bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fine-Graine Visual Classification with Aggregated Object Localization and Salient Feature Suppression
Authors: Qu Haicheng , Zhao Siqi , Liu Wanjun 
Published: January 2022

Abstract
Fine-grained visual classification (FGVC) is desired to classify sub-classes of objects in the same super-class. For the FGVC tasks, it is necessary to find subtle yet discriminative information from local areas. However, traditional FGVC approaches tended to extract strong discriminative features, and overlook some subtle yet useful features. Besides, current methods ignore the influence of background noises on feature extraction. Therefore, aggregated object localization combined with salient feature suppression are proposed, which is a stacked network. First, the feature maps extracted by the coarse network are fed into aggregated object localization to obtain complete foreground object in an image. Secondly, the refined features obtained through zooming in complete foreground object are fed into fine network. Finally, through finer network processing, the feature maps are fed into salient feature suppression module to find more valuable region discriminative features for classification. Experiment results on two datasets show that our proposed method can get superior result compared with state-of-the-art methods.
Extracting Parallel Sentences from Low-Resource Language Pairs with Minimal Supervision
Authors: Shi Xiayang , Liu Xinyi , Yu Zhenqiang , Cheng Pei , Xu Chun 
Published: January 2022

Abstract
At present, machine translation in the market depends on parallel sentence corpus, and the number of parallel sentences will affect the performance of machine translation, especially in low resource corpus. In recent years, the use of non parallel corpora to learn cross language word representation as low resources and less supervision to obtain bilingual sentence pairs provides a new idea. In this paper, we propose a new method. First, we create cross domain mappings in a small number of single languages. Then a classifier is constructed to extract bilingual parallel sentence pairs. Finally, we prove the effectiveness of our method in Uygur Chinese low resource language by using machine translation, and achieve good results.
Runet: Convolutional Networks for Crack Detection
Authors: Hu Lin , Yang Fuping 
Published: January 2022

Abstract
In daily life, crack is a common phenomenon. Such as road cracks, stone cracks. These cracks are usually not easy to be quickly and intuitively identified, because these cracks are characterized by poor continuity and low contrast. Among the existing crack detection methods based on deep learning, the model is too large to be directly used in life. We design an end-to-end neural network, its name is Runet, which greatly reduces the parameters of the model by depthwise separable convolution, increases the receptive field of the network by hole convolution. At the same time, we introduce the trainingtime and inferencetime architecture to increase the running speed and precision.
Attention Based Data Augmentation for Knowledge Distillation with Few Data
Authors: Tian Shengzhao , Chen Duanbing 
Published: January 2022

Abstract
Knowledge distillation has attracted great attentions from computer vision researchers in recent years. However, the performance of student model will suffer from the absence of the complete dataset, which is used to train the teacher model. Especially for conducting knowledge distillation between heterogeneous models, it is difficult for student model to learn and receive guidance with few data. In this paper, a data augmentation method is proposed based on the attentional response of teacher model. The proposed method utilizes the knowledge in teacher model without requiring homogeneous architecture between teacher model and student model. Experimental results demonstrate that combining the proposed data augmentation method with different knowledge distillation methods, the performance of student model can be improved in knowledge distillation with few data.
Transformer Model for Remaining Useful Life Prediction of Aeroengine
Authors: Li Qinghua , Yang Ying 
Published: January 2022

Abstract
Accurate aeroengine remaining useful life (RUL) prediction plays a vital role in ensuring safe operation and reducing maintenance losses. In order to improve the accuracy of aeroengine RUL prediction, an aeroengine RUL prediction method based on the Transformer model is proposed, which gives greater weight to the characteristics of important time steps through self attention mechanism, and solves the memory degradation problem caused by too long sequence in engine RUL prediction, and excavates the complex mapping relationship between input features and aeroengine RUL. Experiments on the C-MAPSS data set show that the Transformer model can better predict the aeroengine RUL based on the aeroengine degradation data. Compared with the long short term memory network model, the root mean square error of the two sub data sets is reduced by 6.57% and 5.63% respectively.
Based on Improved CNN Bearing Fault Detection
Authors: Xu Yaning , Yang Lei 
Published: January 2022

