessels. Second, it extracts the intra-artery anatomical dependency between vessel points and key joint points (in terms of morphological distance field) to precisely identify different vessel branches at the junctions. Third, it enhances the intra-artery local dependency between neighboring points (by using graph convolutional modules) to correct labeling outliers and improve consistency, especially at the vascular endings. We evaluated our method on a real-clinical dataset. Extensive experiments show that CorLab-Net significantly outperformed the state-of-the-art methods in labeling coronary arteries with large appearance-variance.
Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring

The Visibility-based Persistent Monitoring (VPM) problem seeks to find a set of trajectories (or controllers) for robots to persistently monitor a changing environment. Each robot has a sensor, such as a camera, with a limited field-of-view that is obstructed by obstacles in the environment. The robots may need to coordinate with each other to ensure no point in the environment is left unmonitored for long periods of time. We model the problem such that there is a penalty that accrues every time step if a point is left unmonitored. However, the dynamics of the penalty are unknown to us. We present a Multi-Agent Reinforcement Learning (MARL) algorithm for the VPM problem. Specifically, we present a Multi-Agent Graph Attention Proximal Policy Optimization (MA-G-PPO) algorithm that takes as input the local observations of all agents combined with a low resolution global map to learn a policy for each agent. The graph attention allows agents to share their information with others leading to an effective joint policy. Our main focus is to understand how effective MARL is for the VPM problem. We investigate five research questions with this broader goal. We find that MA-G-PPO is able to learn a better policy than the non-RL baseline in most cases, the effectiveness depends on agents sharing information with each other, and the policy learnt shows emergent behavior for the agents.
Robust Adversarial Attack Against Explainable Deep Classification Models Based on Adversarial Images With Different Patch Sizes and Perturbation Ratios

In recent years, adversarial attack methods have been deceived rather easily on deep neural networks (DNNs).In practice, adversarial patches cause misclassification that can be extremely effective.However, many existing adversarial patches are used for attacking DNNs, and only a few of them apply to both the DNN and its explanation model.In this paper, we present different adversarial patches that misguide the prediction of DNN models and change the cause of prediction results of interpretation models, such as gradient-weighted class activation mapping.The proposed adversarial patches have appropriate location and perturbation ratios, which comprise visible or less visible adversarial patches.In addition, image patches within small arrays are localized without covering or overlapping with any of the main objects in a natural image.In particular, we generate two adversarial patches that cover only 3% and 1.5% of the pixels in the original image, while they do not cover the main objects in the natural image.Our experiments are performed using four pre-trained DNN models and the ImageNet dataset.We also examine the inaccurate results of the interpretation models through mask and heatmap visualization.The proposed adversarial attack method could be a reference for developing robust network interpretation models that are more reliable for the decision-making process of pre-trained DNN models.
Hierarchical 3D Feature Learning forPancreas Segmentation

We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encoder that learns to extract volume features at different scales; features taken at different points of the encoder hierarchy are then sent to multiple 3D decoders that individually predict intermediate segmentation maps. Finally, all segmentation maps are combined to obtain a unique detailed segmentation mask. We test our model on both CT and MRI imaging data: the publicly available NIH Pancreas-CT dataset (consisting of 82 contrast-enhanced CTs) and a private MRI dataset (consisting of 40 MRI scans). Experimental results show that our model outperforms existing methods on CT pancreas segmentation, obtaining an average Dice score of about 88%, and yields promising segmentation performance on a very challenging MRI data set (average Dice score is about 77%). Additional control experiments demonstrate that the achieved performance is due to the combination of our 3D fully-convolutional deep network and the hierarchical representation decoding, thus substantiating our architectural design.
Expressing Disaster Situations for Evacuation Training Using Markerless Augmented Reality

Evacuation training is crucial for protecting human lives from natural and man-made disasters, but it should be more realistic to achieve training effects. In this study, we focused on expressing disaster situations using markerless augmented reality to achieve realistic evacuation training. We prototyped a scenario-based evacuation training system that superimposed three-dimensional computer graphics of disaster situations (e.g. fire and debris) onto real-time vision (captured by Android tablets or smartphones) using ARCore and Unity3D. Through preliminary experiments, we found that the prototype system can provide realistic expression and potentially be used for evacuation training, but we have not yet clarified the training results and how the expressions influenced participants' emotions.
Coarse-to-Fine Reasoning for Visual Question Answering

