Reviewerly/exHarmony huggingface dataset:
A Non-Rigid Hierarchical Discrete Grid Structure and Its Application to UAVs Conflict Detection and Path Planning

<p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Following the development of the unmanned aerial vehicle (UAV) industry, the number of UAVs has increased significantly, and higher expectations regarding the effect of spatial path planning have been expressed. Traditional coordinate-based conflict detection and path planning methods of UAVs are highly complex and have low computational efficiency. While the conflict detection method based on the grid system can improve efficiency, the rigid grid structure of the existing local grid system or discrete global grid system is insufficient. Since the rigid structure only encodes grid cells, multiple or different size grids are required to identify UAVs in motion, further complicating path planning. The main contributions of this article are as follows. First, we proposed a nonrigid hierarchical discrete grid structure and coding method for spatial three-dimensional grids and grid data model. This method can accomplish the aggregation of any adjacent eight grids to form multiple nonrigid structures, making the original fixed relationship between parent and child grids more flexible. Second, the nonrigid grid structure optimizes the identification ability of grid vertices, grid edges, and grid faces. It has eight times the identification ability of the rigid grid structure. We exploited this structure for UAV identification with the same size grid at any position of the grid structure. Third, based on the nonrigid grid structure, an improved UAV conflict detection method and path planning method are designed. Experimental results indicate that, on an average, the efficiency of the improved conflict detection is an order of magnitude higher than that of the traditional computational detection method. The improved path planning method reduces the path length by an average of 11% compared to the single-grid planning method, and is 3.85 times more efficient than the multi-grid buffer planning method.
MobileNet and Knowledge Distillation-Based Automatic Scenario Recognition Method in Vehicle-to-Vehicle Systems

Automatic scenario recognition (ASR) based on channel status information (CSI) is an important auxiliary technology for various wireless communication systems, especially vehicle-to-vehicle (V2V) systems. CSI-based ASR methods, only based on the on-board wireless receivers rather than other expensive sensors, can identify the surrounding environments of vehicles to serve assisted and autonomous driving technologies. Recently, deep learning (DL)-based ASR methods have been proposed, which generally outperform traditional, but they generally have high computation complexity and large model sizes, which are hardly introduced into mobile edge devices, such as smart vehicles. In this paper, a lightweight convolutional neural network design method, inspired by MobileNet, is introduced into ASR for reducing computation complexity and model sizes. In addition, this method may bring about performance loss, and knowledge distillation (KD) is adopted to make up for this performance loss. Simulation results demonstrated that our proposed ASR method just has 30% computation complexity and model size of the original method, and its performance loss is very limited, especially after KD.
A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision

Self-supervised pre-training for 3D vision has drawn increasing research interest in recent years. In order to learn informative representations, a lot of previous works exploit invariances of 3D features, e.g., perspective-invariance between views of the same scene, modality-invariance between depth and RGB images, format-invariance between point clouds and voxels. Although they have achieved promising results, previous researches lack a systematic and fair comparison of these invariances. To address this issue, our work, for the first time, introduces a unified framework, under which various pre-training methods can be investigated. We conduct extensive experiments and provide a closer look at the contributions of different invariances in 3D pre-training. Also, we propose a simple but effective method that jointly pre-trains a 3D encoder and a depth map encoder using contrastive learning. Models pre-trained with our method gain significant performance boost in downstream tasks. For instance, a pre-trained VoteNet outperforms previous methods on SUN RGB-D and ScanNet object detection benchmarks with a clear margin.
Multi-label learning with kernel local label information

It is important to fully utilize label correlations in multi-label learning. If there is a strong positive correlation between label i and label j , an instance associated with label i also likely has label j simultaneously. So, label correlations can provide some auxiliary information when predicting unseen instances. Existing some multi-label algorithms utilize label correlations to constraint model parameters in the training stage, while label correlations are ignored in the prediction stage. Moreover, it is difficult to obtain relatively accurate label correlations by directly observing data when some labels have few positive instances in the training data. In this paper, instead of directly calculating label correlations by cosine distance and so on, we introduce a kernel function and an manifold regularization to learn them by iteratively updating. Meanwhile, we utilize them and local label information to aid label prediction. Ultimately, unseen instances are predicted by combining auxiliary label predictions and the model outputs. We compare the proposed algorithm with related algorithms on 10 data sets, and the experimental results validate its effectiveness. • Label correlations are used to train a model and predict labels simultaneously. • Use label correlations and local label information to aid label prediction. • Introduce a kernel function and the constraint to learn label correlations. • Extensive experiments verify the effectiveness of the proposed method.
Evaluation of vibrational energies and wave functions of CO2 on a quantum computer