Abstract
In recent years, the problem about the fault detection of rolling bearings in mechanical equipment has gradually become an important research direction.Among them, the diagnostic method based on vibration signal analysis is widely used in the fault detection of rolling bearings.Since the one-dimensional convolutional neural network (1D-CNN) has certain limitations on the processing of vibration signal data, the solution to this problem in this paper is to integrate the attention mechanism and bi-directional long and short-term memory neural network (BiLSTM) on the basis of the one-dimensional convolutional neural network, using the attention mechanism to give different weights to different feature dimensions in the sample data and extract key and important information, thus the sample data can be further optimized.On the other hand, BiLSTM can automatically extract the deep information of the bearing vibration signal, which makes up for the deficiency of artificial extraction features to a certain extent, and strengthens the discriminative property of high-level features.Subsequently, the improved CNN bearing fault detection model was experimentally validated using the Case Western Reserves University bearing dataset, and it was concluded that the attention mechanism acting on the model obtained by adding BiLSTM to the 1D-CNN could achieve a fault identification accuracy of about 98.9% and the loss degree was reduced to about 0.17%, thus achieving an effective diagnosis of the fault state.
Reference Context Guided Vector to Achieve Multimodal Machine Translation
Authors: Shi Xiayang , Yuan Jiaqi , Huang Yuanyuan , Yu Zhenqiang , Cheng Pei , Liu Xinyi 
Published: January 2022

Abstract
Traditional machine translation mainly realizes the introduction of static images from other modal information to improve translation quality. In processing, a variety of methods are combined to improve the data and features, so that the translation result is close to the upper limit, and some even need to rely on the sensitivity of the sample distance algorithm to the data. At the same time, multi-modal MT will cause problems such as lack of semantic interaction in the attention mechanism in the same corpus, or excessive encoding of the same text image information and corpus irrelevant information, resulting in excessive noise. In order to solve these problems, this article proposes a new input port that adds visual image processing to the decoder. The core idea is to combine visual image information with traditional attention mechanisms at each time step specific to decoding. The dynamic router extracts the relevant visual features, integrates the multi-modal visual features into the decoder, and predicts the target word by introducing the visual image process. At the same time, experiments were carried out on more than 30K datasets translated in the United Kingdom, France and the Czech Republic, which proved the superiority of adding visual images to the decoder to extract features.
Management status of blue swimming crab (Portunus pelagicus Linnaeus, 1758) based on EAFM in the coastal of Pangkep Regency, South Sulawesi
Authors: Yanti N D , Kurnia R , Mashar A , Mardyani Y , Lindawati L 
Published: January 2022

AbstractThe Blue Swimming Crab (BSC) is the main export commodity of Indonesia and one of the BSC agribusinesses in the coastal waters of the Pangkep Regency. However, in recent years, BSC agribusiness in this region has been estimated to be unsustainable. The purpose of this study is to formulate the management of BSC (Portunus pelagicus) resources with the Ecosystem Approach to Fisheries Management (EAFM) in the coastal of the Pangkep Regency. The average value score in the BSC management domain is in the medium category (resource, habitat, economic, and institutional domains); the fishing techniques domain is in the good category, while the social domain is in the poor category. In general, the status of BSC fisheries management is categorized as medium (value = 194.25), therefore it can still be developed and improved. To repair the BSC fisheries management for gradually, tactical decisions were made to enhance the indicator conditions. To make the fishery activities sustainable, recommendations to improve the condition of the BSC fisheries in the Pangkep Regency include strengthening the supervision, improving the seagrass cover, improving coordination among institutions on the BSC fisheries management, and increasing stakeholder participation in the effort to manage the BSC fisheries.
Fisheries forecasting, physical approach comparison between regression and autoregressive integrated moving average (ARIMA)
Authors: Ahmad T E , Rais A , Azhari D R , Rosalia A A , Hutapea R Y F 
Published: January 2022