Bridging the semantic gap between image and question is an important step to improve the accuracy of the Visual Question Answering (VQA) task. However, most of the existing VQA methods focus on attention mechanisms or visual relations for reasoning the answer, while the features at different semantic levels are not fully utilized. In this paper, we present a new reasoning framework to fill the gap between visual features and semantic clues in the VQA task. Our method first extracts the features and predicates from the image and question. We then propose a new reasoning framework to effectively jointly learn these features and predicates in a coarse-to-fine manner. The intensively experimental results on three large-scale VQA datasets show that our proposed approach achieves superior accuracy comparing with other state-of-the-art methods. Furthermore, our reasoning framework also provides an explainable way to understand the decision of the deep neural network when predicting the answer. Our source codes can be found at: https://github.com/aioz-ai/CFR_VQA
Querying a Matrix through Matrix-Vector Products

We consider algorithms with access to an unknown matrix M ε F n×d via matrix-vector products , namely, the algorithm chooses vectors v 1 , ⃛ , v q , and observes Mv 1 , ⃛ , Mv q . Here the v i can be randomized as well as chosen adaptively as a function of Mv 1 , ⃛ , Mv i-1 . Motivated by applications of sketching in distributed computation, linear algebra, and streaming models, as well as connections to areas such as communication complexity and property testing, we initiate the study of the number q of queries needed to solve various fundamental problems. We study problems in three broad categories, including linear algebra, statistics problems, and graph problems. For example, we consider the number of queries required to approximate the rank, trace, maximum eigenvalue, and norms of a matrix M; to compute the AND/OR/Parity of each column or row of M, to decide whether there are identical columns or rows in M or whether M is symmetric, diagonal, or unitary; or to compute whether a graph defined by M is connected or triangle-free. We also show separations for algorithms that are allowed to obtain matrix-vector products only by querying vectors on the right, versus algorithms that can query vectors on both the left and the right. We also show separations depending on the underlying field the matrix-vector product occurs in. For graph problems, we show separations depending on the form of the matrix (bipartite adjacency versus signed edge-vertex incidence matrix) to represent the graph. Surprisingly, very few works discuss this fundamental model, and we believe a thorough investigation of problems in this model would be beneficial to a number of different application areas.
Learning Sparse Masks for Diffusion-Based Image Inpainting

Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse data. Its quality strongly depends on the choice of known data. Optimising their spatial location – the inpainting mask – is challenging. A commonly used tool for this task are stochastic optimisation strategies. However, they are slow as they compute multiple inpainting results. We provide a remedy in terms of a learned mask generation model. By emulating the complete inpainting pipeline with two networks for mask generation and neural surrogate inpainting, we obtain a model for highly efficient adaptive mask generation. Experiments indicate that our model can achieve competitive quality with an acceleration by as much as four orders of magnitude. Our findings serve as a basis for making diffusion-based inpainting more attractive for applications such as image compression, where fast encoding is highly desirable.
Gaussian-Based Runtime Detection of Out-of-distribution Inputs for Neural Networks

In this short paper, we introduce a simple approach for runtime monitoring of deep neural networks and show how to use it for out-of-distribution detection. The approach is based on inferring Gaussian models of some of the neurons and layers. Despite its simplicity, it performs better than recently introduced approaches based on interval abstractions which are traditionally used in verification.
Aha! Adaptive History-driven Attack for Decision-based Black-box Models

The decision-based black-box attack means to craft adversarial examples with only the top-1 label of the victim model available. A common practice is to start from a large perturbation and then iteratively reduce it with a deterministic direction and a random one while keeping it adversarial. The limited information obtained from each query and inefficient direction sampling impede attack efficiency, making it hard to obtain a small enough perturbation within a limited number of queries. To tackle this problem, we propose a novel attack method termed Adaptive History-driven Attack (AHA) which gathers information from all historical queries as the prior for current sampling. Moreover, to balance between the deterministic direction and the random one, we dynamically adjust the coefficient according to the ratio of the actual magnitude reduction to the expected one. Such a strategy improves the success rate of queries during optimization, letting adversarial examples move swiftly along the decision boundary. Our method can also integrate with subspace optimization like dimension reduction to further improve efficiency. Extensive experiments on both ImageNet and CelebA datasets demonstrate that our method achieves at least 24.3% lower magnitude of perturbation on average with the same number of queries. Finally, we prove the practical potential of our method by evaluating it on popular defense methods and a real-world system provided by MEGVII Face++.
Gated multi-attention representation in reinforcement learning

