Reviewerly/exHarmony huggingface dataset:
FedCav: Contribution-aware Model Aggregation on Distributed Heterogeneous Data in Federated Learning

The emerging federated learning (FL) paradigm allows multiple distributed devices to cooperatively train models in parallel with the raw data retained locally. The local-computed parameters will be transferred to a centralized server for aggregation. However, the vanilla aggregation method ignores the heterogeneity of the distributed data, which may lead to slow convergence and low training efficiency. Yet, existing data scheduling and improved aggregation methods either incur privacy concerns or fail to consider the fine-grained heterogeneity. We propose FedCav, a contribution-aware model aggregation algorithm that differentiates the merit of local updates and explicitly favors the model-informed contributions. The intuition is that the local data showing higher inference loss is likely to facilitate better performance improvement. To this end, we design a novel global loss function with explicit optimization preference on informative local updates, theoretically prove its convex property, and use it to regulate the gradient descent process iteratively. Additionally, we propose to identify abnormal updates with fake loss by auditing historic local training statistics. The results of extensive experiments demonstrate that FedCav needs fewer training rounds (~34%) for convergence and achieves better inference accuracy (~2.4%) than the baselines (i.e., FedAvg and FedProx). We also observe that FedCav can actively mitigate the model replacement attacks with agile recovery capability towards the aggregation.
Learning domain transfer for unsupervised magnetic resonance imaging restoration and edge enhancement

Abstract Recently, many effective medical image restoration methods based on deep learning have been proposed. Most of the methods are used to solve single image processing tasks, such as image noise removal, image deblurring, and image super‐resolution. However, real medical images often suffer from multiple degradation factors, such as signal interference in the process of shooting or the relative movement of patients during the process of image acquisition. The image restoration methods only considering a single image degradation factor often fail to yield satisfactory results for the restoration purpose of practical medical images. It is difficult to obtain paired medical images to incorporate real image processing tasks into the framework of supervised learning. For the restoration of real medical images with multiple degradation factors, an unsupervised learning method based on domain transfer to resolve the MRI restoration problem is proposed. Specifically, learning invariant representations from a degraded image and a transferred image via adversarial domain adaption and then extra self‐supervised modules to further improve the robustness of representations are used. In addition, the Laplacian operator is embedded in the encoder to extract additional edges, which can form additional constraints on edge recovery. Experimental results show that the proposed method is effective in both image restoration and edge preservation.
How squeezed states both maximize and minimize the same notion of quantumness

Beam splitters are routinely used for generating entanglement between modes in the optical and microwave domains, requiring input states that are not convex combinations of coherent states. This leads to the ability to generate entanglement at a beam splitter as a notion of quantumness. A similar, yet distinct, notion of quantumness is the amount of entanglement generated by two-mode squeezers (i.e., four-wave mixers). We show that squeezed-vacuum states, paradoxically, both minimize and maximize these notions of quantumness, with the crucial resolution of the paradox hinging upon the relative phases between the input states and the devices. Our notion of quantumness is intrinsically related to eigenvalue equations involving creation and annihilation operators, governed by a set of inequalities that leads to generalized cat and squeezed-vacuum states.
Metrology for the digital age

Based on digital technologies, big data, artificial intelligence and machine-readable information, the digital transformation rapidly changes society, industries, and economies. Metrology as a central element of international trade, for confidence in measurements and part of the quality infrastructure is facing several challenges and opportunities in these developments. In this contribution we discuss some of the key challenges and a potential future role of metrology in the digital age. We address metrological principles for confidence in data and Algorithms, cyber-physical systems, FAIR data and metrology, and the role of metrology in the digital transformation in the quality infrastructure.
The relative contributions of facial parts qualities to the face image utility

