Reviewerly/exHarmony huggingface dataset:
Silicon photonic quantum computing with spin qubits

Universal quantum computing holds the promise to fundamentally change today’s information-based society, yet a hardware platform that will provide a clear path to fault-tolerant quantum computing remains elusive. One recently proposed platform involves the use of circuit-bound photons to build cluster states and perform one-way measurement-based quantum computations on arrays of long-coherence-time solid-state spin qubits. Herein, we discuss the challenges that are faced during any practical implementation of this architecture by itemizing the key physical building blocks and the constraints imposed on the spin qubits and the photonic circuit components by the requirements of fault-tolerant performance. These considerations point to silicon as a leading candidate to host such a platform, and a roadmap for developing a silicon photonic circuit-based platform for measurement-based, fault-tolerant universal quantum computing is offered.
Teaching Yourself: A Self-Knowledge Distillation Approach to Action Recognition

Knowledge distillation, which is a process of transferring complex knowledge learned by a heavy network, i.e., a teacher, to a lightweight network, i.e., a student, has emerged as an effective technique for compressing neural networks. To reduce the necessity of training a large teacher network, this paper leverages the recent self-knowledge distillation approach to train a student network progressively by distilling its own knowledge without a pre-trained teacher network. Far from the existing self-knowledge distillation methods, which mainly focus on still images, our proposed Teaching Yourself is a self-knowledge distillation technique that targets at videos for human action recognition. Our proposed Teaching Yourself is not only designed as an effective lightweight network but also a high generalization capability model. In our approach, the network is able to update itself using the best past model, termed the preceding model, which is then utilized to guide the training process to update the present model. Inspired by consistency training in state-of-the-art semi-supervised learning methods, we also introduce an effective augmentation strategy to increase data diversity and improve network generalization and consistent predictions for our proposed Teaching Yourself approach. Our benchmark has been conducted on both the 3D Resnet-18 and 3D ResNet-50 backbone networks and evaluated on various standard datasets such as UCF101, HMDB51, and Kinetics400 datasets. The experimental results have shown that our teaching yourself method significantly improves the action recognition performance in terms of accuracy compared to existing supervised learning and knowledge distillation methods. We also have conducted an expensive ablation study to demonstrate that our approach mitigates overconfident predictions on dark knowledge and generates more consistent predictions in input variations of the same data point. The code is available at https://github.com/vdquang1991/Self-KD.
Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithm

This paper suggests a new hybrid algorithm by integrating two population-based algorithms: Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA), to solve complex nonlinear systems and unconstrained optimization problems. WOFPA denotes the suggested algorithm, a hybrid Whale Optimization Algorithm and Flower Pollination Algorithm. Nonlinear systems can be cast into unconstrained optimization problems, called merit functions, where the optimal solutions for the merit functions are equivalent to the solutions of nonlinear systems. WOFPA aims to decrease the execution time and the complexity of WOA and FPA. WOFPA has the advantages of WOA and FPA; WOFPA is a high-quality algorithm to solve both problems, nonlinear systems and unconstrained optimization problems. For example, FPA may have a premature convergence in the local optima, and WOFPA subdues the disadvantage of FPA. Numerical experiments of 14 benchmarks nonlinear systems and 30 CEC 2014 benchmarks unconstrained optimization functions with various dimensions are employed to test the performance of WOFPA. To have a further investigation for the performance of WOFPA, WOFPA is compared with WOA, FPA, and other existing algorithms from the literature. Two non-parametric statistical tests, Wilcoxon statistical test and the Friedman test, are conducted for this study to check the performance of the proposed algorithms and other compared algorithms and the significance of our results. The experiment results demonstrate that WOFPA performs better than other algorithms in the literature by getting the optimum solutions for most nonlinear systems and optimization problems and proves its efficiency compared with other existing algorithms.
Micro-Lens Image Stack Upsampling for Densely-Sampled Light Field Reconstruction