Deep reinforcement learning (DRL) has achieved great success in recent years by combining the feature extraction power of deep learning and the decision power of reinforcement learning techniques. In the literature, Convolutional Neural Networks (CNN) is usually used as the feature extraction method and recent studies have shown that the performances of the DRL algorithms can be greatly improved with the utilization of the attention mechanism, where the raw attentions are directly used for the decision-making. However, as is well-known, reinforcement learning is a trial-and-error process and it is almost impossible to learn an optimal policy in the beginning of the learning, especially in environments with sparse rewards, which in turn will cause the raw attention-based models can only remember and utilize the attention information indiscriminately for different areas and may focus on some task-irrelevant regions, but the focusing on such task-irrelevant regions is usually helpless and ineffective for the agent to find the optimal policy. To address this issue, we propose a gated multi-attention mechanism, which is then combined with the Deep Q-learning network (GMAQN). The gated multi-attention representation module (GMA) in GMAQN can effectively eliminate task-irrelevant attention information in the early phase of the trial-and-error process and improve the stability of the model. The proposed method has been demonstrated on the challenging domain of classic Atari 2600 games and experimental results show that compared with the baselines, our method can achieve better performance in terms of both the scores and the effect of focusing in the key regions.
AC Simplifications and Closure Redundancies in the Superposition Calculus

Reasoning in the presence of associativity and commutativity (AC) is well known to be challenging due to prolific nature of these axioms. Specialised treatment of AC axioms is mainly supported by provers for unit equality which are based on Knuth-Bendix completion. The main ingredient for dealing with AC in these provers are ground joinability criteria adapted for AC. In this paper we extend AC joinability from the context of unit equalities and Knuth-Bendix completion to the superposition calculus and full first-order logic. Our approach is based on an extension of the Bachmair-Ganzinger model construction and a new redundancy criterion which covers ground joinability. A by-product of our approach is a new criterion for applicability of demodulation which we call encompassment demodulation. This criterion is useful in any superposition theorem prover, independently of AC theories, and we demonstrate that it enables demodulation in many more cases, compared to the standard criterion.
Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images

Deep learning networks have shown promising performance for object localization in medical images, but require large amount of annotated data for supervised training. To address this problem, we propose: 1) A novel contrastive learning method which embeds the anatomical structure by predicting the Relative Position Regression (RPR) between any two patches from the same volume; 2) An one-shot framework for organ and landmark localization in volumetric medical images. Our main idea comes from that tissues and organs from different human bodies own similar relative position and context. Therefore, we could predict the relative positions of their non-local patches, thus locate the target organ. Our one-shot localization framework is composed of three parts: 1) A deep network trained to project the input patch into a 3D latent vector, representing its anatomical position; 2) A coarse-to-fine framework contains two projection networks, providing more accurate localization of the target; 3) Based on the coarse-to-fine model, we transfer the organ bounding-box (B-box) detection to locating six extreme points along x, y and z directions in the query volume. Experiments on multi-organ localization from head-and-neck (HaN) and abdominal CT volumes showed that our method acquired competitive performance in real time, which is more accurate and \(10^5\) times faster than template matching methods with the same setting for one-shot localization in 3D medical images. Code is available at https://github.com/HiLab-git/RPR-Loc.
Confidentially judging the relationship between an integer and an interval against malicious adversaries and its applications

With the growing prominence of privacy protection issues in big data, artificial intelligence and blockchain, secure multi-party computation has become a research hotspot. Confidentially computing the set problem is an important branch of secure multi-party computation. The relationship between an integer and an interval is the most basic problem in the study of computing set problems. At present, there are many research solutions for secure multi-party computation against malicious adversaries. In this paper, we analyze some possible malicious attacks in the protocol of determining an integer and an interval relationship under the semi-honest model. By using the Goldwasser–Micali encryption scheme, Paillier encryption algorithm, zero-knowledge proof and cut-choose method, we design the secret judgment protocol of an integer and an interval relationship under the malicious model. Finally, an ideal-practical example proof method is used to prove that the protocol is secure under the malicious model. Compared with the existing schemes, it not only keeps good efficiency, but also can resist the attack of malicious opponents, and the protocol is more fair. The scheme has a wide application prospect.
Better constraints of imperceptibility, better adversarial examples in the text