Face image quality assessment predicts the utility of a face image for automated face recognition. A high-quality face image can achieve good performance for the identification or verification task. Some recent face image quality assessment algorithms are established on deep-learning-based approaches, which rely on face embeddings of aligned face images. Such face embeddings fuse complex information into a single feature vector and are, therefore, challenging to disentangle. The semantic context however can provide better interpretable insights into neural-network decisions. We investigate the effects of face subregions (semantic contexts) and link the general image quality of face subregions with face image utility. The evaluation is performed on two difficult large-scale datasets (LFW and VGGFace2) with three face recognition solutions (FaceNet, SphereFace, and ArcFace). In total, we applied four face image quality assessment methods and one general image quality assessment method on four face subregions (eyes, mouth, nose, and tightly cropped face region) and the aligned faces. In addition, the effect of fusion of different face subregions was investigated to increase the robustness of the outcomes.
Hierarchical Reasoning Network for Human-Object Interaction Detection

Human-object interaction detection that aims at detecting <human, verb, object> triplets is critical for the holistic human-centric scene understanding. Existing approaches ignore the modeling of correlations among hierarchical human parts and objects. In this work, we introduce a Hierarchical Reasoning Network (HRNet) to capture relations among human parts at multiple scales (including the holistic human, human region, and human keypoint levels) and objects via a unified graph. In particular, HRNet first constructs one multi-level human parts graph, each level of which consists of human parts at one specific scale, objects, and the unions of human part-object pairs as nodes, and their mutual visual and spatial layout relations as intra-level reasoning. To also capture the relations across scales, we further introduce inter-level reasoning between the nodes of two consecutive levels based on the prior of human body structure. The representations of graph nodes are propagated along intra-level and inter-level reasoning in turn during reasoning. Extensive experiments demonstrate our HRNet obtains new state-of-the-art results on three challenging HICO-DET, V-COCO and HOI-A benchmarks, validating the compelling effectiveness of the proposed method.
Can we trust tests to automate dependency updates? A case study of Java Projects

Developers are increasingly using services such as Dependabot to automate dependency updates. However, recent research has shown that developers perceive such services as unreliable, as they heavily rely on test coverage to detect conflicts in updates. To understand the prevalence of tests exercising dependencies, we calculate the test coverage of direct and indirect uses of dependencies in 521 well-tested Java projects. We find that tests only cover 58% of direct and 20% of transitive dependency calls. By creating 1,122,420 artificial updates with simple faults covering all dependency usages in 262 projects, we measure the effectiveness of test suites in detecting semantic faults in dependencies; we find that tests can only detect 47% of direct and 35% of indirect artificial faults on average. To increase reliability, we investigate the use of change impact analysis as a means of reducing false negatives; on average, our tool can uncover 74% of injected faults in direct dependencies and 64% for transitive dependencies, nearly two times more than test suites. We then apply our tool in 22 real-world dependency updates, where it identifies three semantically conflicting cases and five cases of unused dependencies. Our findings indicate that the combination of static and dynamic analysis should be a requirement for future dependency updating systems.
Robot path planning based on genetic algorithm with hybrid initialization method

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.
Privacy during Pandemic: A Global View of Privacy Practices around COVID-19 Apps

A large number of mobile phone applications have been built and deployed to combat COVID-19, offering various services to users, including virus information, contact tracing, and symptom monitoring among others. At the same time, the privacy and security vulnerabilities of user data over these apps have become a big concern in many places. To examine this issue, we conducted a mixed-method study with a combined approach of app analysis and an online survey to understand the privacy vulnerabilities of such apps and get an overview of user perceptions around this issue. In addition, we considered the notion of privacy in two different socio-economic contexts (Global North and Global South) to specify similarities and differences in app-specific privacy functionalities (data practices, functional requirements, regulations, etc.) and identify factors that impacted users' decision to use such apps (such as trust, preferences, concerns, motivations, etc.). Thus, this paper presents two diverse sets of opinions from these two geographic regions (including 27 countries), which provide a broader understanding of how the privacy concerns around COVID-19 are connected to various economic, political, and social factors. Furthermore, our analysis of 39 apps provides a deep insight into what many COVID-19 apps are lacking to ensure proper privacy practices and how those issues are entangled with various contextual challenges.
TravelNet: Self-supervised Physically Plausible Hand Motion Learning from Monocular Color Images

