Reviewerly/exHarmony huggingface dataset:
Time-optimal trajectory generation for aerial coverage of urban building

This paper presents a hierarchical architecture for generating the cooperative trajectories of multiple unmanned aerial vehicles (UAVs) attached with camera sensors, which aim to cover buildings with optimal time in 3D urban environment. It incorporates the centralized high-level layer performing the mission analysis and task allocation functions yielding instructions that are transmitted to the UAVs, as well as the decentralized low-level fashion that the UAVs perform the trajectory generation function in turn. First, the mission features especially the theoretical coverage time of building envelope are extracted quantitatively, and the buildings are then allocated to the UAVs in order to convert the cooperative control problem into multiple single-vehicle control problems. Then, each UAV visits and scans its allocated buildings sequentially, and the corresponding coverage trajectories are obtained by the parallel circle strategy (PCS) and the time-optimal guidance vector field (TOGVF) transition method, as well as the interfered fluid dynamic system (IFDS) method for obstacle avoidance. Finally, our proposed method is verified in various scenarios, and the simulation results show its high efficiency with least time to solve the cooperative coverage problem.
A safe reinforced feature screening strategy for lasso based on feasible solutions

As a popular method in machine learning, lasso performs regression and feature selection simultaneously. However, for large datasets, the training efficiency of lasso remains a challenge. Recently, an Enhanced screening rule via Dual Polytope Projection (EDPP) was proposed to substantially reduce the scale of lasso by deleting inactive features beforehand. However, EDPP may mistakenly discard active features in practice due to the unavailable optimal solutions. To solve this problem, a safe reinforced feature screening rule based on EDPP and feasible solutions (S-EDPP) is introduced in this paper. By utilizing feasible solutions and estimating a proper upper bound of the deviation, S-EDPP can be guaranteed to be safe both in theory and in practice. Theoretical analysis of the deviation term in S-EDPP is given to verify its efficiency. Furthermore, S-EDPP is also extended to accelerate the Elastic Net, which is a corrective method of lasso. Experiments on synthetic and real datasets verify that S-EDPP is a safe modification of EDPP and it gives superior performance than other existing safe rules.
Complexity of Causal Order Structure in Distributed Quantum Information Processing: More Rounds of Classical Communication Reduce Entanglement Cost

We prove a trade-off relation between the entanglement cost and classical communication complexity of causal order structure of a protocol in distributed quantum information processing. We consider an implementation of a class of two-qubit unitary gates by local operations and classical communication (LOCC) assisted with shared entanglement, in an information theoretical scenario of asymptotically many input pairs and vanishingly small error. We prove the trade-off relation by showing that (i) one ebit of entanglement per pair is necessary for implementing the unitary by any two-round protocol, and that (ii) the entanglement cost by a three-round protocol is strictly smaller than one ebit per pair. We also provide an example of bipartite unitary gates for which there is no such trade-off.
A Strategy for Live Migration of Virtual Machines in a Cloud Federation

Migration of virtual machines (VMs) from one data center to another is essential to deal with scenarios such as load balancing, maintenance, power management, VM failures, etc. Application of VM migration is not only limited to a single cloud service provider, but also to inter-cloud architectures such as cloud federation. Migration in a federation is a complex operation as it encompasses many autonomous service providers with different standards, rules, and protocols. Migration can be carried out in two ways, first, live and, second, non-live. In case of live migration, a VM continues to execute at the source in contrast to non-live where its execution is suspended. In this paper, a framework for secure live migration of VMs in a cloud federation is proposed. Costs associated with it is analyzed for serial, parallel, and improved serial strategies. The proposed framework is simulated using a CloudSim simulator. Metrics considered for evaluation are communication overhead, migration time, and downtime. Further, power consumption for all strategies is calculated and compared. It is observed that downtime is least for parallel, whereas migration time is least for improved serial. The power consumption is highest for parallel and least for improved serial.
Generalized Bayesian Model Selection for Speckle on Remote Sensing Images