State-of-the-art adversarial attacks in the text domain have shown their power to induce machine learning models to produce abnormal outputs. The samples generated in these attacks have three important attributes: attack ability, transferability, and imperceptibility. However, compared with the other two attributes, the imperceptibility of adversarial examples has not been well investigated. Unlike the pixel-level perturbations in images, adversarial perturbations in the text are usually traceable, reflecting changes in characters, words, or sentences. The generation of imperceptible samples in texts is more difficult than in images. Therefore, how to constrain adversarial perturbations added in the text is a crucial step to construct more natural adversarial texts. Unfortunately, recent studies merely select measurements to constrain the added adversarial perturbations, but none of them explain where these measurements are suitable, which one is better, and how they perform in different kinds of adversarial attacks. In this paper, we fill this gap by comparing the performance of these metrics in various attacks. Furthermore, we propose a stricter constraint for word-level attacks to obtain more imperceptible samples. It is also helpful to enhance existing word-level attacks for adversarial training.
Impact of the CNN Patch Size in the Writer Identification

Writer identification remains a very interesting challenge where many researchers have tried to find the various parameters which can help to find the right writer of a handwritten text. The introduction of deep learning has made it possible to achieve unprecedented records in the field. However, the question to ask, what size of patch to use to train a CNN model in order to have the best performance? In our paper, we try to find an answer to this question by investigating the results of the use of several patch sizes for a Resnet34 model and two languages Arabic and French from the LAMIS-MSHD dataset.
Design of Augmented Reality Training Content for Railway Vehicle Maintenance Focusing on the Axle-Mounted Disc Brake System

Light maintenance training for electric multiple-unit components of the organization of railway operations is generally conducted using maintenance manuals and work videos, following the guidelines of each organization. These manuals are in the form of booklets, complicated and inconvenient for maintenance operators to carry. Therefore, training content that visualizes maintenance procedures in a three-dimensions (3D) space is necessary to overcome the drawbacks of booklet-type training. In this study, we developed augmented reality (AR)-based training content for railway vehicle maintenance to increase training efficiency. Providing warning signs for risky procedures reduces human error, and transparency control makes trainees check the product hierarchy. A virtual experience based on the maintenance manual is provided to improve maintenance proficiency. An axle-mounted disc brake system maintenance manual is implemented in AR to reflect the requirements of maintenance operators. The convenience of this tool is improved by loading the AR content on a mobile device. Two methods of verification were used: the system usability scale (SUS) survey and training efficiency evaluation. The resulting SUS grade was B (excellent), and the training efficiency improved by 34%.
Gold and silver deportment in sulfide ores – A case study of the Freiberg epithermal Ag-Pb-Zn district, Germany

A C T Deportment data is essential for the planning of mining and minerals processing operations.This need is particularly tangible for deposits of noble metals, such as gold and silver.Therefore, the current paucity of published gold and silver deportment data for individual ore deposits and districtsand the concomitant lack of understanding how this relates to salient geological and mineralogical features of these depositsis surprising.In the present study, we apply a combination of bulk geochemistry, laser ablation-inductively coupled plasma-mass spectrometry, electron microprobe analysis, and mineral liberation analysis to gold-and silver-rich samples from the epithermal Freiberg Ag-Pb-Zn district, to investigate variability in gold and silver deportments, as well as the corresponding geological/mineralogical controls.The results show that the main carriers of gold are electrum and arsenopyrite, whereas silver is mostly hosted by Ag-sulfosalts (pyrargyrite, miargyrite, polybasite) and fahlore (freibergite).Deportments vary greatly between samples.These variations can be related to the relative abundances of different minerals within the samples, which in turn reflect their spatio-temporal position within the district.Comparisons with other epithermal Ag-Pb-Zn districts similar to Freiberg indicate that the results presented here are of general significance.