Due to hardware restriction, it is costly to capture densely-sampled Light Fields (LFs) with high angular and spatial resolution, which becomes the main bottleneck of LFs development. In this paper, we propose a learning-based framework to synthesize novel views and reconstruct densely-sampled LFs from sparsely-sampled LFs. In the proposed framework, micro-lens image stacks and view image stacks are separately grouped, in which details in novel views are explored from spatial and angular domain. The two kinds of stacks contain epipolar information and 3D convolution layers are employed to effectively extract features that include structure information. Moreover, an innovative way is proposed to synthesize views by upsampling micro-lens image stacks using deconvolution layers. The parameters in decovolution layers provide the view position information and different interpolation and extrapolation tasks can be explicitly modeled. It is validated that this view synthesis module can be embedded in different frameworks and improve the related performances. Without precise depth estimation and view warping, the proposed method is mainly designed for reconstructing LFs with small baselines. Related experimental results show that the proposed model outperforms other state-of-the-art methods in terms of both visual and numerical evaluations. Furthermore, the consistency between synthesized views and the intrinsic structure information is well preserved in the proposed method.
ADT-Det: Adaptive Dynamic Refined Single-Stage Transformer Detector for Arbitrary-Oriented Object Detection in Satellite Optical Imagery

The detection of arbitrary-oriented and multi-scale objects in satellite optical imagery is an important task in remote sensing and computer vision. Despite significant research efforts, such detection remains largely unsolved due to the diversity of patterns in orientation, scale, aspect ratio, and visual appearance; the dense distribution of objects; and extreme imbalances in categories. In this paper, we propose an adaptive dynamic refined single-stage transformer detector to address the aforementioned challenges, aiming to achieve high recall and speed. Our detector realizes rotated object detection with RetinaNet as the baseline. Firstly, we propose a feature pyramid transformer (FPT) to enhance feature extraction of the rotated object detection framework through a feature interaction mechanism. This is beneficial for the detection of objects with diverse patterns in terms of scale, aspect ratio, visual appearance, and dense distributions. Secondly, we design two special post-processing steps for rotated objects with arbitrary orientations, large aspect ratios and dense distributions. The output features of FPT are fed into post-processing steps. In the first step, it performs the preliminary regression of locations and angle anchors for the refinement step. In the refinement step, it performs adaptive feature refinement first and then gives the final object detection result precisely. The main architecture of the refinement step is dynamic feature refinement (DFR), which is proposed to adaptively adjust the feature map and reconstruct a new feature map for arbitrary-oriented object detection to alleviate the mismatches between rotated bounding boxes and axis-aligned receptive fields. Thirdly, the focus loss is adopted to deal with the category imbalance problem. Experiments on two challenging satellite optical imagery public datasets, DOTA and HRSC2016, demonstrate that the proposed ADT-Det detector achieves a state-of-the-art detection accuracy (79.95% mAP for DOTA and 93.47% mAP for HRSC2016) while running very fast (14.6 fps with a 600 × 600 input image size).
Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive Shielding

Reinforcement learning is a promising approach to synthesizing policies for challenging robotics tasks. A key problem is how to ensure safety of the learned policy-e.g., that a walking robot does not fall over or that an autonomous car does not run into an obstacle. We focus on the setting where the dynamics are known, and the goal is to ensure that a policy trained in simulation satisfies a given safety constraint. We propose an approach, called model predictive shielding (MPS), that switches on-the-fly between a learned policy and a backup policy to ensure safety. We prove that our approach guarantees safety, and empirically evaluate it on the cart-pole.
Finding the sweet spot for organizational control and team autonomy in large-scale agile software development

Abstract Agile methods and the related concepts of employee empowerment, self-management, and autonomy have reached large-scale software organizations and raise questions about commonly adopted principles for authority distribution. However, the optimum mechanism to balance the need for alignment, quality, and process control with the need or willingness of teams to be autonomous remains an unresolved issue. In this paper, we report our findings from a multiple-case study in two large-scale software development organizations in the telecom industry. We analysed the autonomy of the agile teams in the organizations using Hackman’s classification of unit authority and found that the teams were partly self-managing. Further, we found that alignment across teams can be achieved top-down by management and bottom-up through membership in communities or through dialogue between the team and management. However, the degree of team autonomy was limited by the need for organizational alignment. Top-down alignment and control were maintained through centralized decision-making for certain areas, the use of supervisory roles, mandatory processes, and checklists. One case employed a bottom-up approach to alignment through the formation of a community composed of all teams, experts, and supporting roles, but excluding managers. This community-based alignment involved teams in decision-making and engaged them in alignment initiatives. We conclude that implementation of such bottom-up structures seems to provide one possible mechanism for balancing organizational control and team autonomy in large-scale software development.
A knowledge representation model based on the geographic spatiotemporal process