Synthetic aperture radar (SAR) and ultrasound (US) are two important active imaging techniques for remote sensing, both of which are subject to speckle noise caused by coherent summation of back-scattered waves and subsequent nonlinear envelope transformations. Estimating the characteristics of this multiplicative noise is crucial to develop denoising methods and to improve statistical inference from remote sensing images. In this paper, reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with a wider interpretation and a recently proposed RJMCMC-based Bayesian approach, trans-space RJMCMC, has been utilized. The proposed method provides an automatic model class selection mechanism for remote sensing images of SAR and US where the model class space consists of popular envelope distribution families. The proposed method estimates the correct distribution family, as well as the shape and the scale parameters, avoiding performing an exhaustive search. For the experimental analysis, different SAR images of urban, forest and agricultural scenes, and two different US images of a human heart have been used. Simulation results show the efficiency of the proposed method in finding statistical models for speckle.
Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization, which aims at temporally locating action instances in untrimmed videos using only video-level class labels during training, is an important yet challenging problem in video analysis. Many current methods adopt the “localization by classification” framework: first do video classification, then locate temporal area contributing to the results most. However, this framework fails to locate the entire action instances and gives little consideration to the local context. In this paper, we present a novel architecture called Cascaded Pyramid Mining Network (CPMN) to address these issues using two effective modules. First, to discover the entire temporal interval of specific action, we design a two-stage cascaded module with proposed Online Adversarial Erasing (OAE) mechanism, where new and complementary regions are mined through feeding the erased feature maps of discovered regions back to the system. Second, to exploit hierarchical contextual information in videos and reduce missing detections, we design a pyramid module which produces a scale-invariant attention map through combining the feature maps from different levels. Final, we aggregate the results of two modules to perform action localization via locating high score areas in temporal Class Activation Sequence (CAS). Extensive experiments conducted on THUMOS14 and ActivityNet-1.3 datasets demonstrate the effectiveness of our method.
Deep intrinsically motivated continuous actor-critic for efficient robotic visuomotor skill learning

Abstract In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is composed of a critic and an actor network. Both networks receive the hidden representation of a deep convolutional autoencoder which is trained to reconstruct the visual input, while the centre-most hidden representation is also optimized to estimate the state value. Separately, an ensemble of predictive world models generates, based on its learning progress, an intrinsic reward signal which is combined with the extrinsic reward to guide the exploration of the actor-critic learner. Our approach is more data-efficient and inherently more stable than the existing actor-critic methods for continuous control from pixel data. We evaluate our algorithm for the task of learning robotic reaching and grasping skills on a realistic physics simulator and on a humanoid robot. The results show that the control policies learned with our approach can achieve better performance than the compared state-of-the-art and baseline algorithms in both dense-reward and challenging sparse-reward settings.
Ion-Based Quantum Sensor for Optical Cavity Photon Numbers

We dispersively couple a single trapped ion to an optical cavity to extract information about the cavity photon-number distribution in a nondestructive way. The photon-number-dependent ac Stark shift experienced by the ion is measured via Ramsey spectroscopy. We use these measurements first to obtain the ion-cavity interaction strength. Next, we reconstruct the cavity photon-number distribution for coherent states and for a state with mixed thermal-coherent statistics, finding overlaps above 99% with the calibrated states.
Image denoising via overlapping group sparsity using orthogonal moments as similarity measure

Recently, sparse representation has attracted a great deal of interest in many of the image processing applications. However, the idea of self-similarity, which is inherently present in an image, has not been considered in standard sparse representation. Moreover, if the dictionary atoms are not constrained to be correlated, the redundancy present in the dictionary may not improve the performance of sparse coding. This paper addresses these issues by using orthogonal moments to extract the correlations among the atoms and group them together by extracting the characteristics of the noisy image patches. Most of the existing sparsity-based image denoising methods utilize an over-complete dictionary, for example, the K-SVD method that requires solving a minimization problem which is computationally challenging. In order to improve the computational efficiency and the correlation between the sparse coefficients, this paper employs the concept of overlapping group sparsity formulated for both convex and non-convex denoising frameworks. The optimization method used for solving the denoising framework is the well known majorization-minimization method, which has been applied successfully in sparse approximation and statistical estimations. Experimental results demonstrate that the proposed method offers, in general, a performance that is better than that of the existing state-of-the-art methods irrespective of the noise level and the image type.
A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-Based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing

The scene text interpretation is a critical part of the natural scene interpretation. Currently, most of the existing work is based on high-end graphics processing units (GPUs) implementation, which is commonly used on the server side. However, in Internet of Things (IoT) application scenarios, the communication overhead from the edge device to the server is quite large, which sometimes even dominates the total processing time. Hence, the edge-computing oriented design is needed to solve this problem. In this paper, we present an architectural design and implementation of a natural scene text interpretation (NSTI) accelerator, which can classify and localize the text region on pixel-level efficiently in real-time on mobile devices. To target the real-time and low-latency processing, the binary convolutional encoder-decoder network is adopted as the core architecture to enable massive parallelism due to its binary feature. Massively parallelized computations and a highly pipelined data flow control enhance its latency and throughput performance. In addition, all the binarized intermediate results and parameters are stored on chip to eliminate the power consumption and latency overhead of the off-chip communication. The NSTI accelerator is implemented in a 40 nm CMOS technology, which can process scene text images (size of 128 × 32) at 34 fps and latency of 40 ms for pixelwise interpretation with the pixelwise classification accuracy over 90% on ICDAR-03 and ICDAR-13 dataset. The real energy-efficiency is 698 GOP/s/W and the peak energy-efficiency can get up to 7825 GOP/s/W. The proposed accelerator is 7 times more energy efficient than its optimized GPU-based implementation counterpart, while maintaining a real-time throughput with latency of 40 ms.
New Negations on the Membership Functions of Type-2 Fuzzy Sets