AbstractEmpowerment of fishery resources requires analysis of forecasting results as an effort to maintain sustainability and human needs. Forecasting is an approach to predicting based on past facts, which is expected to be used as a decision support system. However, there are problems with the accuracy. In this study, we compare the regression method with ARIMA to find out which method can be used as the most appropriate choice in fisheries forecasting with physical benchmarks (seasonal and climate). We conduct a systematic literature study on various studies with the theme of fisheries forecasting. The search focuses on studies with the main criteria in the form of an explicit discussion of the basic forecasting methods and literature on marine physical influences. Then define a search method by combining fishery stocks or landings against forecasting with marine physical features optionally using PRISMA. The results show that the ARIMA models have a better fair value and accuracy than the regression because the ARIMA model captures the history of data autocorrelation and extrapolates it to the forecasting framework that will be carried out. Hence, it is most suitable for use with additional marine physical variability.
The effect of addition dfferent types of binders to the effervescent chemical characteristics of Sonneratia casolaris fruits
Authors: Ratrinia P W , Sumartini  , Hasibuan N E 
Published: January 2022

Abstract

Sonneratia caseolaris fruit that has not been widely explored is as an effervescent raw material. The purpose of this study was to determine the effervescent chemical characteristics of Sonneratia caseolaris fruit with the addition of several different types of binders. The research design used in this study was a Completely Randomized Design using four treatments using different binders, namely Polyvinyl Pyrrolidone (PVP), gelatin, Pulvis Gummi Arabicum (PGA), and maltodextrin. The test method used in this research is the analysis of water content, pH value, vitamin C, antioxidants, phenols, and soluble time. Statistical analysis used was the analysis of variance (ANOVA) with a 95% confidence level. The results showed that different binders had a significantly different effect (P<0.05) on the test results for water content, pH, vitamin C, antioxidants, phenols, and soluble time. The addition of gelatin to the effervescent gave the best value on the results of the soluble time test, namely water content, antioxidants, and soluble time. Meanwhile, the addition of PGA gave the best value for the phenolic test results. The conclusion obtained from this study is that the addition of different binders can have a significantly different effect on the effervescent chemical characteristics of Sonneratia caseolaris fruit.
Sustainability status of bay management: case study in Jor Bay, East Lombok Regency, West Nusa Tenggara Province
Authors: AlAmin MA , Adrianto L , Kusumastanto T , Imran Z , Damar A 
Published: January 2022

Abstract
Jor Bay in East Lombok - West Nusatenggara is a small bay system characterized by a common pool resource, in which found a contestation of utilization among resources users. Even though the bay has been managed by a local institution, namely Lembaga Pemangku Awiq-awiq Teluk Jor (LPATJ), the role and perform of institutions in managing the bay is still very low. To ensure the sustainability of the bay, the need to converge the different resource users’ interests by balancing utilization to accommodate economical, ecological and social dimensions. This study aimed to assess the sustainability status of bay management and design future sustainable bay management strategies. To measure the sustainability status, we used a sustainability index intended to develop to bay ecosystem form. The current sustainability status of Jor Bay management showed a moderate level. The institutional dimension provides the greatest sustainability value, while the lowest degree shows in the ecological dimension. The governability of Jor Bay management shows low institutional interaction, limited scale (local), minimal initiative and low willingness to cooperate among elements. For this reason, the ICM (integrated coastal management) based management mechanism needs to be strengthened to ensure the functioning of the Jor Bay management system.
Assessing of coral reef resilience on Lembeh Island, North Sulawesi
Authors: Idris  , Fakhrurrozi  , Adiwijaya C 
Published: January 2022

