Reviewerly/exHarmony huggingface dataset:
In situ high-resolution two-dimensional profiles of redox sensitive metal mobility in sediment-water interface and porewater from estuarine sediments

Metals in contaminated sediments may present high environmental risks and ecological threats to benthic organisms. Redox sensitive elements with different oxidation states show variations in solubility as a function of redox status of the sediment water environment. The novel high-resolution ZrO-Chelex-AgI diffusive gradients in thin film (HR-ZCA DGT) technique provided sensitive in situ mapping of metals in the estuarine sediments. The present study investigated the sub-millimeter two-dimensional distributions of DGT-labile S(-II), P(V), and six redox sensitive metals (Fe, Mn, V, Cu, Ni, and Zn) across sediment-water interface (SWI) severely influenced by anthropogenic activity. We for the first time used the V-turning value (the V/Fe ratios at ~0.03) to accurately identify the actual SWI. The diffusion boundary layer (DBL) thickness of Ni, Cu and Zn was consistent with those identified by the dissolved oxygen microelectrode method, and was 3-6 mm above the SWI. No significant release of dissolved Fe and P from sediments into the overlying water was found by diffusion process. The estimated fluxes (Fdif) of Ni, Cu, and Zn at DBL were 4.0-176, -1.1-235, and 5.0-108 μg m-2 d-1, respectively, and were significantly higher in sediments near the industrial effluent dumping sites than those in sediments impacted by domestic wastewater releases. Metal diffusion flux was mainly controlled by the particulate matter on the surface sediment and organic degradation. Traditional diffusion flux may have underestimated the flux of metals from the surface sediments. The discharge of hypoxic tributary was an important source of metal pollution in the contaminated estuarine sediments.
Unsupervised Domain Adaptation by Multi-Loss Gap Minimization Learning for Person Re-Identification

Unsupervised domain adaptation (UDA) person re-identification (ReID) faces enormous challenges due to the severe shift between the source and target domains, as well as the dramatic variations within the target domain. In this paper, to address these issues, we propose a multi-loss gap minimization learning (MGML) approach for UDA person ReID. Firstly, we introduce the part model to learn discriminative patch features and design a Patch-based Part Ignoring (PPI) loss to select reliable instances for the efficient learning of the part model. Then, given the gap that typically occurs because of the inter-domain shift and intra-domain variations, a Gap-based Minimum Camera Discrepancy (G-MCD) loss is proposed. Specifically, in terms of the inter-domain, we propose to leverage the tracklet and camera information to label each distance vector, and accordingly align pair-wise distance distributions to bridge the inter-domain gap. As for the intra-domain, to alleviate the biased search, we propose to perform the mined neighborhood for intra-camera and inter-camera separately to optimize matching results by exploring neighborhood relations more deeply. Finally, experimental results on three challenging datasets demonstrate that applying our method to unlabeled target domain outperforms current UDA methods for person ReID.
Progressive Subtractive Recurrent Lightweight Network for Video Deraining

Presence of rainy artifacts severely degrade the overall visual quality of a video and tend to overlap with the useful information present in the video frames. This degraded video affects the effectiveness of many automated applications like traffic monitoring, surveillance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> As video deraining is a pre-processing step for automated applications, it is highly demanded to have a lightweight deraining module. Therefore, in this paper, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Progressive Subtractive Recurrent Lightweight Network”</i> is proposed for video deraining. Initially, the Multi-Kernel feature Sharing Residual Block (MKSRB) is designed to learn different sizes of rain streaks which facilitates the complete removal of rain streaks through progressive subtractions. These MKSRB features are merged with previous frame output recurrently to maintain the temporal consistency. Further, multi-receptive feature subtraction is performed through Multi-scale Multi-Receptive Difference Block (MMRDB) to avoid loss of details and extract high-frequency information. Finally, progressively learned features through MKSRB and recurrent feature merging are aggregated with fused MMRDB features which outputs the rain-free frame. Substantial experiments on prevailing synthetic datasets and real-world videos verify the superior performance of the proposed method over the existing state-of-the-art methods for video deraining.
Caputo derivative applied to very short time photovoltaic power forecasting