In order to develop a method for evaluating vibrational energies and wave functions of a polyatomic molecule by quantum computing, we introduce the reduced multistate contracted variational quantum eigensolver (RMC-VQE) method, which is a variant of the multistate contracted VQE method [Parrish et al., Phys. Rev. Lett. 122, 230401 (2019)], and apply the RMC-VQE method to a two-mode model of CO2. In the RMC-VQE method, much fewer matrix elements of the Hamiltonian are evaluated on the quantum computer than in the MC-VQE method. By measuring the matrix elements of the Hamiltonian using the quantum computer ibm_kawasaki and diagonalizing the Hamiltonian matrix on a classical computer, we obtain the vibrational energies of the Fermi doublet, which differ from the exact energies obtained using a classical computer by less than 0.1 cm−1. We also obtain accurate vibrational wave functions of the Fermi doublet states.
Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information

A finite element-based image segmentation strategy enhanced by an anisotropic mesh adaptation procedure is presented. The methodology relies on a split Bregman algorithm for the minimisation of a region-based energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation. More precisely, a Bayesian energy functional is considered to account for image spatial information, ensuring that the methodology is able to identify inhomogeneous spatial patterns in complex images. In addition, the anisotropic mesh adaptation guarantees a sharp detection of the interface between background and foreground of the image, with a reduced number of degrees of freedom. The resulting split-adapt Bregman algorithm is tested on a set of real images showing the accuracy and robustness of the method, even in the presence of Gaussian, salt and pepper and speckle noise.
Optimal and Differentially Private Data Acquisition

We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest. We formulate this question as a Bayesian-optimal mechanism design problem, in which an individual can share her (verifiable) data in exchange for a monetary reward or services, but at the same time has a (private) heterogeneous privacy cost which we quantify using differential privacy. We consider two popular differential privacy settings for providing privacy guarantees for the users: central and local. In both settings, we establish minimax lower bounds for the estimation error and derive (near) optimal estimators for given heterogeneous privacy loss levels for users. Building on this characterization, we pose the mechanism design problem as the optimal selection of an estimator and payments that will elicit truthful reporting of users' privacy sensitivities. Under a regularity condition on the distribution of privacy sensitivities we develop efficient algorithmic mechanisms to solve this problem in both privacy settings. Our mechanism in the central setting can be implemented in time O (n log n) where n is the number of users and our mechanism in the local setting admits a Polynomial Time Approximation Scheme (PTAS).
Ship Detection in Visible Remote Sensing Image Based on Saliency Extraction and Modified Channel Features

Ship detection in visible remote sensing (VRS) images has been widely used in the military and civil fields. However, the various backgrounds and the variable scale and orientation bring great difficulties to effective detection. In this paper, we propose a novel ship target detection scheme based on small training samples. The scheme contains two main stages: candidate region extraction and ship identification. In the first stage, we propose a visual saliency detection model based on the difference in covariance statistical characteristics to quickly locate potential ships. Moreover, the multi-scale fusion for the saliency model is designed to overcome the problem of scale variation. In the second stage, we propose a three-channel aggregate feature, which combines a rotation-invariant histogram of oriented gradient and the circular frequency feature. The feature can identify the ship target well by avoiding the impact of its rotation and shift. Finally, we propose the VRS ship dataset that contains more realistic scenes. The results on the VRS ship dataset demonstrate that the saliency model achieves the best AUC value with 0.9476, and the overall detection achieves a better performance of 65.37% in terms of AP@0.5:0.95, which basically meets the need of the detection tasks.
Motion artifact removal in coronary CT angiography based on generative adversarial networks

Coronary motion artifacts affect the diagnostic accuracy of coronary CT angiography (CCTA), especially in the mid right coronary artery (mRCA). The purpose is to correct CCTA motion artifacts of the mRCA using a GAN (generative adversarial network).We included 313 patients with CCTA scans, who had paired motion-affected and motion-free reference images at different R-R interval phases in the same cardiac cycle and included another 53 CCTA cases with invasive coronary angiography (ICA) comparison. Pix2pix, an image-to-image conversion GAN, was trained by the motion-affected and motion-free reference pairs to generate motion-free images from the motion-affected images. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated to evaluate the image quality of GAN-generated images.At the image level, the median of PSNR, SSIM, DSC, and HD of GAN-generated images were 26.1 (interquartile: 24.4-27.5), 0.860 (0.830-0.882), 0.783 (0.714-0.825), and 4.47 (3.00-4.47), respectively, significantly better than the motion-affected images (p < 0.001). At the patient level, the image quality results were similar. GAN-generated images improved the motion artifact alleviation score (4 vs. 1, p < 0.001) and overall image quality score (4 vs. 1, p < 0.001) than those of the motion-affected images. In patients with ICA comparison, GAN-generated images achieved accuracy of 81%, 85%, and 70% in identifying no, < 50%, and ≥ 50% stenosis, respectively, higher than 66%, 72%, and 68% for the motion-affected images.Generative adversarial network-generated CCTA images greatly improved the image quality and diagnostic accuracy compared to motion-affected images.• A generative adversarial network greatly reduced motion artifacts in coronary CT angiography and improved image quality. • GAN-generated images improved diagnosis accuracy of identifying no, < 50%, and ≥ 50% stenosis.
Machine truth serum: a surprisingly popular approach to improving ensemble methods

