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Funding: Horizon 2020 Framework Programme - 838885; Leverhulme Trust - RPG-2016-047; Engineering and Physical Sciences Research Council - EP/L017008, EP/P010482/1; Fonds de la Recherche Scientifiques de Belgique - 2.5020.11; Walloon Region - n1117545; Fonds De La Recherche Scientifique - FNRS - F.4534.21. Two multiresonant thermally activated delayed fluorescence (MR-TADF) emitters are presented and it is shown how further borylation of a deep-blue MR-TADF emitter, DIDOBNA-N, both blueshifts and narrows the emission producing a new near-UV MR-TADF emitter, MesB-DIDOBNA-N, are shown. DIDOBNA-N emits bright blue light (ΦPL = 444 nm, FWHM = 64 nm, ΦPL = 81%, τd = 23 ms, 1.5 wt% in TSPO1). The deep-blue organic light-emitting diode (OLED) based on this twisted MR-TADF compound shows a very high maximum external quantum efficiency (EQEmax) of 15.3% for a device with CIEy of 0.073. The fused planar MR-TADF emitter, MesB-DIDOBNA-N shows efficient and narrowband near-UV emission (λPL = 402 nm, FWHM = 19 nm, ΦPL = 74.7%, τd = 133 ms, 1.5 wt% in TSPO1). The best OLED with MesB-DIDOBNA-N, doped in a co-host, shows the highest efficiency reported for a near-UV OLED at 16.2%. With a CIEy coordinate of 0.049, this device also shows the bluest EL reported for a MR-TADF OLED to date. Publisher PDF

Time-ordered data (TOD) from ground-based CMB experiments are generally filtered before map-making to remove or reduce the contamination from the ground and the atmospheric emissions. However, when the observation region contains strong point sources, the filtering process will result in considerable leakage around the point sources in a measured CMB map, and leave spurious polarization signals. Therefore, such signals need to be assessed and removed before CMB science exploitation. In this work, we present a new method that we call "template fitting" and can effectively remove these leakage signals in pixel domain, not only satisfying the requirement for measuring primordial gravitational waves from CMB-$B$ modes, but also avoiding time-consuming operations on TOD., 13pages, 6 figures, 5 tables

Tropical rainforests in the central hilly section of Hainan Island are the source of the Nandu, Changhua, and Wanquan rivers, which are crucial for water conservation and ecological protection. The quantitative assessment of water yield in the three basins is beneficial for developing regional water resource protection plans, establishing ecological compensation mechanisms, and maintaining ecological balance. Based on land use data from five periods between 1980 and 2020, this paper adopts the InVEST model and geographic detectors to investigate the spatial-temporal variation characteristics and driving factors of water yield in three major basins of Hainan Island. The results demonstrate that forestland, which makes up more than 70% of the total area in the three basins of Hainan Island, is the predominant land use type. With a depth of 1269.18 mm, Wanquan Basin is the deepest of the three basins, followed by Nandu Basin and Changhua Basin. The total water yield of three basins shows a slightly decreasing trend from 17.991 billion m3 in 1980 to 17.864 billion m3 in 2020. The spatial distribution of water yield is high in the southeast region and low in the northwest region, with strong autocorrelation and significant aggregation. According to geographic detection, land use type is the dominant factor for the spatial differentiation of water yield in the three basins, with a contribution rate of 0.563, and soil type and annual precipitation are important impact factors. The interaction and synergy of soil types and land use types jointly affect the spatial differentiation of water yield in the basin. The results of this study can provide data support and scientific references for biodiversity conservation and ecosystem restoration in the three major basins of Hainan Island.

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Additional file 1.

