Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Communication Architectures for Distributed Smart Grid Applications

Wired and wireless communications both play an important role in the blend of communications technologies necessary to enable future smart grid communications. Hybrid networks exploit independent mediums to extend network coverage and improve performance. However, whereas individual technologies have been applied in simulation networks, as far as we know there is only limited attention that has been paid to the development of a suite of hybrid communication simulation models for the communications system design. Hybrid simulation models are needed to capture the mixed communication technologies and IP address mechanisms in one simulation. To close this gap, we have developed a suite of hybrid communication system simulation models to validate the critical system design criteria for a distributed solar Photovoltaic (PV) communications system, including a single trip latency of 300 ms, throughput of 9.6 Kbps, and packet loss rate of 1%. The results show that three low-power wireless personal area network (LoWPAN)-based hybrid architectures can satisfy three performance metrics that are critical for distributed energy resource communications.
Molecular Genetics and Targeted Therapies for Paediatric High-grade Glioma

Brain tumours are the leading cause of paediatric cancer-associated death worldwide. High-grade glioma (HGG) represents a main cause of paediatric brain tumours and is associated with poor prognosis despite surgical and chemoradiotherapeutic advances. The molecular genetics of paediatric HGG (pHGG) are distinct from those in adults, and therefore, adult clinical trial data cannot be extrapolated to children. Compared to adult HGG, pHGG is characterised by more frequent mutations in PDGFRA, TP53 and recurrent K27M and G34R/V mutations on histone H3. Ongoing trials are investigating novel targeted therapies in pHGG. Promising results have been achieved with BRAF/MEK and PI3K/mTOR inhibitors. Combination of PI3K/mTOR, EGFR, CDK4/6, and HDAC inhibitors are potentially viable options. Inhibitors targeting the UPS proteosome, ADAM10/17, IDO, and XPO1 are more novel and are being investigated in early-phase trials. Despite preclinical and clinical trials holding promise for the discovery of effective pHGG treatments, several issues persist. Inadequate blood-brain barrier penetration, unfavourable pharmacokinetics, dose-limiting toxicities, long-term adverse effects in the developing child, and short-lived duration of response due to relapse and resistance highlight the need for further improvement. Future pHGG management will largely depend on selecting combination therapies which work synergistically based on a sound knowledge of the underlying molecular target pathways. A systematic investigation of multimodal therapy with chemoradiotherapy, surgery, target agents and immunotherapy is paramount. This review provides a comprehensive overview of pHGG focusing on molecular genetics and novel targeted therapies. The diagnostics, genetic discrepancies with adults and their clinical implications, as well as conventional treatment approaches are discussed.
Differences in Trophic and Community Structure of Kelp Forest Fishes Inside and Outside of Three Long-standing MPAs in the Southern California Bight

Abstract. In many marine ecosystems worldwide, overfishing is a prominent cause in removing large predatory fishes from ecological communities. Fluctuation in the abundance of higher trophic level species can transform an ecosystem's structure and function by altering trophic interactions through density-mediated top-down control. Accordingly, understanding the extent to which humans indirectly influence a community through altering predator abundance is of critical importance. Thus, during the summer of 2013 and 2014 the impacts of fishing on the trophic structure of kelp forest fishes were examined within the Southern California Bight. In 2013, we tested whether decreased abundance through fishing for higher trophic level predators relieves predation pressure on lower trophic level prey. Using a combination of underwater survey techniques, density (no. fish/100 m2) and biomass (g/100 m2) of conspicuous fish species were sampled inside and outside of three long-standing Marine Protected Areas (MPAs) off La Jolla, Santa Catalina Island, and Anacapa Island, California. We found that the secondary carnivore and herbivore/omnivore trophic levels were significantly lower in density and biomass outside of MPAs. Inversely, the primary carnivore trophic level biomass was higher outside of MPAs. At the species level, we observed a lower abundance outside MPAs of large kelp bass (>25 cm) and higher densities of potential prey, kelp perch. Other primary carnivore fishes (blacksmith and señorita) showed a non-significant trend of higher biomass outside MPAs. Our results provide evidence of trophic level changes due to fishing pressure and provide support for a possible weakening of top-down control on specifically the kelp perch population through the removal of large kelp bass outside MPAs. The removal or recovery of predators can greatly influence an ecosystem. As more recent studies suggest that indirect community effects of fishing and protection can take up to decades to detect, it is necessary to document the continued changes on the structure, function, and dynamics of the kelp forests and rocky reefs off southern California.
Controlling trapping, overgrazing and invasive vegetation is key to saving Java's last population of the Black-winged Myna

