Reviewerly/exHarmony huggingface dataset:
Meta Batch-Instance Normalization for Generalizable Person Re-Identification

Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce style variations, but the loss of discriminative information could not be avoided. In this paper, we propose a novel generalizable Re-ID framework, named Meta Batch-Instance Normalization (MetaBIN). Our main idea is to generalize normalization layers by simulating unsuccessful generalization scenarios beforehand in the meta-learning pipeline. To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers. Moreover, we diversify the virtual simulations via our meta-train loss accompanied by a cyclic inner-updating manner to boost generalization capability. After all, the MetaBIN framework prevents our model from overfitting to the given source styles and improves the generalization capability to unseen domains without additional data augmentation or complicated network design. Extensive experimental results show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark and the cross-domain Re-ID problem. The source code is available at: https://github.com/bismex/MetaBIN.
Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges

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SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable, end-to-end method for VOS called Sparse Spatiotemporal Transformers (SST). SST extracts per-pixel representations for each object in a video using sparse attention over spatiotemporal features. Our attention-based formulation for VOS allows a model to learn to attend over a history of multiple frames and provides suitable inductive bias for performing correspondence-like computations necessary for solving motion segmentation. We demonstrate the effectiveness of attention-based over recurrent networks in the spatiotemporal domain. Our method achieves competitive results on YouTube-VOS and DAVIS 2017 with improved scalability and robustness to occlusions compared with the state of the art. Code is available at https://github.com/dukebw/SSTVOS.
Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection

Recently proposed decoupled training methods emerge as a dominant paradigm for long-tailed object detection. But they require an extra fine-tuning stage, and the dis-jointed optimization of representation and classifier might lead to suboptimal results. However, end-to-end training methods, like equalization loss (EQL), still perform worse than decoupled training methods. In this paper, we re-veal the main issue in long-tailed object detection is the imbalanced gradients between positives and negatives, and find that EQL does not solve it well. To address the problem of imbalanced gradients, we introduce a new version of equalization loss, called equalization loss v2 (EQL v2), a novel gradient guided reweighing mechanism that re-balances the training process for each category independently and equally. Extensive experiments are performed on the challenging LVIS benchmark. EQL v2 outperforms origin EQL by about 4 points overall AP with 14 ∼ 18 points improvements on the rare categories. More importantly, it also surpasses decoupled training methods. With-out further tuning for the Open Images dataset, EQL v2 improves EQL by 7.3 points AP, showing strong generalization ability. Codes have been released at https://github.com/tztztztztz/eqlv2
Attention induced multi-head convolutional neural network for human activity recognition

Deep neural networks, including convolutional neural networks (CNNs), have been widely adopted for human activity recognition in recent years. They have attained significant performance improvement over traditional techniques due to their strong feature representation capabilities. Some of the challenges faced by the HAR community is the non-availability of a substantial amount of labeled training samples, and the higher computational cost and system resources requirements of deep learning architectures as opposed to shallow learning algorithms. To address these challenges, we propose an attention-based multi-head model for human activity recognition (HAR). This framework contains three lightweight convolutional heads, with each head designed using one-dimensional CNN to extract features from sensory data. The lightweight multi-head model is induced with attention to strengthen the representation ability of CNN, allowing for automatic selection of salient features and suppress unimportant ones. We conducted ablation studies and experiments on two publicly available benchmark datasets: WISDM and UCI HAR, to evaluate our model. The experimental outcome demonstrates the effectiveness of the proposed framework in activity recognition and achieves better accuracy while ensuring computational efficiency.
ClusterFL