Knee osteoarthritis (KOA) is characterized by the degen- eration of joint cartilage,which has become a major factor in disability for elderly people.Total knee arthroplasty (TKA) is one of the common surgeries for advanced KOA patients.Rehabilitation after total knee arthroplasty is of great significance to the recovery of knee joint function.Transcutaneous Electrical Acupoint Stimulation(TEAS) may have a beneficial effect on rehabilitation after total knee arthroplasty,however relevant evidence remains uncertain.The purpose of this paper is to systematically evaluate the evidence of TEAS for rehabilitation after total knee arthroplasty.

The peculiar velocities of dark matter halos are crucial to study many issues in cosmology and galaxy evolution. In this study, by using the state-of-the-art deep learning technique, a UNet-based neural network, we propose to reconstruct the peculiar velocity field from the redshift-space distribution of dark matter halos. Through a point-to-point comparison and examination of various statistical properties, we demonstrate that, the reconstructed velocity field is in good agreement with the ground truth. The power spectra of various velocity field components, including velocity magnitude, divergence and vorticity, can be successfully recovered when $k\lesssim 1.1$ $h/\rm Mpc$ (the Nyquist frequency of the simulations) at about 80% accuracy. This approach is very promising and presents an alternative method to correct the redshift-space distortions using the measured 3D spatial information of halos. Additionally, for the reconstruction of the momentum field of halos, UNet achieves similar good results. Hence the applications in various aspects of cosmology are very broad, such as correcting redshift errors and improving measurements in the structure of the cosmic web, the kinetic Sunyaev-Zel'dovich effect, BAO reconstruction, etc., Comment: 15 pages, 13 figures, 4 tables

The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision. As co-evolution is integral to protein structure prediction, AF2's accuracy is significantly influenced by the depth of multiple sequence alignment (MSA), which requires extensive exploration of a large protein database for similar sequences. However, not all protein sequences possess abundant homologous families, and consequently, AF2's performance can degrade on such queries, at times failing to produce meaningful results. To address this, we introduce a novel generative language model, MSA-Augmenter, which leverages protein-specific attention mechanisms and large-scale MSAs to generate useful, novel protein sequences not currently found in databases. These sequences supplement shallow MSAs, enhancing the accuracy of structural property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter can generate de novo sequences that retain co-evolutionary information from inferior MSAs, thereby improving protein structure prediction quality on top of strong AF2.

Supplemental figures and tables

Additional file 1: Figure S1. Test result for publication bias A. Begg’s test (P = 0.13); B. Egger’s test (P = 0.36); C. Sensitivity analysis of the hazard ratio of overall survival. Table S1. The Newcastle-Ottawa scale quality of included studies. Table S2. The summary results about 5-year overall survival in patients receiving AC in < 8 weeks and that in patients who started the AC in > 8 weeks. Table S3. The summary results about 5-year relapse-free survival in patients receiving AC in < 8 weeks and that in patients who started the AC in > 8 weeks. Table S4. The summary results about 5-year overall survival in patients receiving AC in ≤ 4 weeks and that in patients who started the AC in 5-8 weeks.

Vision Transformer (ViT) has performed remarkably in various computer vision tasks. Nonetheless, affected by the massive amount of parameters, ViT usually suffers from serious overfitting problems with a relatively limited number of training samples. In addition, ViT generally demands heavy computing resources, which limit its deployment on resource-constrained devices. As a type of model-compression method, model binarization is potentially a good choice to solve the above problems. Compared with the full-precision one, the model with the binarization method replaces complex tensor multiplication with simple bit-wise binary operations and represents full-precision model parameters and activations with only 1-bit ones, which potentially solves the problem of model size and computational complexity, respectively. In this paper, we investigate a binarized ViT model. Empirically, we observe that the existing binarization technology designed for Convolutional Neural Networks (CNN) cannot migrate well to a ViT's binarization task. We also find that the decline of the accuracy of the binary ViT model is mainly due to the information loss of the \textbf{Attention} module and the \textbf{Value} vector. Therefore, we propose a novel model binarization technique, called \textbf{G}roup \textbf{S}uperposition \textbf{B}inarization (\textbf{GSB}), to deal with these issues. Furthermore, in order to further improve the performance of the binarization model, we have investigated the gradient calculation procedure in the binarization process and derived more proper gradient calculation equations for GSB to reduce the influence of gradient mismatch. Then, the knowledge distillation technique is introduced to alleviate the performance degradation caused by model binarization. Analytically, model binarization can limit the parameter’s search space during parameter updates while training a model. Therefore, the binarization process can actually play an implicit regularization role and help solve the problem of overfitting in the case of insufficient training data. Experiments on three datasets with limited numbers of training samples demonstrate that the proposed GSB model achieves state-of-the-art performance among the binary quantization schemes and exceeds its full-precision counterpart on some indicators. Code and models are available at https://github.com/IMRL/GSB-Vision-Transformer.

Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, with a global prevalence of 2-7% in children and 2.5% in adults.1 Pharmacological treatment is a key component of evidence-based care for individuals with ADHD and the use of ADHD medication has increased markedly across all age groups.2 Stimulants have generally been recommended as first-line pharmacologic treatment and non-stimulants as second line, for school-aged children, adolescents, and adults.2,3 Many randomized controlled trials (RCTs) and observational studies demonstrated short-term beneficial effects of ADHD medications in reducing the core symptoms of ADHD and improving behavioral and neuropsychiatric outcomes (e.g., reducing the risk of injuries, motor vehicle accidents, substance use disorder and improving education outcomes such as test scores).4 Due to the chronic nature of ADHD, long-term pharmacotherapy is typically required. However, in adults, ADHD often goes untreated and leads to impairments in multiple domains.5 Despite the well-known effectiveness of ADHD medications, treatment discontinuation is very common.6 Nearly 50% of individuals with ADHD discontinue treatment within 2–3 years of starting pharmacologic therapy or do not adhere to treatment guidelines.6,7 Non-adherence to medication among individuals with ADHD is suspected to be a major barrier to positive treatment outcomes. In the short-term, non-adherence could affect the tolerability and efficacy of the treatments; and in the long-term, entail poor outcomes and continued impairments in many domains.8 Although there is a growing literature focusing on ADHD over the life span, relatively little is known about the patterns of treatment as people with ADHD grow older. Moreover, despite largely similar treatment guidelines, the use of ADHD medication varies significantly between countries.2,9 An international comparison of medication discontinuation will reveal how these differences extend into the adult population, and will inform clinical practice. References: 1. Faraone SV, Asherson P, Banaschewski T, et al. Attention-deficit/hyperactivity disorder. Nat Rev Dis Primers 2015; 1: 15020. 2. Cortese S. Pharmacologic Treatment of Attention Deficit-Hyperactivity Disorder. The New England journal of medicine 2020; 383(11): 1050-6. 3. Chan E, Fogler JM, Hammerness PG. Treatment of attention-deficit/hyperactivity disorder in adolescents: a systematic review. Jama 2016; 315(18): 1997-2008. 4. Chang Z, Ghirardi L, Quinn PD, Asherson P, D’Onofrio BM, Larsson H. Risks and Benefits of Attention-Deficit/Hyperactivity Disorder Medication on Behavioral and Neuropsychiatric Outcomes: A Qualitative Review of Pharmacoepidemiology Studies Using Linked Prescription Databases. Biological Psychiatry 2019; 86(5): 335-43. 5. Jain R, Jain S, Montano CB. Addressing Diagnosis and Treatment Gaps in Adults With Attention-Deficit/Hyperactivity Disorder. The primary care companion for CNS disorders 2017; 19(5). 6. Gajria K, Lu M, Sikirica V, et al. Adherence, persistence, and medication discontinuation in patients with attention-deficit/hyperactivity disorder - a systematic literature review. Neuropsychiatr Dis Treat 2014; 10: 1543-69. 7. Adler LD, Nierenberg AA. Review of Medication Adherence in Children and Adults with ADHD. Postgraduate medicine 2010; 122(1): 184-91. 8. Ferrin M, Taylor E. Child and caregiver issues in the treatment of attention deficit–hyperactivity disorder: education, adherence and treatment choice. Future Neurology 2011; 6(3): 399-413. 9. Bachmann CJ, Wijlaars LP, Kalverdijk LJ, et al. Trends in ADHD medication use in children and adolescents in five western countries, 2005–2012. European Neuropsychopharmacology 2017; 27(5): 484-93.

Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Constructing an effective detection model usually requires adequate training data stored in a centralized manner, however, this requirement sometimes could not be satisfied in realistic scenarios. As a prevailing approach to address the above problem, federated learning has demonstrated its power to cooperate with the distributed data available while protecting the privacy of data providers. However, it is still unclear that how existing time series anomaly detection algorithms perform with decentralized data storage and privacy protection through federated learning. To study this, we conduct a federated time series anomaly detection benchmark, named FedTADBench, which involves five representative time series anomaly detection algorithms and four popular federated learning methods. We would like to answer the following questions: (1)How is the performance of time series anomaly detection algorithms when meeting federated learning? (2) Which federated learning method is the most appropriate one for time series anomaly detection? (3) How do federated time series anomaly detection approaches perform on different partitions of data in clients? Numbers of results as well as corresponding analysis are provided from extensive experiments with various settings. The source code of our benchmark is publicly available at https://github.com/fanxingliu2020/FedTADBench., 8 pages, 6 figures, published by IEEE HPCC 2022

ObjectiveTo explore the association between adiposity and plasma folate deficiency odds among women of reproductive age in China.MethodsA cross-sectional survey on nutritional status among women of reproductive aged 18–30 years in 2005–2006 in China was conducted. General adiposity was defined as body mass index (BMI) ≥24 kg/m2, and central adiposity was defined as waist circumference >80 cm. A plasma folate concentration ResultsA total of 3,076 women of reproductive age were included in the final analysis. Compared to women with normal BMI and WC, women with both general and central adiposity had the highest odds for plasma folate deficiency (OR = 3.107, 95% CI: 1.819–5.307). Women with exclusively central adiposity had excess odds for plasma folate deficiency (WC > 80 cm, BMI 2; OR = 2.448, 95% CI: 1.144–5.241), which was higher than women with exclusively general adiposity (BMI ≥ 24 kg/m2, WC ≤ 80 cm; OR = 1.709, 95% CI: 1.259–2.319). The combined use of BMI and WC can detect more women (11.7%) at higher plasma folate deficiency odds than either used alone.ConclusionsWomen with central adiposity in normal weight have higher odds for plasma folate deficiency than those with general obesity only. Early screening for central adiposity among women of reproductive age would be meaningful to prevent folate deficiency and improve life-cycle health.

Intracerebral hemorrhage (ICH) is the most devastating subtype of stroke, but effective prevention and treatment strategies are lacking. Recently, gut microbiome and its metabolitesis are considered to be an influencing factor of stroke. However, little is known about the effects of the gut microbiome on ICH and host metabolic activity. Therefore, we used 16S sequencing, macrogenomics sequencing and untargeted metabolomics to explore the differences in gut microbial-metabolome interactions between patients with intracerebral hemorrhage and healthy control populations. We found a significant decrease in the phylum of Firmicutes and a significant increase of Bacteroidetes in ICH patients. At the genus level, Streptococcus, Bifidobacterium, Akkermansia, and Lactobacillus were more abundant in ICH patients. Macrogenomic analysis revealed active glycosaminoglycan degradation, heme synthesis, galactose degradation, lipopolysaccharide core region synthesis, and beta-Lactam resistance in ICH patients. Serum untargeted metabolomic analysis combined with ROC curves showed that octanoylcarnitine, decanoylcarnitine, dodecanoylcarnitine, glyceric acid, pyruvic acid, aspartic acid, methylcysteine, pyroglutamic acid, 9E-tetradecenoic acid, N-Acetylneuraminic acid, and aconitic acid were the best markers for the diagnosis of ICH. Correlation analysis showed that microbiome enriched in the gut of ICH patients were significantly correlated with serum metabolites, revealing a close correlation between the gut microbiome of ICH patients and the host metabolome, and significant differences from the healthy population. microbiota-host co-metabolites including pyruvic acid and 9E-tetradecenoic acid is associated with the the National Institutes of Health Stroke Scale (NIHSS) scores. In conclusion, microbiome-related metabolites in ICH patients was associated with the severity of ICH, the microbiota-host co-metabolites may be a potential may be potential therapeutic targets.