ABSTRACT The Black-winged Myna (Acridotheres melanopterus) is an Endangered passerine endemic to the islands of Java and Bali, Indonesia. Illegal trapping to supply the cage-bird trade has led to its near-total extinction, with the global population estimated to number fewer than 100 individuals. We estimated the current range and population size of the species at Baluran National Park, which supports Java's last known population, and used species distribution modeling to evaluate potential suitability of currently unoccupied areas across the park to identify priorities for management intervention. We estimate that the Black-winged Myna population numbers 179 individuals (95% CI: 111–288; density: 14.3 ± 3.5 individuals km–2) and that its current range is 12.3 km2. Our model indicated that some 72 km2 of the park (30% of total area) has potentially suitable habitat for the species, and we infer that the principal cause for the disparity between its current and potential range is trapping, compounded by savanna loss and degradation due to illegal domestic cattle grazing and the spread of invasive thorny acacia (Vachellia nilotica). The partial clearance of acacia in recent years appears to have assisted a modest population recovery by the myna. Its further population growth and range expansion in Baluran will depend on effective management of illegal poaching, further clearance of acacia, and easing domestic cattle grazing pressure on areas of savanna, particularly through engagement with communities living inside the park. Any actions that increase the size of the Black-winged Myna population are likely to benefit other threatened savanna-dependent wildlife in the park, notably banteng (Bos javanicus) and Green Peafowl (Pavo muticus). While our models and recommendations may be applicable to other protected areas in Java, and indeed other threatened myna species, trapping and habitat change may have site-specific dimensions, especially outside of protected areas, and thus demand local bespoke solutions. LAY SUMMARY The Black-winged Myna, confined to Java and Bali in Indonesia, is threatened with extinction due to illegal trapping for the cage-bird trade. Baluran National Park supports the last known population on Java. We estimated the number of mynas in the park, mapped where they occur, and assessed their habitat to determine how much of it is currently unoccupied. There are ∼180 Black-winged Mynas in the park, indicating recent population growth. However, they occupy <20% of the potentially suitable habitat, restricting further population growth. Trapping is the foremost factor holding back the mynas, but overgrazing by domestic livestock and invasion by thorny acacia negatively affect the potentially suitable habitat. Thorny acacia eradication must continue. Working with the human communities living in the park is key to alleviating the grazing pressure and addressing the trapping issue.
A Segmentation Model of Lung Parenchyma in Chest CT Based on ResUnet

Segmentation of the lung parenchymal region in chest CT is an essential part of the automatic diagnosis of lung diseases. Therefore, the quality of the segmentation directly affects the results of the automatic diagnosis. This paper proposes a model for lung parenchymal segmentation in chest CT based on ResUnet. It introduces the residual learning unit to transfer low-level information and enhances the connection between layers using skip connections based on the U-Net architecture. Then, it achieves full feature extraction through down-convolution and up-sampling and uses image enhancement and data augmentation to preprocess the data set. Through experiment, the proposed segmentation model has better results than the IoU and Dice of other models and can better segment the lung parenchyma in chest CT.
Analysis Method of Ship Detention Judgment based on optimized DBSCAN Cluster Algorithm

In order to realize the rapid extraction of maritime traffic behavior characteristics, we should effectively monitor and analyze the berthing and navigation of ships, and timely discover the abnormal behavior of ships. In this paper, an analysis method of ship detention judgment based on optimized DBSCAN cluster algorithm is proposed. In this method the target track information in the monitoring period of the monitoring area, the area is meshed to find the potential core points, and the potential core points are analyzed by DBSCAN cluster analysis to determine the ship detention behavior. The results show that this method can quickly determine the ship detention behavior. Compared with the traditional DBSCAN cluster analysis method, this method maintains high accuracy, reduces the computing time complexity and effectively improves the computational efficiency.
Network Abnormality Location Algorithm Based on Greedy Monte Carlo Tree

The cloud service providers manage very large data centers all over the world. When an abnormality occurs, various types of alarm information are triggered. The operation engineers need to quickly discover and locate all the abnormalities. To solve the problems of computing-intensive, non-real-time, and inaccurate abnormal detection and location algorithm, we propose to improve the Monte Carlo Tree Search (MCTS) based on the greedy algorithm by: 1) improving the selection of the next node in MCTS by using greedy algorithm and searching the best node with depth-first method; 2) adopting the sparse matrix to store the record of the 5-dimensional log files, then employing the subscript file to record the subscript of the 5-dimensional array, and using the subscript to access the sparse matrix to save memory space and searching time; 3) reducing calculations by pruning some branches based on the observation that the optimal node combination of present layer must belong to the search space of the best layer combination of the previous layer. The experimental results show that GMCTS algorithm reduces 40% computation time than the HotSpot in 5D data, and the correct positioning efficiency is up to 96.1%.
On sampled metrics for item recommendation