This paper aims to reconstruct physically plausible hand motion from monocular color images. Existing frame-by-frame estimating approaches can not guarantee the physical plausibility (e.g. penetration, jittering) directly. In this paper, we embed physical constraints on the per-frame estimated motions in both spatial and temporal space. Our key idea is to adopt a self-supervised learning strategy to train a novel encoder-decoder, named TravelNet, whose training motion data is prepared by the physics engine using discrete pose states. TravelNet captures key pose states from hand motion sequences as compact motion descriptors, inspired by the concept of keyframes in animation. Finally, it manages to extract those key states out of perturbations without manual annotations, and reconstruct the motions preserving details and physical plausibility. In the experiments, we show that the outputs of the TravelNet contain both finger synergism and time consistency. Through the proposed framework, hand motions can be accurately reconstructed and flexibly re-edited, which is superior to the state-of-the-art methods.
Analysis of dynamics in chaotic neural network reservoirs: Time-series prediction tasks

A chaotic neural network reservoir (CNNR), in which a chaotic neural network model is used as a reservoir layer, efficiently introduces chaotic dynamics without violating the echo state property criterion. However, it is unclear which aspect of the complex reservoir dynamics contributes to the performance of CNNR. In this study, we analyze the dynamics of CNNR, which is used to engage time-series prediction tasks, and focus particularly on the design and evaluation of CNNR hardware. First, the memory capacity of CNNR is estimated using the NARMA-30 time series. Second, we analyze the dynamics of CNNR using hardware compatible indices such as the Lyapunov spectrum, permutation entropy, spatial mutual information, average firing rate, and Kullback-Leibler divergence. We show that any one of these indices alone is not enough to reveal the dynamics in CNNR. Therefore, we combined some of these indices to evaluate the dynamics further. The relationship between the prediction performance of CNNR and combined indices is identified. We finally introduce a complexity entropy causality plane to decipher the dynamics of CNNR in detail. As a result, we provide useful qualitative methods to evaluate and design the dynamics of CNNR hardware for time-series prediction tasks.
Determination of the Optimal Number of Clusters: A Fuzzy-Set Based Method

The optimal number of clusters ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> ) is one of the determinants of clustering efficiency. In this article, we present a new method of quantifying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> for centroid-based clustering. First, we propose a new clustering validity index named fRisk( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> ) based on the fuzzy set theory. It takes the role of normalization and accumulation of local risks coming from each action either splitting data from a cluster or merging data into a cluster. fRisk( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> ) exploits the local distribution information of the database to catch the global information of the clustering process in the form of the risk degree. Based on the monotonous reduction property of fRisk( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> ), which is proved theoretically, we present a fRisk-based new algorithm named fRisk4-bA for determining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> . In the algorithm, the well-known L-method is employed as a supplemented tool to catch <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> on the graph of the fRisk( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> ). Along with the stable convergence trend of the method to be proved theoretically, numerical surveys are also carried out. The surveys show that the high reliability and stability, as well as the sensitivity in separating/merging clusters in high-density areas, even if the presence of noise in the databases, are the strong points of the proposed method.
Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks

Computer vision on low-power edge devices enables applications including search-and-rescue and security. State-of-the-art computer vision algorithms, such as Deep Neural Networks (DNNs), are too large for inference on low-power edge devices. To improve efficiency, some existing approaches parallelize DNN inference across multiple edge devices. How-ever, these techniques introduce significant communication and synchronization overheads or are unable to balance workloads across devices. This paper demonstrates that the hierarchical DNN architecture is well suited for parallel processing on multiple edge devices. We design a novel method that creates a parallel inference pipeline for computer vision problems that use hierarchical DNNs. The method balances loads across the collaborating devices and reduces communication costs to facilitate the processing of multiple video frames simultaneously with higher throughput. Our experiments consider a representative computer vision problem where image recognition is performed on each video frame, running on multiple Raspberry Pi 4Bs. With four collaborating low-power edge devices, our approach achieves 3.21× higher throughput, 68% less energy consumption per device per frame, and a 58% decrease in memory when compared with existing sinaledevice hierarchical DNNs.
Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation

Mitochondria segmentation from electron microscopy images has seen great progress, especially for learning-based methods. However, since the learning of model requires massive annotations, it is time and labour expensive to learn a specific model for each acquired dataset. On the other hand, it is challenging to generalize a learned model to datasets of unknown species or those acquired by unknown devices, mainly due to the difference of data distributions. In this paper, we study unsupervised domain adaptation to enhance the generalization capacity, where no annotation for target datasets is required. We start from an effective solution, which learns the target data distribution with pseudo labels predicted by a source-domain model. However, the obtained pseudo labels are usually noisy due to the domain gap. To address this issue, we propose an uncertainty-aware model to rectify noisy labels. Specifically, we insert Monte-Carlo dropout layers to a UNet backbone, where the uncertainty is measured by the standard deviation of predictions. Experiments on MitoEM and FAFB datasets demonstrate the superior performance of proposed model, in terms of the adaptations between different species and acquisition devices.
Quantum optimal control for pure-state preparation using one initial state

This paper presents a framework for solving the pure-state preparation problem using numerical optimal control. As an example, we consider the case where a number of qubits are dispersively coupled to a readout cavity. We model open system quantum dynamics using the Markovian Lindblad master equation driven by external control pulses. The main result of this paper develops a basis of density matrices (a parameterization) where each basis element is a density matrix by itself. Utilizing a specific objective function, we show how an ensemble of the basis elements can be used as a single initial state throughout the optimization process—independent of the system dimension. We apply the general framework to the specific application of ground-state reset of one and two qubits coupled to a readout cavity.
Cognitive multi-modal consistent hashing with flexible semantic transformation

Multi-modal hashing can encode the large-scale social geo-media multimedia data from multiple sources into a common discrete hash space, in which the heterogeneous correlations from multiple modalities could be well explored and preserved into the objective semantic-consistent hash codes. The current researches on multi-modal hashing mainly focus on performing common data reconstruction, but they fail to effectively distill the intrinsic and consensus structures of multi-modal data and fully exploit the inherent semantic knowledge to capture semantic-consistent information across multiple modalities, leading to unsatisfactory retrieval performance. To facilitate this problem and develop an efficient multi-modal geographical retrieval method, in this article, we propose a discriminative multi-modal hashing framework named Cognitive Multi-modal Consistent Hashing (CMCH), which can progressively pursue the structure consensus over heterogeneous multi-modal data and simultaneously explore the informative transformed semantics. Specifically, we construct a parameter-free collaborative multi-modal fusion module to incorporate and excavate the underlying common components from multi-source data. Particularly, our formulation seeks for a joint multi-modal compatibility among multiple modalities under a self-adaptive weighting manner, which can take full advantages of their complementary properties. Moreover, a cognitive self-paced learning policy is further leveraged to conduct progressive feature aggregation, which can coalesce multi-modal data onto the established common latent space in a curriculum learning mode. Furthermore, deep semantic transform learning is elaborated to generate flexible semantics for interactively guiding collaborative hash codes learning. An efficient discrete learning algorithm is devised to address the resulting optimization problem, which obtains stable solutions when dealing with large-scale multi-modal retrieval tasks. Sufficient experiments performed on four large-scale multi-modal datasets demonstrate the encouraging performance of the proposed CMCH method in comparison with the state-of-the-arts over multi-modal information retrieval and computational efficiency. The source codes of this work could be available at https://github.com/JunfengAn1998a/CMCH .