The dual-buck inverter (DBI) does not have a shoot-through problem, and thus, the harmonics caused by the dead-time can be avoided. Besides, the DBI of half-cycle control can reduce the inductor current. However, half-cycle control would cause zero-crossing distortion (ZCD). This article discusses the working principle of DBI and the cause of ZCD. ZCD would lead to grid current distortion, which can be extracted by a high pass filter (HPF). Therefore, the negative feedback of grid current harmonic components can inhibit ZCD. In this article, the half-cycle control method without ZCD is proposed. The logic of half-cycle control is analyzed in detail. The ZCD formation process and the inhibition method is given by the quantitative switching model analysis. What is more, the influence on the control system of the method is discussed. Finally, a simulation model and a prototype of the three-phase DBI have been built to verify the theoretical analysis.

In order to improve the weeding efficiency and protect farm crops, accurate and fast weeds removal guidance to agricultural mobile robots is an utmost important topic. Based on this motivation, we propose a time-efficient quadratic traversal algorithm for the removal guidance of weeds around the recognized corn in the field. To recognize the weeds and corns, a Faster R-CNN neural network is implemented in real-time recognition. Then, an ultra-green characterization (EXG) hyperparameter is used for grayscale image processing. An improved OTSU (IOTSU) algorithm is proposed to accurately generate and optimize the binary image. Compared to the traditional OTSU algorithm, the improved OTSU algorithm effectively shortens the search speed of the algorithm and reduces the calculation processing time by compressing the range of the search grayscale range. Finally, based on the contour of the target plants and the Canny edge detection operator, the shortest weeding path guidance can be calculated by the proposed quadratic traversal algorithm. The experimental results proved that our search success rate can reach 90.0% on the testing date. This result ensured the accurate selection of the target 2D coordinates in the pixel coordinate system. Transforming the target 2D coordinate point in the pixel coordinate system into the 3D coordinate point in the camera coordinate system as well as using a depth camera can achieve multitarget depth ranging and path planning for an optimized weeding path.

MicroRNAs (miRNAs) are a class of gene expression regulators that participate in the occurrence and development of hepatocellular carcinoma (HCC), although the underlying mechanism by which they function in HCC has not been fully elucidated. Here, small RNA deep sequencing was used to identify aberrantly expressed miRNAs in HCC tissues, and a novel miRNA named miR-HCC2 was identified. RT-qPCR analysis demonstrated that miR-HCC2 displayed higher expression in HCC tissues than in adjacent non-tumor tissues. We documented that miR-HCC2 facilitated the growth, migration and invasion of HCC cells by accelerating cell cycle progression, incressing the expression of epithelial-to-mesenchymal transition (EMT)-associated marker vimentin but decreasing the expression of E-cadherin. MiR-HCC2 directly targeted the 3' UTR of BAMBI and ELMO1 and up-regulated their expression. Both BAMBI and ELMO1 had the same patterns of expression with miR-HCC2 in HCC tissues. Additionally, blocking BAMBI or ELMO1 counteracted the phenotypic alterations elicited by miR-HCC2. Collectively, our investigation identified miR-HCC2 as a new positive modulator of HCC aggressiveness that may serve as a potential biomarker for the development of diagnostic and therapeutic approaches for HCC.