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B, not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate.
Learning-based Vulnerability Detection in Binary Code

Cyberattacks typically exploit software vulnerabilities to compromise computers and smart devices. To address vulnerabilities, many approaches have been developed to detect vulnerabilities using deep learning. However, most learning-based approaches detect vulnerabilities in source code instead of binary code. In this paper, we present our approach on detecting vulnerabilities in binary code. Our approach uses binary code compiled from the SARD dataset to build deep learning models to detect vulnerabilities. It extracts features on the syntax information of the assembly instructions in binary code, and trains two deep learning models on the features for vulnerability detection. From our evaluation, we find that the BLSTM model has the best performance, which achieves an accuracy rate of 81% in detecting vulnerabilities. Particularly the F1-score, recall, and specificity of the BLSTM model are 75%, 95% and 75% respectively. This indicates that the model is balanced in detecting both vulnerable code and non-vulnerable code.
Single stock trading with deep reinforcement learning: A comparative study

In this paper, we apply Deep Reinforcement Learning (DRL) methods to automate the trading of single stock. The A2C, PPO, DDPG, TD3 and SAC deep reinforcement learning models are built and studied comparatively. Shanghai Composite Index (SH00001) is used as the trading stock, where the stock data before the Covid-19 is used as the training set, and the data after the Covid-19 is used as the testing (trading) set to back-test the performance of these models. Experimental results show that the DDPG, TD3, and SAC models outperform the benchmark, among which the DDPG model shows the most obvious advantages in returns and risk control, achieving a cumulative return rate of 25%, while the TD3 and SAC models achieve a cumulative return rate of 16-17%. The A2C and PPO models have inferior performance comparing to the benchmark.
Micro-expression Recognition Based on Attention-enhanced LSTM Neural Networks

Micro-expression recognition is a difficult task in computer vision. Most existing micro-expression recognition methods extract facial features globally, leading to the inclusion of many irrelevant features and affecting the recognition accuracy in a negative way. In this paper, Long Short-Term Memory (LSTM) neural networks with spatial and temporal attention mechanisms are designed and employed to extract features selectively from the input sequences. Key frames are identified from the original micro-expression sequences at first. Then the VGG-Face model is used to extract the spatial features of those key frames. The spatial features of the micro-expression sequences are then fed into attention-enhanced long short-term memory neural networks, using a softmax function for the final classification. Our experiments with CASME II show that the attention-enhanced LSTM models improve the accuracy of micro-expression recognition significantly, compared to the results of several other leading methods.
Invited Commentary Invited Commentary: On the Road to Improved Exposure Assessment using Geographic Information Systems

1 Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD. 2 Division of Environmental Epidemiology, Department of Environmental and Occupational Medicine, University of Medicine and Dentistry of New Jersey–Robert Wood Johnson Medical School, Piscataway, NJ.
DRT: Detection Refinement for Multiple Object Tracking

Deep learning methods have led to remarkable progress in multiple object tracking (MOT). However, when tracking in crowded scenes, existing methods still suffer from both inaccurate and missing detections. This paper proposes Detection Reﬁnement for Tracking (DRT) to address these two issues for people tracking. First, we construct an encoder-decoder backbone network with a novel semi-supervised heatmap training procedure, which leverages human heatmaps to obtain a more precise localization of the targets. Second, we integrate a "one patch, multiple predictions" mechanism into DRT which reﬁnes the detection results and recovers occluded pedestrians at the same time. Additionally, we leverage a data-driven LSTM-based motion model which can recover lost targets at a negligible computational cost. Compared with strong baseline meth-ods, our DRT achieves signiﬁcant improvements on publicly available MOT datasets. In addition, DRT generalizes well, i.e. it can be applied to any detector to improve their performance.
Training Better Deep Neural Networks with Uncertainty Mining Net

In this work, we consider the problem of training deep neural networks on partially labeled data with label noise. That is, semi-supervised training of deep neural networks with noisily labeled data. As far as we know, this is a scarcely studied topic. We present a novel end-to-end deep generative framework for improving classiﬁer performance when dealing with such data challenges. We call it Uncertainty Mining Net (UMN). We utilize all the available data (labeled and unlabeled) to train the classiﬁer via a semi-supervised generative framework. During training, UMN estimates the uncertainty of the labels to focus on clean data for learning. More precisely, UMN applies a novel sample-wise label uncertainty estimation scheme. Extensive experiments and comparisons against state-of-the-art methods on several popular benchmark datasets demonstrate that UMN can reduce the impact of label noise and signiﬁcantly improve classiﬁer performance.
Multi-Class Novelty Detection with Generated Hard Novel Features