Intra-hour photovoltaic power forecasting provides essential information for real time optimal control of microgrids. At this purpose, a critical issue is the selection of the forecasting method. The choice of a forecasting method depends on many factors such as the availability of historical data, the time horizon, the lag period, and the time available for the forecast. Persistence based methods are particularly tailored for real time forecasting which require fast information and are typically a good trade-off choice when dealing with real time operation of microgrids. Their accuracy, however, could be not satisfactory in some cases such as when it appears critical to consider the trend of the power output in the last few minutes rather than only the last measured value. Derivatives help reach this goal, but fractional derivatives seem to be a more accurate choice in order to take into account the history of the variable to be forecasted as they are a promising tool for describing memory phenomena. In this paper, a novel intra-hour forecasting method is proposed based on the Caputo derivative. Numerical applications are carried out to show the efficacy of the proposed approach. Also, the accuracy of the proposed approach is tested through comparison with three models namely, persistence, derivative-persistence and auto regressive moving average models. The strength of the proposed forecasting tool is strictly related to its low computational burden without compromising accuracy. This makes of it an interesting means for real time grid operation strategies and can be of interest for the grid operators especially in vision of the changes distribution grids are witnessing with the transition to the smart grid paradigm.
mixR: An R package for Finite Mixture Modeling for Both Raw and Binned Data

R (R Core Team, 2020) provides a rich collection of packages for building and analyzing finite mixture models, which are widely used in unsupervised learning, such as model-based clustering and density estimation.For example, mclust (Scrucca et al., 2016) can be used to build Gaussian mixture models with different covariance structures, mixtools (Benaglia et al., 2010) implements parametric and non-parametric mixture models as well as mixtures of Gaussian regressions, flexmix (Leisch, 2004) provides a general framework for finite mixtures of regression models, mixdist (Macdonald et al., 2018) fits mixture models for grouped and conditional data (also called binned data).To our knowledge, almost all R packages for finite mixture models are designed to use raw data as the modeling input except mixdist.However, the popular model selection methods based on information criteria or bootstrapping likelihood ratio test (bLRT) (Feng & McCulloch, 1996;McLachlan, 1987;Yu & Harvill, 2019) are not implemented in mixdist.To bridge this gap and to unify the interface for finite mixture modeling for both raw and binned data, we implement mixR package that provides the following primary features.
Optimized steering: Quantum state engineering and exceptional points

The state of a quantum system may be steered towards a predesignated target state, employing a sequence of weak $\textit{blind}$ measurements (where the detector's readouts are traced out). Here we analyze the steering of a two-level system using the interplay of a system Hamiltonian and weak measurements, and show that $\textit{any}$ pure or mixed state can be targeted. We show that the optimization of such a steering protocol is underlain by the presence of Liouvillian exceptional points. More specifically, for high purity target states, optimal steering implies purely relaxational dynamics marked by a second-order exceptional point, while for low purity target states, it implies an oscillatory approach to the target state. The dynamical phase transition between these two regimes is characterized by a third-order exceptional point.
Exploiting Multi-View Part-Wise Correlation via an Efficient Transformer for Vehicle Re-Identification

Image-based vehicle re-identification (ReID) has witnessed much progress in recent years. However, most of existing works struggled to extract robust but discriminative features from a single image to represent one vehicle instance. We argue that images taken from distinct viewpoints, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> front and back, have significantly different appearances and patterns for recognition. In order to identify each vehicle, these models have to capture consistent “ID codes” from totally different views, causing learning difficulties. Additionally, we claim that part-level correspondences among views, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> various vehicle parts observed from the identical image and the same part visible from different viewpoints, contribute to instance-level feature learning as well. Motivated by these, we propose to extract comprehensive vehicle instance representations from multiple views through modelling part-wise correlations. To this end, we present our efficient transformer-based framework to exploit both inner- and inter-view correlations for vehicle ReID. In specific, we first adopt a convnet encoder to condense a series of patch embeddings from each view. Then our efficient transformer, consisting of a distillation token and a noise token in addition to a regular classification token, is constructed for enforcing these patch embeddings to interact with each other regardless of whether they are taken from identical or different views. We conduct extensive experiments on widely used vehicle ReID benchmarks, and our approach achieves the state-of-the-art performance, showing the effectiveness of our method.
ChestXRayBERT: A Pretrained Language Model for Chest Radiology Report Summarization