Knowledge graphs (KGs) represent entities and relations as computable networks, which is of great value for discovering hidden knowledge and patterns. Geographic KGs mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic spatiotemporal processes. By analyzing the spatiotemporal features and evolution of geographic elements, this study presents the geographic evolutionary knowledge graph (GEKG). Its representation model has five core elements: time, geographic event (geo-event), geographic entity (geo-entity), activity and property, and defines six relations: logical, semantic, evolutionary and temporal relation, participation and inclusion. It establishes a hierarchical cubical model structure and each temporal layer extends vertically and horizontally starting with the earliest geo-event. Vertical expansion refers to the connection between different kinds of element, such as the participation relation between geo-entities and geo-events. Horizontal expansion indicates the association between the same kinds of element, such as the semantic relation between geo-entities. For different layers, the spatiotemporal differences of elements produce the evolutionary relation. Finally, the comparison of GEKG with Yet Another Great Ontology (YAGO) and Geographic Knowledge Graph (GeoKG) shows that GEKG has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons.
When Blockchain Meets the AEC Industry: Present Status, Benefits, Challenges, and Future Research Opportunities

Blockchain is regarded as a potential technology for transforming the architecture, engineering, and construction (AEC) industry, and the number of related publications is increasing rapidly. However, a systematic review of blockchain applications in the AEC industry is lacking. The objective of this study was to review the current status of blockchain applications via a bibliometric analysis combined with a systematic literature review. According to related articles collected from databases, the present status of blockchain was analysed with regard to the distribution of articles over publication years, journals, institutions, countries, cooperation networks between authors, keyword co-occurrence networks, and research methodologies. The results indicated that research on blockchain applications is still relatively new and fragmented with regard to several topics. Five areas of benefit were identified: (i) supply chain management, (ii) contract management, (iii) information management, (iv) stakeholder management, and (v) integration management. On the basis of the technology–organisation–environment framework, nine types of challenges were identified. Future research opportunities were proposed according to the research findings. This study contributes to the current body of knowledge and provides guidelines for further research on blockchain applications in the AEC industry.
Blockchain-Based Trust Edge Knowledge Inference of Multi-Robot Systems for Collaborative Tasks

Collaborative inference helps robots to complete large tasks with mutual collaboration in edge-assisted multi-robot systems. It is challenging to provide trusted edge collaborative inference in the presence of malicious nodes. In this article, we propose a blockchain-based collaborative edge knowledge inference (BCEI) framework for edge-assisted multi-robot systems. First, we formulate the inference process at the edge as the collaborative knowledge graph construction and sharing model. Second, to guarantee the trust of knowledge sharing, an efficient knowledge-based blockchain consensus method is presented. Finally, we conduct a case study on the emergency rescue application to evaluate the proposed framework. The experiment results demonstrate the efficiency of the proposed framework in terms of latency and accuracy.
Pairwise symmetry reasoning for multi-agent path finding search

Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents. In this work, we show that one of the reasons why MAPF is so hard to solve is due to a phenomenon called pairwise symmetry, which occurs when two agents have many different paths to their target locations, all of which appear promising, but every combination of them results in a collision. We identify several classes of pairwise symmetries and show that each one arises commonly in practice and can produce an exponential explosion in the space of possible collision resolutions, leading to unacceptable runtimes for current state-of-the-art (bounded-sub)optimal MAPF algorithms. We propose a variety of reasoning techniques that detect the symmetries efficiently as they arise and resolve them by using specialized constraints to eliminate all permutations of pairwise colliding paths in a single branching step. We implement these ideas in the context of a leading optimal MAPF algorithm CBS and show that the addition of the symmetry reasoning techniques can have a dramatic positive effect on its performance — we report a reduction in the number of node expansions by up to four orders of magnitude and an increase in scalability by up to thirty times. These gains allow us to solve to optimality a variety of challenging MAPF instances previously considered out of reach for CBS.
Measuring geometric imperfections of variable–angle filament–wound cylinders with a simple digital image correlation setup