It has been shown that convolutional neural networks clearly have the overconﬁ-dence problem, i.e., mis-classify a novel sample into one of the known classes with high conﬁdence. The task of multi-class novelty detection is thus important to detect novel samples during inference. In this work, we propose to generate hard novel features via a generative adversarial network to facilitate constructing a powerful novelty detector. The generated features should be around the boundaries between known classes and novel classes. They cause a bigger challenge for the novelty detector, and consequently enforce the novelty detector to be stronger. We verify effectiveness of hard novel features from several perspectives, and show that this idea yields the state-of-the-art performance.
Impact of climate change on streamflow and water quality in the upper Dong Nai river basin, Vietnam

In this study, we investigated the impact of climate change on streamflow and water quality (TSS, T-N, and T-P loads) in the upper Dong Nai River Basin using the Soil and Water Assessment Tool (SWAT) hydrological model. The calibration and validation results indicated that the SWAT model is a reasonable tool for simulating streamflow and water quality for this basin. Based on the well-calibrated SWAT model, the responses of streamflow, sediment load, and nutrient load to climate change were simulated. Climate change scenarios (RCP 4.5 and RCP 8.5) were developed from five GCM simulations (CanESM2, CNRM-CM5, HadGEM2-AO, IPSL-CM5A-LR, and MPI-ESM-MR) using the delta change method. The results indicated that climate in the study area would become warmer and wetter in the future. Climate change leads to increases in streamflow, sediment load, T-N load, and T-P load. Besides that, the impacts of climate change would exacerbate serious problems related to water shortage in the dry season and soil erosion and degradation in the wet season. In addition, it is indicated that changes in sediment yield and nutrient load due to climate change are larger than the corresponding changes in streamflow.
Grand Unified Domain Adaptation

Existing domain adaptation (DA) methods try to handle various DA scenarios subject to imbalanced label sets or multiple source/target domains, e.g. , Closed set, Open set, Multi-Source, Partial and Multi-Target DA. Though Universal Domain Adaptation (UniDA) and Versatile Domain Adaptation (VDA) have been proposed to address these scenarios simultaneously, the related proposed methods still suffer from two issues: i) UniDA and VDA can hardly cover all of existing DA scenarios, e.g. , UniDA cannot handle Multi-Source and Multi-Target DA scenarios, and VDA does not include Open set DA. ii) The proposed UniDA and VDA methods mainly focus on the versatility, and they ignore the essential DA problem, i.e. , domain mismatch. This paper introduces Grand Uniﬁed Domain Adaptation (GUDA) scenario, which needs no prior knowledge about the number of source/target domains or the overlap. GUDA can cover more existing DA scenarios. Towards tackling GUDA, we formulate a grand uniﬁed adaptation network called G raph C ontrastive A daptation N etwork (GCAN), which can handle above mentioned DA scenarios and further reduces the domain mismatch without any modiﬁcation. GCAN includes a graph contrastive adaptation objective at the node level, and a trans-ferability rule to gain the common category identiﬁcation loss. The results illustrate that GCAN works stably on different GUDA settings and shows comparable performance against recent DA methods on ﬁve benchmarks.
Discriminative Clue Alignment Network for Both Image- and Video-Based Person Re-Identification

The body misalignment problem resulted from various factors, e.g., scale and pose variation, occlusion, etc., has always been a great challenge in person re-identiﬁcation. It is intuitive to model individual body parts and match them through pose estimation or human parsing, which, however, requires additional annotations and training, and may fail with occlusion. Some recent studies employ self-attention mechanisms to discover the Discriminative Clues (DCs) on the body. But, unlike the body parts, the DCs are not naturally aligned properly. To this end, we propose a Discriminative Clue Alignment Network (DCANet), along with a discriminant constraint, to automatically identify various DCs and then align them into a ﬁxed pattern, without explicitly analyzing body parts. Moreover, such an alignment scheme makes the temporal aggregation of features from video frames extremely simple, because the DCs are effectively aligned across frames. Therefore our method can be easily applied to video-based person re-identiﬁcation as well. Experiments on several popular public benchmarks show that DCANet can achieve state-of-the-art performance on both image-and video-based re-identiﬁcation tasks.