Automatically generating the “impression” section of a radiology report given the “findings” section can summarize as much salient information of the “findings” section as possible, thus promoting more effective communication between radiologists and referring physicians. To significantly reduce the workload of radiologists, we develop and evaluate a novel framework of abstractive summarization methods to automatically generate the “impression” section of chest radiology reports. Despite recent advancements in natural language process (NLP) field such as BERT and its variants, existing abstractive summarization models and methods could not be directly applied to radiology reports, partly due to domain-specific radiology terminology. In response, we develop a pre-trained language model in the chest radiology domain, named ChestXRayBERT, to solve the problem of automatically summarizing chest radiology reports. Specifically, we first collect radiology-related scientific papers as pre-training corpus and pre-train a ChestXRayBERT on it. Then, an abstractive summarization model is proposed, which consists of the pre-trained ChestXRayBERT and a Transformer decoder. Finally, the model is fine-tuned on chest X-ray reports for the abstractive summarization task. When evaluated on the publicly available OPEN-I and MIMIC-CXR datasets, the performance of our proposed model achieves significant improvement compared with other neural networks-based abstractive summarization models. In general, the proposed ChestXRayBERT demonstrates the feasibility and promise of tailoring and extending advanced NLP techniques to the domain of medical imaging and radiology, as well as in the broader biomedicine and healthcare fields in the future.
Decision Making on Tourism by Using Natural Language Processing

The paper deals with measuring the effectiveness of Natural Language in decision-making to have comprehensive results. It’s more convenient for people to comprehend the meaning of linguistic terms rather than quantitative data. A lot of studies have shown that people don’t understand or they are reluctant to act upon numerical data. We surveyed people presenting two different types of information: Natural Language Generation (NLG) and graphical representation to measure the predicted number of tourist arrivals to three countries in four quarters. Fuzzy Inference analysis was applied on the base of the collected data. As a result of the survey, it became clear that people are more inclined in making decisions on NLG rather than on state-of-the-art graphics. Only 27% of the people made decision on graphical based information, the others chose NLG conveyed by “IF…Then” rules. The validity of the decisions made on NLG is 78%, while the other one is less than 62%.
Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

Purpose The results of obstacle avoidance path planning for the manipulator using artificial potential field (APF) method contain a large number of path nodes, which reduce the efficiency of manipulators. This paper aims to propose a new intelligent obstacle avoidance path planning method for picking robot to improve the efficiency of manipulators. Design/methodology/approach To improve the efficiency of the robot, this paper proposes a new intelligent obstacle avoidance path planning method for picking robot. In this method, we present a snake-tongue algorithm based on slope-type potential field and combine the snake-tongue algorithm with genetic algorithm (GA) and reinforcement learning (RL) to reduce the path length and the number of path nodes in the path planning results. Findings Simulation experiments were conducted with tomato string picking manipulator. The results showed that the path length is reduced from 4.1 to 2.979 m, the number of nodes is reduced from 31 to 3 and the working time of the robot is reduced from 87.35 to 37.12 s, after APF method combined with GA and RL. Originality/value This paper proposes a new improved method of APF, and combines it with GA and RL. The experimental results show that the new intelligent obstacle avoidance path planning method proposed in this paper is beneficial to improve the efficiency of the robotic arm. Graphical abstract Figure 1 According to principles of bionics, we propose a new path search method, snake-tongue algorithm, based on a slope-type potential field. At the same time, we use genetic algorithm to strengthen the ability of the artificial potential field method for path searching, so that it can complete the path searching in a variety of complex obstacle distribution situations with shorter path searching results. Reinforcement learning is used to reduce the number of path nodes, which is good for improving the efficiency of robot work. The use of genetic algorithm and reinforcement learning lays the foundation for intelligent control.
Learning Fast and Robust Gesture Recognition

Autonomous Unmanned Aerial Vehicles (UAVs, or drones) are being increasingly employed to assist in many tasks, typically in collaboration with humans. Since most drones are equipped with RGB cameras, a typical way of visual interaction is through human hand gestures. Thus, this paper examines a common, two-stage algorithmic framework for gesture recognition, suitable for execution on any camera-equipped UAV with embedded AI capabilities. First, a fast 2D human body pose estimation Deep Neural Network (DNN) extracts 2D skeleton information from the input video frames. Then, these per-frame skeletons that have been computed over a temporal window are fed to a separate classifier, which outputs the final gesture prediction. However, no exhaustive quantitative comparisons have been conducted up to now in order to specify the best-performing algorithmic ingredients in the context of this framework. Therefore, we investigated and experimentally evaluated various possibilities for 2D skeleton information utilization, as well as for gesture classification itself, in order to identify the ideal combination for optimal efficiency. Using the empirically best approach, we achieved increased gesture recognition performance on two challenging datasets, when compared to competing relevant methods, at a runtime advantage on embedded AI compute hardware.
A Robust Game-Theoretical Federated Learning Framework With Joint Differential Privacy