Abstract
Resilience is the ability of a system to reach equilibrium after a temporary disturbance. When an ecosystem is disturbed, then the recovery of the system is very dependent on the diversity of species that are still left. The remaining communities determine the direction of new successional communities formed after the disturbance has passed, including coral, fish, and other biota communities. This study aimed to determine the level of resilience of coral reef ecosystems on Lembeh Island. The observation was carried out from January 16-19, 2019, at a 7-meter depth. Determination of the coral reef index will refer to coral reef resilience index table. It is found that the resilience of coral reefs on Lembeh Island currently has a high potential for recovery (resilience) from natural stressors based on the 16 parameters. Seven parameters have very high resilience values: the proportion of bleaching resistant species, community resistance to bleaching, taxonomic diversity, size/age distribution, the abundance of reef fish on live coral, and the abundance of coral benthos on live coral and coral disease levels. Lembeh Island is dominated by hard corals measuring between 5.1 to 25 cm (size M) by ~46%, then sizes more than 25 cm (size L) by ~37% and juvenile size (size S) by 17%. The coral health index on Lembeh Island has the capacity to recover if the condition of coral cover is minimally maintained or improved and lower fleshy seaweed cover by increasing the abundance and biomass of herbivorous fish.
Comparing Central Aortic Pressures Obtained Using a SphygmoCor Device to Pressures Obtained Using a Pressure Catheter
Authors: Esposito Cara , Machado Priscilla , Cohen Ira S , Mehrotra Praveen , Savage Michael , Fischman David , Davis Marguerite , Ruggiero Nicholas , Walinsky Paul , McDonald Maureen E , Dickie Kristopher , Forsberg Flemming , Dave Jaydev K 
Published: January 2022

Abstract

BACKGROUND
This study compared aortic pressures estimated using a SphygmoCor XCEL PWA device (ATCOR, Naperville, IL) noninvasively with aortic pressures obtained using pressure catheters during catheterization procedures and analyzed the impact of a linear-fit function on the estimated pressure values.

METHODS
One hundred and thirty-six patients scheduled for cardiac catheterization procedure were enrolled in IRB approved studies. Catheterization procedures were performed according to standard-of-care to acquire aortic pressure measurements. Immediately after the catheterization procedure with the pressure catheters removed, while the patients were still in the catheterization laboratory, central aortic pressures were estimated with the SphygmoCor device (using its inbuilt transfer function). The error between measured and estimated aortic pressures was evaluated using Bland–Altman analysis (n = 93). A linear-fit was performed between the measured and estimated pressures, and using the linear equation the error measurements were repeated. A bootstrap analysis was performed to test the generalizability of the linear-fit function. In a subset of cases (n = 13), central aortic pressure values were also obtained using solid-state high-fidelity catheters (Millar, Houston, TX), and the error measurements were repeated.

RESULTS
The magnitude of errors between the measured and estimated aortic pressures (mean errors &gt;6.4 mm Hg; mean errors &gt;8.0 mm Hg in the subset) were reduced to less than 1 mm Hg after using the linear-fit function derived in this study.

CONCLUSIONS
For the population examined in this study, the SphygmoCor data must be used with the linear-fit function to obtain aortic pressures that are comparable to the measurements obtained using pressure catheters.

CLINICAL TRIALS REGISTRATION
Trial Numbers NCT03243942 and NCT03245255.
A calcium-sensitive antibody isolates soluble amyloid-β aggregates and fibrils from Alzheimer’s disease brain
Authors: Stern Andrew M , Liu Lei , Jin Shanxue , Liu Wen , Meunier Angela L , Ericsson Maria , Miller Michael B , Batson Megan , Sun Tingwan , Kathuria Sagar , Reczek David , Pradier Laurent , Selkoe Dennis J 
Published: January 2022