BackgroundUnhealthy eating habits are a significant modifiable risk factor for cardiovascular diseases (CVDs); nevertheless, no evidence of their impact on the CVD burden has been reported in East Asian countries. We aimed to determine the trend and predict the future CVDs burden attributed to modifiable dietary risk factors in the East Asian countries (China, Japan, South Korea, and North Korea) and the world.MethodsThe Global Burden of Disease (GBD) 2019 data were used to find the trend of CVDs [ischemic heart disease (IHD) and ischemic stroke (IS)] mortality- and disability-adjusted life-years (DALYs) attributed to dietary risk factors in the East Asian countries and the world (1990–2019) and its prediction from 2020 to 2030. We used the joinpoint regression model and the autoregressive integrated moving average (ARIMA) model for trend and future forecast, respectively.ResultsFrom 1990 to 2019, regardless of sex, the age-standardized mortality rate (ASMR) and DALYs of IHD attributed to dietary risk factors significantly decreased in Japan, South Korea, and the world. However, the ASMR of IHD significantly increased in Chinese males and for both sexes in North Korea. The ASMR and DALYs of IHD and IS due to dietary risk factors were higher in males than in females in the world. From 2020 to 2030, the ASMR of IHD is predicted to increase in South Korean females and Japanese males. Globally, a diet low in whole grains was the top risk factor for the highest IHD mortality and DALYs in 2019, followed by a diet low in legumes and a diet high in sodium. A diet low in whole grains, a diet high in sodium, and a diet low in legumes were the leading risk factors for high IHD mortality in East Asian countries.ConclusionThe trend of IHD and IS burden due to dietary risk factors varies substantially across the East Asian countries compared to the trend of CVDs burden in the world. The study findings may help the public health policymakers to design proper strategies for improvement of the quality of life to combat the CVDs burden in the future for the East Asian countries.

BackgroundMental health issue among college students is routinely a major public health concern, and coronavirus disease 2019 (COVID-19) pandemic may have exacerbated the students' mental health issues which include psychological distress, panic disorder, insomnia, and posttraumatic stress symptoms. However, few studies reached a consensus on the impact of COVID-19 fear on mental health among college students. Therefore, we aimed to conduct a systematic review and meta-analysis that quantitatively synthesized the fear among college students during the COVID-19 pandemic.MethodsPubMed, EMBASE, CINAHL, and PsycINFO electronic databases were systematically searched to identify cross-sectional study reporting the state of COVID-19 fear examined by the Fear of COVID-19 Scale (FCV-19S) published up until November 20, 2021. Methodological quality was complied with the evaluation criteria of the Agency for Healthcare Research and Quality. The random effects model was employed to estimate the pooled mean of FCV-19S score. Subgroup analysis and meta-regression analysis were also conducted. Publication bias was assessed by Begg's test and funnel plot.ResultsA total of 16 studies with a sample size of 11,872 were included. A pooled mean of FCV-19S score was 17.60 [95% confidence interval (CI): 16.41–18.78]. The mean of COVID-19 fear in women (17.11, 95% CI: 16.59–17.64) was higher than that in men (15.21, 95% CI: 14.33–16.08). The highest and lowest pooled means of FCV-19S score were observed in the studies conducted in multiple countries that include Israel, Russian, and Belarus (21.55, 95% CI: 20.77–22.33) and in Europe (16.52, 95% CI: 15.26–17.77), respectively. No significant publication bias was detected by Begg's test.ConclusionsCollege students experienced a moderate level of fear caused by COVID-19 pandemic. It is necessary to design and implement prevention programs that target the mental health of college students.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021287017, identifier: CRD42021287017.