Wisdom of the crowd (Surowiecki, 2005a) disclosed a striking fact that the majority voting answer from a crowd is usually more accurate than a few individual experts. The same story is observed in machine learning - ensemble methods (Dietterich, 2000) leverage this idea to exploit multiple machine learning algorithms in various settings e.g., supervised learning and semi-supervised learning to achieve better performance by aggregating the predictions of different algorithms than that obtained from any constituent algorithm alone. Nonetheless, the existing aggregating rule would fail when the majority answer of all the constituent algorithms is more likely to be wrong. In this paper, we extend the idea proposed in Bayesian Truth Serum (Prelec, 2004) that "a surprisingly more popular answer is more likely to be the true answer instead of the majority one" to supervised classification further improved by ensemble final predictions method and semi-supervised classification (e.g., MixMatch (Berthelot et al., 2019)) enhanced by ensemble data augmentations method. The challenge for us is to define or detect when an answer should be considered as being "surprising". We present two machine learning aided methods which can reveal the truth when the minority instead of majority has the true answer on both settings of supervised and semi-supervised classification problems. We name our proposed method the Machine Truth Serum. Our experiments on a set of classification tasks (image, text, etc.) show that the classification performance can be further improved by applying Machine Truth Serum in the ensemble final predictions step (supervised) and in the ensemble data augmentations step (semi-supervised).
Cartoon-texture evolution for two-region image segmentation

Abstract Two-region image segmentation is the process of dividing an image into two regions of interest, i.e., the foreground and the background. To this aim, Chan et al. (SIAM J Appl Math 66(5):1632–1648, 2006) designed a model well suited for smooth images. One drawback of this model is that it may produce a bad segmentation when the image contains oscillatory components. Based on a cartoon-texture decomposition of the image to be segmented, we propose a new model that is able to produce an accurate segmentation of images also containing noise or oscillatory information like texture. The novel model leads to a non-smooth constrained optimization problem which we solve by means of the ADMM method. The convergence of the numerical scheme is also proved. Several experiments on smooth, noisy, and textural images show the effectiveness of the proposed model.
Codesign with more-than-humans: toward a meta co-design tool for human-non-human collaborations

Abstract What does more-than-human mean? How can we, as humans, understand that our ecology is only one of the many that do exist within the world? Furthermore, in which way should we step aside to let all ecological actors exercise their agency? And, more specifically, what should be the role of design and designers in tackling complex issues and in contributing to a major shift in thoughts? These questions fostered a reflection on the relation between possible futures and the design practice itself and set the basis for the creation of a provotype. A provotype (from “ provo cation” and “proto type ”) is a conceptual product or an artifact whose objective is to foster reflections and provoke discussions mainly concerning social and environmental sustainability, innovations, and technologies, leaving gaps to be filled with the audience imagination. The research reported in this contribution deals with issues and questions that fall under the umbrella of the topic of alternative biopolitics in future scenarios: how can we co-design with more-than-human actors? In which way can symbiosis between different entities be achieved? What is the meaning of interspecies justice, and which should be the steps to follow to fulfill it? And, finally, maybe the most significant question to focus on: how can communication between different entities be fostered? The designed provotype consists of a fictional event (“The first Multispecies Symposium”) which takes place in 2100, further helped the researchers in opening new reflections that made it possible to experiment with participatory design and to finalize a tool that can be used to share and expand reflections about futures without hierarchies, not human-centered, sustainable progress and hope, participative futures.
Activity detection and counting people using Mask-RCNN with bidirectional ConvLSTM