Measuring the geometric imperfections in cylindrical shells is a critical step necessary to create accurate numerical models that can capture the imperfection-sensitive behavior of these structures. Modern composite structures, such as variable–angle filament–wound (VAFW) cylinders, have a unique imperfection signature that is still unknown to the scientific community. This new class of variable–stiffness structures developed by our research group combines wide tailoring capabilities with the efficient manufacturability enabled by filament winding process. The present study proposes a novel imperfection measurement method that is simple and applicable to both small and large structures. The topographic data is measured with only a pair of cameras. Practical aspects of using digital image correlation (DIC) are described and discussed in detail, such as lighting, focus adjustment, and calibration. State–of–the–art best–fit routines, based on least–squares optimization, are used to transform raw data into a common coordinate system. Finally, the transformed data is stitched to build a full 3D imperfection pattern that can be readily used in a nonlinear finite element analysis. The developed method is used to measure the imperfections of 12 VAFW cylinders. The mass of the cylinders is used to validate the geometric imperfections and evaluate the variability of the proposed methodology.
A Guided Edge-Aware Smoothing-Sharpening Filter Based on Patch Interpolation Model and Generalized Gamma Distribution

Smoothing and sharpening are two fundamental image processing operations. The latter is usually related to the former through the unsharp masking algorithm. In this paper, we develop a new type of filter which performs smoothing or sharpening via a tuning parameter. The development of the new filter is based on (1) a new Laplacian-based filter formulation which unifies the smoothing and sharpening operations, (2) a patch interpolation model similar to that used in the guided filter which provides edge-awareness capability, and (3) the generalized Gamma distribution which is used as the prior for parameter estimation. We have conducted detailed studies on the properties of two versions of the proposed filter (self-guidance and external guidance). We have also conducted experiments to demonstrate applications of the proposed filter. In the self-guidance case, we have developed adaptive smoothing and sharpening algorithms based on texture, depth and blurriness information extracted from an image. Applications include enhancing human face images, producing shallow depth of field effects, focus-based image enhancement, and seam carving. In the external guidance case, we have developed new algorithms for combining flash and no-flash images and for enhancing multi-spectral images using a panchromatic image.
REAL: Rapid Exploration with Active Loop-Closing toward Large-Scale 3D Mapping using UAVs

Exploring an unknown environment without colliding with obstacles is one of the essentials of autonomous vehicles to perform diverse missions such as structural inspections, rescues, deliveries, and so forth. Therefore, unmanned aerial vehicles (UAVS), which are fast, agile, and have high degrees of freedom, have been widely used. However, previous approaches have two limitations: a) First, they may not be appropriate for exploring large-scale environments because they mainly depend on random sampling-based path planning that causes unnecessary movements. b) Second, they assume the pose estimation is accurate enough, which is the most critical factor in obtaining an accurate map. In this paper, to explore and map unknown large-scale environments rapidly and accurately, we propose a novel exploration method that combines the pre-calculated Peacock Trajectory with graph-based global exploration and active loop-closing. Because the two-step trajectory that considers the kinodynamics of UAVs is used, obstacle avoidance is guaranteed in the receding-horizon manner. In addition, local exploration that considers the frontier and global exploration based on the graph maximizes the speed of exploration by minimizing unnecessary revisiting. In addition, by actively closing the loop based on the likelihood, pose estimation performance is improved. The proposed method’s performance is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.
StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing

From facial recognition to autonomous driving, Artificial Intelligence (AI) will transform the way we live and work over the next couple of decades. Existing AI approaches for urban computing suffer from various challenges, including dealing with synchronization and processing of vast amount of data generated from the edge devices, as well as the privacy and security of individual users, including their bio-metrics, locations, and itineraries. Traditional centralized-based approaches require data in each organization be uploaded to the central database, which may be prohibited by data protection acts, such as GDPR and CCPA. To decouple model training from the need to store the data in the cloud, a new training paradigm called Federated Learning (FL) is proposed. FL enables multiple devices to collaboratively learn a shared model while keeping the training data on devices locally, which can significantly mitigate privacy leakage risk. However, under urban computing scenarios, data are often communication-heavy, high-frequent, and asynchronized, posing new challenges to FL implementation. To handle these challenges, we propose a new hybrid federated learning architecture called StarFL. By combining with Trusted Execution Environment (TEE), Secure Multi-Party Computation (MPC), and (Beidou) satellites, StarFL enables safe key distribution, encryption, and decryption, and provides a verification mechanism for each participant to ensure the security of the local data. In addition, StarFL can provide accurate timestamp matching to facilitate synchronization of multiple clients. All these improvements make StarFL more applicable to the security-sensitive scenarios for the next generation of urban computing.