Federated learning is a promising distributed machine learning paradigm that has been playing a significant role in providing privacy-preserving learning solutions. However, alongside all its achievements, there are also limitations. First, traditional frameworks assume that all the clients are voluntary and so will want to participate in training only for improving the model’s accuracy. However, in reality, clients usually want to be adequately compensated for the data and resources they will use before participating. Second, today’s frameworks do not offer sufficient protection against malicious participants who try to skew a jointly trained model with poisoned updates. To address these concerns, we have developed a more robust federated learning scheme based on joint differential privacy. The framework provides two game-theoretic mechanisms to motivate clients to participate in training. These mechanisms are dominant-strategy truthful, individual rational, and budget-balanced. Further, the influence an adversarial client can have is quantified and restricted, and data privacy is similarly guaranteed in quantitative terms. Experiments with different training models on real-word datasets demonstrate the effectiveness of the proposed approach.
Federated Learning for Bearing Fault Diagnosis with Dynamic Weighted Averaging

Data-driven methods for machinery fault diagnosis have been widely investigated and developed in recent years. Generally, the performance of the data-driven-based diagnosis models deeply depends on abundant high-quality data, which is unrealistic to a specific machine owner in the actual industry. Therefore, multiple data owners are motivated to unite to train a good global model with the aggregated big dataset. However, due to legal regulations on data privacy or potential conflict of interest, it is difficult to build a trusted and safe central database to aggregate the local datasets. To address this issue, a federated learning scheme with dynamic weighted averaging is proposed. In the training process, models are updated with local data. Then, the model parameters are uploaded to the server and aggregated, followed by distributing the updated global model back to the federated learning participants. In this case, data from different participants is not exposed during the training, which ensures data privacy. The proposed dynamic weighted averaging algorithm further considers the imbalance of distributed data, which can effectively reduce the influence of bad local data. The experiments show that the proposed federated learning scheme outperforms the traditional one and offers a promising perspective in privacy-preserving data mining.
Securely Outsourcing Neural Network Inference to the Cloud With Lightweight Techniques

Neural network (NN) inference services enrich many applications, like image classification, object recognition, facial verification, and more. These NN inference services are increasingly becoming an essential offering from cloud computing providers, where end-users’ data are offloaded to the cloud for inference under a customized model. However, current cloud-based inference services operate on clear inputs and NN models, raising paramount privacy concerns. Individual user data may contain private information that should always remain confidential. Meanwhile, the NN model is deemed proprietary to the model owner as model training requires substantial resources. In this article, we present, tailor, and evaluate <monospace>Sonic</monospace> , a lightweight secure NN inference service delegated in the cloud. <monospace>Sonic</monospace> leverages the cloud computing paradigm to fully outsource the secure inference, freeing end devices and model owners from being actively online for assistance. <monospace>Sonic</monospace> guards both user input and model privacy along the whole service flow. We design a series of secure and efficient NN layer functions purely using lightweight cryptographic primitives. Extensive evaluations demonstrate that <monospace>Sonic</monospace> achieves up to <inline-formula><tex-math notation="LaTeX">$60\times$</tex-math></inline-formula> bandwidth saving in online inference compared to prior art.
Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response

In December 2019, a new pandemic called the coronavirus began ravaging the world. By May 2020, the pandemic had caused great loss of lives and disrupted the way of lives in more ways than one. The nature of the disease saw several strategies to curb its spread rolled out. These strategies included closing of businesses and borders, restriction of movements and working from home, mask mandate among others. With these measures and the effects, many individuals have taken to the social media to express their frustrations, opinions and how the pandemic is affecting them. This study employs dictionary based method for sentiment polarization from tweets related to coronavirus posted on Twitter. We also examine the co-occurrence of words to gain insights on the aspects affecting the masses. The results showed that mental health issues, lack of supplies were some of the direct effects of the pandemic. It was also clear that the COVID-19 prevention guidelines were well understood by those who tweeted. The results from this study may help governments combat the consequences of COVID-19 like mental health issues, lack of supplies e.g. food and also gauge the effectiveness or the reach of their guidelines.
Quantum computing critical exponents

We show that the variational quantum-classical simulation algorithm admits a finite circuit depth scaling collapse when targeting the critical point of the transverse field Ising chain. The order parameter only collapses on one side of the transition due to a slowdown of the quantum algorithm when crossing the phase transition. In order to assess performance of the quantum algorithm and compute correlations in a system of up to 752 qubits, we use techniques from integrability to derive closed-form analytical expressions for expectation values with respect to the output of the quantum circuit. We also reduce a conjecture made by Ho and Hsieh [W. W. Ho and T. H. Hsieh, SciPost Phys. 6, 029 (2019)] about the exact preparation of the transverse field Ising ground state to a system of equations.