Image Incorporation concerns, including background confusion, uneven population distribution, and variations in scale and familiarity, can make group counting difficult. Pre-existing information and multi-level contextual representations are required to handle these problems effectively with deep neural networks and Mask-RCNN. Numerous studies on crowd counting use density maps without segmentation, which treat a group of individuals as a single entity. This article offers a hybrid method for crowd counting that combines Mask-RCNN (MRCNN) and a bidirectional convolutional long-term memory network (ConvLSTM), dubbed (CC: MRCNN-biCLSTM). The CC: MRCNN-biCLSTM is based on the Mask-RCN; it first segments instances and generates density maps, which are passed into adversarial learning during the training phase. Finally, the bidirectional convolutional LSTM is being used to return metrics and counts for individuals within a group of individuals. Following that, the suggested activity detection technique based on the Bayesian non-linear filter AD-BNF is used to identify a person’s activity. Additionally, the suggested approach resolves human grouping and enhances metric performance. Extensive studies demonstrate that the suggested method outperforms more sophisticated techniques on four frequently used difficult criteria for density map precision and quality.
Does colonisation explain the low level of growth in African financial markets?

This study examines the effect of colonisation on the growth of financial markets in Africa using a sample of 30 countries for settler mortality and 35 countries for settler sedentarisation over the 1996–2017 period. Using a robust ordinary least squares regression with fixed effects, we find that settler mortality exerts a negative impact on the evolution of financial markets in former African colonies, while the opposite effect is obtained for the settler sedentarisation rate. These results remain robust to the several tests performed. Our study provides new evidence and perspectives to the long-standing debate regarding the relationship between history and finance. As a key recommendation, we propose that policymakers should draw more inspiration from the past to make better policy decisions in terms of economic, institutional, sociocultural, and legal governance to enhance the development of their financial markets.
A Survey on Deep Learning Techniques for Joint Named Entities and Relation Extraction

Named Entity Recognition (NER) and Relation Extraction (RE) are two principal subtasks of knowledge-based systems that extract meaningful information from unstructured text. With Recent advances in Deep Learning techniques, new models use Joint Named Entities and Relation Extraction (JNERE) techniques that simultaneously accomplish NER and RE subtasks. These models avoid the drawbacks of using the traditional pipeline method. As contributions of our study to the other related works, we specifically survey JNERE techniques. The reason for not focusing on pipeline methods or other older techniques is the recent advances of JNERE methods in achieving the state-of-art results for most databases. Additionally, we provide a comprehensive report on the embedding techniques and datasets available for this task. Finally, we discuss the approaches and how they imnpoved the results.
Incremental Few-Shot Object Detection for Robotics

Incremental few-shot learning is highly expected for practical robotics applications. On one hand, robot is desired to learn new tasks quickly and flexibly using only few annotated training samples; on the other hand, such new additional tasks should be learned in a continuous and incremental manner without forgetting the previous learned knowledge dramatically. In this work, we propose a novel Class-Incremental Few- Shot Object Detection (CI-FSOD) framework that enables deep object detection network to perform effective continual learning from just few-shot samples without re-accessing the previous training data. We achieve this by equipping the widely-used Faster-RCNN detector with three elegant components. Firstly, to best preserve performance on the pre-trained base classes, we propose a novel Dual-Embedding-Space (DES) architecture which decouples the representation learning of base and novel categories into different spaces. Secondly, to mitigate the catastrophic forgetting on the accumulated novel classes, we propose a Sequential Model Fusion (SMF) method, which is able to achieve long-term memory without additional storage cost. Thirdly, to promote inter-task class separation in feature space, we propose a novel regularization technique that extends the classification boundary further away from the previous classes to avoid misclassification. Overall, our framework is simple yet effective and outperforms the previous SOTA with a significant margin of 2.4 points in AP performance.
HoloSeg: An Efficient Holographic Segmentation Network for Real-time Scene Parsing

Real-time semantic segmentation is a crucial but challenging dense prediction task for scene parsing. However, the existing CNN-based methods commonly bias the model in favor of speed-boosting compromising spatial resolution due to business requirements and hardware constrains, which impedes the high-accuracy segmentation result. To address the dilemma, we provide a novel Holographic Segmentation Network (HoloSeg), which presents a strong ability of comprehensive information preservation and extraction, and achieves a better trade-off between speed and accuracy. We first design a Lossless Sample Pair (LSP) without any stride for early spatial preservation and later resolution recovery while modeling long-range context dependence. Then, we propose Distributed Pyramid Learning (DPL) to efficiently extract multiscale features and saves a lot of computation. Finally, we propose Resolution Fusion and Restoration (RFR) to fuse multi-level semantic representations across stages and generate output without decoder. Without bells and whistles, HoloSeg achieves state-of-the-art performance on the Cityscapes benchmark which reports 76.24% mIoU at 231 FPS. Code is available online: https://github.com/LiShuTJ/HoloSeg.