AbstractAqueously soluble oligomers of amyloid-β peptide may be the principal neurotoxic forms of amyloid-β in Alzheimer’s disease, initiating downstream events that include tau hyperphosphorylation, neuritic/synaptic injury, microgliosis and neuron loss. Synthetic oligomeric amyloid-β has been studied extensively, but little is known about the biochemistry of natural oligomeric amyloid-β in human brain, even though it is more potent than simple synthetic peptides and comprises truncated and modified amyloid-β monomers. We hypothesized that monoclonal antibodies specific to neurotoxic oligomeric amyloid-β could be used to isolate it for further study.Here we report a unique human monoclonal antibody (B24) raised against synthetic oligomeric amyloid-β that potently prevents Alzheimer’s disease brain oligomeric amyloid-β-induced impairment of hippocampal long-term potentiation. B24 binds natural and synthetic oligomeric amyloid-β and a subset of amyloid plaques, but only in the presence of Ca2+. The amyloid-β N terminus is required for B24 binding. Hydroxyapatite chromatography revealed that natural oligomeric amyloid-β is highly avid for Ca2+. We took advantage of the reversible Ca2+-dependence of B24 binding to perform non-denaturing immunoaffinity isolation of oligomeric amyloid-β from Alzheimer’s disease brain-soluble extracts.Unexpectedly, the immunopurified material contained amyloid fibrils visualized by electron microscopy and amenable to further structural characterization. B24-purified human oligomeric amyloid-β inhibited mouse hippocampal long-term potentiation. These findings identify a calcium-dependent method for purifying bioactive brain oligomeric amyloid-β, at least some of which appears fibrillar.
Developmental course of the repetition effect and change detection responses from infancy through childhood: a longitudinal study
Authors: Deguire Florence , López-Arango Gabriela , Knoth Inga Sophia , Côté Valérie , Agbogba Kristian , Lippé Sarah 
Published: January 2022

Abstract
Neuronal repetition effect (repetition suppression and repetition enhancement) and change detection responses are fundamental brain responses that have implications in learning and cognitive development in infants and children. Studies have shown altered neuronal repetition and change detection responses in various clinical populations. However, the developmental course of these neuronal responses from infancy through childhood is still unknown. Using an electroencephalography oddball task, we investigate the developmental peculiarities of repetition effect and change detection responses in 43 children that we followed longitudinally from 3 months to 4 years of age. Analyses were conducted on theta (3–5 Hz), alpha (5–10 Hz), and beta (10–30 Hz) time–frequency windows. Results indicated that in the theta time–frequency window, in frontocentral and frontal regions of the brain, repetition and change detection responses followed a U-shaped pattern from 3 months to 4 years of age. Moreover, the change detection response was stronger in young infants compared to older children in frontocentral regions, regardless of the time–frequency window. Our findings add to the evidence of top–down modulation of perceptual systems in infants and children.
Fever of Unknown Origin and Incidence of Cancer
Authors: Søgaard Kirstine K , Farkas Dóra Körmendiné , Leisner Michelle Z , Schmidt Sigrun Alba Johannesdottir , Lash Timothy L , Sørensen Henrik Toft 
Published: January 2022

Abstract

Background
Diagnostic tools for determining causes of fever of unknown origin (FUO) have improved over time. We examined if cancer incidence among these patients changed over a 20-year period.

Methods
Population-based cohort study using nationwide Danish registries. We identified individuals diagnosed with FUO (1998-2017) to quantify their excess risk of cancer compared with the general population. Follow-up for cancer started 1 month after FUO. We computed absolute risks and standardized incidence ratios (SIRs) of cancer, and mortality rate ratios adjusted for age, sex, and cancer stage.

Results
Among 6620 patients with FUO (46.9% male; median age: 39 years), 343 were diagnosed with cancer (median follow-up: 6.5 years). The 1- to &lt;12-month risk was 1.2%, and the SIR was 2.3 (95% CI, 1.8–2.9). The increased 1- to &lt;12-month SIR was mainly due to an excess of Hodgkin lymphoma (SIR = 41.7) non-Hodgkin lymphoma (SIR = 16.1), myelodysplastic syndrome/chronic myeloproliferative diseases (SIR = 6.0), lower gastrointestinal cancer (SIR = 3.3), and urinary tract cancer (SIR = 2.9). Beyond 1-year follow-up, malignant melanoma, hepatobiliary tract/pancreatic cancer, and brain/CNS/eye cancer were diagnosed more often than expected. The 1- to &lt;12-month cancer SIR attenuated over time, and for the 2013–2017 period we found no excess risk. Patients diagnosed with cancer ≤1 year after FUO had similar mortality to cancer patients without this diagnosis.

Conclusions
Patients with FUO have a higher 1- &lt;12-month cancer SIR; thereafter, the incidence for most cancers equals that of the general population. Decreasing SIRs over time suggests improvements in the initial diagnostic workup for FUO.