Federated Learning (FL) has recently received significant interests thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications since they are oblivious to the intrinsic relationship between data of different users. We propose ClusterFL, a similarity-aware federated learning system that can provide high model accuracy and low communication overhead for HAR applications. ClusterFL features a novel clustered multi-task federated learning framework that maximizes the training accuracy of multiple learned models while automatically capturing the intrinsic clustering relationship among the data of different nodes. Based on the learned cluster relationship, ClusterFL can efficiently drop out the nodes that converge slower or have little correlation with other nodes in each cluster, significantly speeding up the convergence while maintaining the accuracy performance. We evaluate the performance of ClusterFL on an NVIDIA edge testbed using four new HAR datasets collected from total 145 users. The results show that, ClusterFL outperforms several state-of-the-art FL paradigms in terms of overall accuracy, and save more than 50% communication overhead at the expense of negligible accuracy degradation.
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. In order to overcome this challenge, we propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc. With the motivation of circumventing this challenge, we propose a cross-site modeling for MR image reconstruction in which the learned intermediate latent features among different source sites are aligned with the distribution of the latent features at the target site. Extensive experiments are conducted to provide various insights about FL for MR image reconstruction. Experimental results demonstrate that the proposed framework is a promising direction to utilize multi-institutional data without compromising patients' privacy for achieving improved MR image reconstruction. Our code is available at https://github.com/guopengf/FL-MRCM.
Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability

Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically creates an explanation for a single prediction by any ML model by learning a simpler interpretable model (e.g., linear classifier) around the prediction through generating simulated data around the instance by random perturbation, and obtaining feature importance through applying some form of feature selection. While LIME and similar local algorithms have gained popularity due to their simplicity, the random perturbation methods result in shifts in data and instability in the generated explanations, where for the same prediction, different explanations can be generated. These are critical issues that can prevent deployment of LIME in sensitive domains. We propose a deterministic version of LIME. Instead of random perturbation, we utilize Agglomerative Hierarchical Clustering (AHC) to group the training data together and K-Nearest Neighbour (KNN) to select the relevant cluster of the new instance that is being explained. After finding the relevant cluster, a simple model (i.e., linear model or decision tree) is trained over the selected cluster to generate the explanations. Experimental results on six public (three binary and three multi-class) and six synthetic datasets show the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME), where we quantitatively determine the stability and faithfulness of DLIME compared to LIME.
A comprehensive framework based on GIS-AHP for the installation of solar PV farms in Kahramanmaraş, Turkey

Solar photovoltaic (PV) technologies receive investment, support, and incentives despite their apparent high costs. In terms of renewable energy policies and spatial planning, site selection for solar PV farms is a critical issue. A novel comprehensive framework for assessing the site suitability of solar PV farms is proposed, which considers the preservation of natural, ecological conservation, and cultural areas. The framework combines a Geographical Information System (GIS) with layers of satellite-derived data for energy resources as well as locally collected data, and the Multi Criteria Decision Making (MCDM) method based on the Analytic Hierarchy Process (AHP). In the GIS environment, maps belonging to fourteen sub-criteria of the three main criteria (Geography, Climate and Location) for Kahramanmaraş, Turkey were obtained, as well as suitability map derived from their weighted sum. The AHP method generates the appropriate weight for each input criteria using a pairwise comparison matrix. GHI, aspect, distance from power line network, land use/cover, annual average temperature, and others were found as the most necessary sub-criteria, respectively. According to the findings, 9.62% of study area very low, 20.15% is low, 22.51% is moderate, 23.98% is high and 23.74% is very high, and 26% is unsuitable for solar PV farms. Based on the results, the northern districts of the study region were determined to be the most suitable for the construction of solar PV farms. When existing five PV farms' location selection decisions were examined, it was observed that the investment outcomes were consistent with the results of the study. The proposed framework is projected to minimize the cost, time, and resources used on the construction of solar PV farms.
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabled target domain. Recently, adversarial domain adaptation with two distinct classifiers (biclassifier) has been introduced into UDA which is effective to align distributions between different domains. Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers. However, the similarity of the outputs cannot guarantee the accuracy of target samples, i.e., traget samples may match to wrong categories even if the discrepancy between two classifiers is small. To challenge this issue, in this paper, we propose a cross-domain gradient discrepancy minimization (CGDM) method which explicitly minimizes the discrepancy of gradients generated by source samples and target samples. Specifically, the gradient gives a cue for the semantic information of target samples so it can be used as a good supervision to improve the accuracy of target samples. In order to compute the gradient signal of target smaples, we further obtain target pseudo labels through a clustering-based self-supervised learning. Extensive experiments on three widely used UDA datasets show that our method surpasses many previous state-of-the-arts.
Deterministic multi-mode gates on a scalable photonic quantum computing platform