Type-2 fuzzy sets (T2FSs) were introduced by L. A. Zadeh in 1975 as an extension of type-1 fuzzy sets (T1FSs). In this extension, the degree to which an element belongs to a set is just a label of the linguistic variable “TRUTH,” which allows to represent reality in a more appropriate way. On the other hand, negations play an essential role within fuzzy sets theory. In fact, they are necessary in order to obtain, for example, complements of fuzzy sets, dual of at-norm or a t-conorm, entropies, implications, as well as to study the possible contradictions appearing in a fuzzy system. However, meanwhile negations on [0, 1] (set of the membership degrees of a fuzzy set) have been deeply studied throughout the literature, the same has not happened with the negations on M = Map ([0, 1], [0, 1]), set of functions from [0, 1] to [0, 1] (and also set of the membership degrees of a T2FS), and so many aspects of the negations on M have not yet been investigated. In a previous paper, the axioms that an operation must satisfy to be considered a negation or a strong negation on a bounded partially ordered set were established. A family of strong negations on L and set of normal and convex functions of M were also presented. Moreover, let us note that the main characteristic of fuzzy systems is just the flexibility in order to be able to represent knowledge according to each situation, offering different models among which the expert can choose the one that best suits his/her criteria. Thus, it seems useful to find broad sets of negations in M and in L, which, as far as we know, have not been done by other researchers. According to these ideas, in this paper, the authors first present new negations and strong negations on L, and then show, for the first time, some negations on M with respect to each of the two partial orders defined in this set.
A spatiotemporal model of video quality assessment via 3D gradient differencing

Some of the video quality assessment (VQA) metrics assess the video quality using spatial and temporal features separately, however, the video distortions are highly related to spatial and temporal content simultaneously. The accuracy of VQA may be reduced by separated spatial and temporal aspects. The distortion of video is under the twin impacts of spatial and temporal distortions. 3D gradient is a spatiotemporal structural capturer to extract both spatial and temporal features at the same time. Hence, this paper proposed a novel VQA metric via spatiotemporal 3D gradient differencing to describe the degradation of video quality effectively. Firstly, 3D gradient is constructed to extract spatiotemporal feature of video sequences. And then the differencing of 3D gradients between the reference and the distorted video is used to predict perceptual local quality of frames. Sequence pooling strategy is applied to assemble the frame quality indices into the score of video quality. Experimental results show that the proposed metric has a good consistency with the subjective perception and performs better than traditional metrics.
A Gray Relational Analysis-Based Motion Detection Algorithm for Real-World Surveillance Sensor Deployment

The automated detection of moving objects is an essential task for any intelligent transportation system. To achieve reliable and accurate motion detection in video streams acquired from either jitter or static cameras in real-world scenarios, a novel motion detection approach based on gray relational analysis is proposed in this paper, which integrates a multi-sample background generation module and a moving object detection module. As our experimental results demonstrate, the proposed approach attains superior motion detection performance compared to other state-of-the-art techniques based on qualitative and quantitative evaluations. In addition, the processing speed of the proposed approach makes it suitable for real-time applications.
Introducing Heuristic Information into Ant Colony Optimization Algorithm for Identifying Epistasis

Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping the SNPs underlying complex diseases, there is still difficulty in detecting epistatic interactions due to the lack of heuristic information to expedite the search process. In this study, a method EACO is proposed to detect epistatic interactions based on the ant colony optimization (ACO) algorithm, the highlights of which are the introduced heuristic information, fitness function, and a candidate solutions filtration strategy. The heuristic information multi-SURF* is introduced into EACO for identifying epistasis, which is incorporated into ant-decision rules to guide the search with linear time. Two functionally complementary fitness functions, mutual information and the Gini index, are combined to effectively evaluate the associations between SNP combinations and the phenotype. Furthermore, a strategy for candidate solutions filtration is provided to adaptively retain all optimal solutions which yields a more accurate way for epistasis searching. Experiments of EACO, as well as three ACO based methods (AntEpiSeeker, MACOED, and epiACO) and four commonly used methods (BOOST, SNPRuler, TEAM, and epiMODE) are performed on both simulation data sets and a real data set of age-related macular degeneration. Results indicate that EACO is promising in identifying epistasis.
ShuffleDet: Real-Time Vehicle Detection Network in On-Board Embedded UAV Imagery