Quantum computing can be realized with numerous different hardware platforms and computational protocols. A highly promising approach to foster scalability is to apply a photonic platform combined with a measurement-induced quantum information processing protocol where gate operations are realized through optical measurements on a multipartite entangled quantum state -- a so-called cluster state. Heretofore, a few quantum gates on non-universal or non-scalable cluster states have been, but a full set of gates for universal scalable quantum computing has not been realized. We propose and demonstrate the deterministic implementation of a multi-mode set of measurement-induced quantum gates in a large two-dimensional (2D) optical cluster state using phase-controlled continuous variable quadrature measurements. Each gate is simply programmed into the phases of the high-efficiency quadrature measurements which execute the transformations by teleportation through the cluster state. Using these programmable gates, we demonstrate a small quantum circuit consisting of 10 single-mode gates and 2 two-mode gates on a three-mode input state. On this platform, fault-tolerant universal quantum computing is possible if the cluster state entanglement is improved and a supply of Gottesman-Kitaev-Preskill qubits is available. Moreover, it operates at the telecom wavelength and is therefore network connectable without quantum transducers.
Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences

We address the problem of visible-infrared person re-identification (VI-reID), that is, retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal setting. Two main challenges in VI-reID are intraclass variations across person images, and cross-modal discrepancies between visible and infrared images. Assuming that the person images are roughly aligned, previous approaches attempt to learn coarse image- or rigid part-level person representations that are discriminative and generalizable across different modalities. However, the person images, typically cropped by off-the-shelf object detectors, are not necessarily well-aligned, which distract discriminative person representation learning. In this paper, we introduce a novel feature learning framework that addresses these problems in a unified way. To this end, we propose to exploit dense correspondences between cross-modal person images. This allows to address the cross-modal discrepancies in a pixel-level, suppressing modality-related features from person representations more effectively. This also encourages pixel-wise associations between cross-modal local features, further facilitating discriminative feature learning for VI-reID. Extensive experiments and analyses on standard VI-reID benchmarks demonstrate the effectiveness of our approach, which significantly outperforms the state of the art.
Blockchain in food supply chains: a literature review and synthesis analysis of platforms, benefits and challenges

The globalisation of the food industry increases the complexity and the difficulty in enhancing efficiency and solving issues in food supply chains. Blockchain is a promising decentralised information technology that could benefit food supply chains by reducing transaction cost and time, increasing process transparency, security, and efficiency, as well as building trust among participants. In this paper, we introduce the major blockchain platforms currently used in food supply chains and conduct a synthesis analysis to explore the benefits and challenges of blockchain technology in the food industry. We demonstrate that blockchain enables unprecedented visibility at each step of the food supply chain, helps increase transaction transparency, food safety, and quality, and also reduces food fraud and waste. Furthermore, it serves as a digital solution for reducing operational costs and improving efficiency in food supply chains.
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

We present a flexible and high-performance framework, named Pyramid R-CNN, for two-stage 3D object detection from point clouds. Current approaches generally rely on the points or voxels of interest for RoI feature extraction on the second stage, but cannot effectively handle the sparsity and non-uniform distribution of those points, and this may result in failures in detecting objects that are far away. To resolve the problems, we propose a novel second-stage module, named pyramid RoI head, to adaptively learn the features from the sparse points of interest. The pyramid RoI head consists of three key components. Firstly, we propose the RoI-grid Pyramid, which mitigates the sparsity problem by extensively collecting points of interest for each RoI in a pyramid manner. Secondly, we propose RoI-grid Attention, a new operation that can encode richer information from sparse points by incorporating conventional attention-based and graph-based point operators into a unified formulation. Thirdly, we propose the Density-Aware Radius Prediction (DARP) module, which can adapt to different point density levels by dynamically adjusting the focusing range of RoIs. Combining the three components, our pyramid RoI head is robust to the sparse and imbalanced circumstances, and can be applied upon various 3D backbones to consistently boost the detection performance. Extensive experiments show that Pyramid R-CNN outperforms the state-of-the-art 3D detection models by a large margin on both the KITTI dataset and the Waymo Open dataset.
High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation

Rooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km2 of global land surface area to demarcate 0.2 million km2 of rooftop area, which together represent 27 PWh yr-1 of electricity generation potential for costs between 40-280 $ MWh-1. Out of this, 10 PWh yr-1 can be realised below 100 $ MWh-1. The global potential is predominantly spread between Asia (47%), North America (20%) and Europe (13%). The cost of attaining the potential is lowest in India (66 $ MWh-1) and China (68 $ MWh-1), with USA (238 $ MWh-1) and UK (251 $ MWh-1) representing some of the costliest countries.
Neural Radiance Flow for 4D View Synthesis and Video Processing

We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images. Key to our approach is the use of a neural implicit representation that learns to capture the 3D occupancy, radiance, and dynamics of the scene. By enforcing consistency across different modalities, our representation enables multi-view rendering in diverse dynamic scenes, including water pouring, robotic interaction, and real images, outperforming state-of-the-art methods for spatial-temporal view synthesis. Our approach works even when being provided only a single monocular real video. We further demonstrate that the learned representation can serve as an implicit scene prior, enabling video processing tasks such as image super-resolution and de-noising without any additional supervision.
Virtual reality adoption during the COVID-19 pandemic: A uses and gratifications perspective

The coronavirus disease 2019 (COVID-19) pandemic has impacted all aspects of people's lives, including how we work, play, learn, exercise, and socialize. Virtual reality (VR) technology has the potential to mitigate many of the challenges brought about by the pandemic, which has spurred increased adoption. However, relatively low adoption overall and limited software still restrict the power of VR to address COVID-19 difficulties effectively. This study examines how the perceived impacts of COVID-19 might lead to different VR uses and gratifications and device ownership / variability. Furthermore, we investigate the importance of social interactivity within VR for increasing adoption intentions. We surveyed 298 Amazon Mechanical Turk users during the Fall of 2020. Results indicate that the pandemic's perceived impacts influenced the likelihood of acquiring VR for education, tourism, and work. For VR ownership and variability, those who purchased VR during the pandemic were more likely to report buying it for work. Those with access to high-end VR hardware were more likely to report a broader range of uses, including socializing, health, and telemedicine. Validating the importance of various applications during the pandemic, we found that the desire for social interactivity mediates the impacts of COVID-19 on future adoption intentions. Theoretically, we propose several gratifications sought via the use of VR during the pandemic. Practically, we discuss recommendations for future VR research, marketing, and software design.
Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems

A novel bio-inspired algorithm called Orca Predation Algorithm (OPA) is proposed in this paper. OPA simulates the hunting behavior of orcas and abstracts it into several mathematical models: including driving, encircling and attacking of prey. The algorithm assigns different weights to the phases of prey driving and encircling through parameter adjustment to balance the exploitation and exploration stages of the algorithm. In the attacking phase, after considering the positions of several superior orcas and some randomly selected orcas, the optimal solution can be approached without losing the diversity of the particles. In order to estimate the performance of OPA, 67 unconstrained benchmark functions were first employed, and then the efficiency of the algorithm was further evaluated on five constrained engineering optimization problems. Besides, the computational complexity, parameter sensitivity and four qualitative metrics of OPA were analyzed to evaluate the applicability of the algorithm. The experimental results demonstrate that OPA can generate more promising results with superior performance relative to other test algorithms on different search landscapes.