On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms. We propose a computationally inexpensive detection network for vehicle detection in UAV imagery which we call ShuffleDet. In order to enhance the speed-wise performance, we construct our method primarily using channel shuffling and grouped convolutions. We apply inception modules and deformable modules to consider the size and geometric shape of the vehicles. ShuffleDet is evaluated on CARPK and PUCPR+ datasets and compared against the state-of-the-art real-time object detection networks. ShuffleDet achieves 3.8 GFLOPs while it provides competitive performance on test sets of both datasets. We show that our algorithm achieves real-time performance by running at the speed of 14 frames per second on NVIDIA Jetson TX2 showing high potential for this method for real-time processing in UAVs.
Efficient and Secure Outsourcing of Large-Scale Linear System of Equations

Solving the large-scale linear system of equations is one of the most fundamental problems both in theory and practice. However this problem requires too much computational resource for most users to solve it. With the rapid development of cloud services, many users tend to outsource the expensive computing to the cloud server, which is regarded as an efficient way of solving such problem. Nevertheless, the cloud server can not protect the data privacy well, especially when the user's linear system of equations contain private and sensitive data. There are many previous research works on secure outsourcing of systems of linear equations. In this paper we first analyze a privacy preserving CGM (conjugate gradient method) algorithm for secure outsourcing of large-scale systems of linear equations proposed in [1] . We find that the cloud server can recover the protected coefficient matrix of the linear system of equations from the message it receives, which makes the security method in this scheme fails. This is a serious problem, which makes the private and sensitive data of the user leak to the cloud server, and privacy preserving does not exist. To overcome this problem, we modified this algorithm to protect the message from leaking, which can protect the users' privacy well. We also show the security of this new scheme and do experiments to show its efficiency.
Adversarial MACE Prediction After Acute Coronary Syndrome Using Electronic Health Records

Acute coronary syndrome (ACS), as an emergent and severe syndrome due to decreased blood flow in the coronary arteries, is a leading cause of death and serious long-term disability globally. ACS is usually caused by one of three problems: ST elevation myocardial infarction, non-ST elevation myocardial infarction, or unstable angina. Major adverse cardiac event (MACE) prediction, as a critical tool to estimate the likelihood an individual is at risk of ACS, has been widely adopted in the early prevention and intervention of ACS. Although valuable, existing MACE prediction models are designed to predict the overall probability of MACE occurrence for ACS patients, and lack the ability to look for insight into the disease to distinguish the different subtypes of ACS in a fine-grained manner. It is interesting to exploit the different subtypes of ACS and mine their private and shared underlying knowledge to improve the performance of MACE prediction. In this study, we propose utilizing a large volume of heterogeneous electronic health records for the application of MACE prediction. In detail, we address the multi-subtype-oriented MACE prediction for ACS as a multi-task learning (MTL) problem, present a MTL-based model to predict MACE of ACS patients with the different subtypes, and incorporate adversarial learning into the model to alleviate both the shared and private latent feature spaces of each subtype of ACS from interfering with each other. A real clinical dataset containing 2,863 ACS patient samples is collected from a Chinese hospital to validate the proposed model. Experimental results demonstrate that the prediction performance of our proposed model obtains a significant improvement, compared to single-subtype-oriented MACE prediction models.
A CPHD Approximation Based on a Discrete-Gamma Cardinality Model

The cardinalized probability hypothesis density (CPHD) filter has become one of the most acclaimed algorithms for multi-target Bayesian filtering due to its ability to accurately estimate the number of objects and the object states in tracking scenarios affected by clutter. The CPHD filter generalizes the probabilistic hypothesis density filter by jointly propagating the firstorder multi-target moment (intensity function) along with the entire probability distribution on the number of targets (cardinality distribution). In general, the CPHD recursion is computationally intractable, however successful approximations have been devised with reported computational complexity dominated by O(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations per filtering iteration, where m is the number of measurements. Room for improvement was originally acknowledged by Mahler, who conceived the idea of approximating the cardinality distribution by two-parameter distributions. In this paper, we further explore this idea to provide an efficient approximation of the CPHD filter where the cardinality distribution is modeled as a discretized Gamma distribution. Experiments show that the resulting filter is less computationally complex than the standard implementation of the CPHD filter but shows similar cardinality accuracy and variance.