In order to explore the variation of crumb rubber modified asphalt (CR) before and after aging at the micro- and nanoscales, the molecular dynamics simulation and atomic force microscopy (AFM) asphalt microstructure scanning were carried out on CR before and after aging. The molecular dynamics energy, radial distribution function (RDF), surface microstructure image, roughness, and other test results of the CR before and after aging were compared and analyzed. The results show that the molecular dynamic energy of the CR after aging increases; the asphaltene-asphaltene RDF is decreased after aging; and the AFM surface microstructure images of asphalt before and after aging do not change significantly, but quantitative analysis by roughness theory shows that aging makes the microscopic surface of the CR more uniform and gentle. By analyzing the changes of asphalt properties before and after aging at two scales, it can be found that there is a certain relationship between the properties obtained at different scales. The reasons and mechanisms for the influence of microstructure on aging are obtained by analyzing this relationship.

Thin films of TiN are deposited onto Zr–1% Nb alloys using vacuum-arc plasma in two steps. First, the plasma-immersion ion implantation (PIII) of titanium is implemented; then, without interrupting the experiment, the vacuum-arc deposition (VAD) of TiN is carried out. The VAD and PIII modes for obtaining the most effective coating (from the viewpoint of protecting the substrate from corrosion and hydrogen embrittlement) have been previously determined. Within studies of the mechanical properties of the obtained coatings, a study of changes in the adhesion of coatings under Arctic conditions (at a temperature of –20°C, seawater ice) and hydrogenation of the samples is carried out. It is established that coatings that are frozen in seawater ice for 24 hours without preliminary saturation with hydrogen have the best adhesive strength. The mechanism of the established effect is briefly discussed.

Because cervical histopathology image analysis plays a very importation role in the cancer diagnosis and medical treatment processes, since the 1980s, more and more effective machine vision techniques are introduced and applied in this field to assist histopathologists to obtain a more rapid, stable, objective, and quantified analysis result. To discover the inner relation between the visible images and the actual diseases, a variety of machine vision techniques are used to help the histopathologists to get to know more properties and characteristics of cervical tissues, referring to artificial intelligence, pattern recognition, and machine learning algorithms. Furthermore, because the machine vision approaches are usually semi- or full-automatic computer based methods, they are very efficient and labour cost saving, supporting a technical feasibility for cervical histopathology study in our current big data age. Hence, in this article, we comprehensively review the development history of this research field with two crossed pipelines, referring to all related works since 1988 till 2020. In the first pipeline, all related works are grouped by their corresponding application goals, including image segmentation, feature extraction, and classification. By this pipeline, it is easy for histopathologists to have an insight into each special application domain and find their interested applied machine vision techniques. In the second pipeline, the related works on each application goals are reviewed by their detailed technique categories. Using this pipeline, machine vision scientists can see the dynamic of technological development clearly and keep up with the future development trend in this interdisciplinary field.

Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. Several data augmentation methods have been proposed to alleviate this problem. However, prior work only leveraged superficial grammar or rules for data augmentation, which resulted in limited improvement. We propose to use subtree substitution for compositional data augmentation, where we consider subtrees with similar semantic functions as exchangeable. Our experiments showed that such augmented data led to significantly better performance on SCAN and GeoQuery, and reached new SOTA on compositional split of GeoQuery., 6 pages

Additional file 1. Table S1: Gene papA sequences used in this study; Figure S1: Inhibition of supernatant of Lactobacillus plantarum Zhang-LL on Bacillus subtilisWB800N, E.coli K99, and Listeria monocytogenes ATCC54003; Figure S2: Inhibition of supernatant of various concentration of IPTG and induction time on L. monocytogenes ATCC54003; Figure S3: Gel diffusion assay of batch fermentation supernatant on L. monocytogenesATCC54003. Figure S4: Scan electron microscope of L. monocytogenes ATCC54003.

Simile interpretation (SI) and simile generation (SG) are challenging tasks for NLP because models require adequate world knowledge to produce predictions. Previous works have employed many hand-crafted resources to bring knowledge-related into models, which is time-consuming and labor-intensive. In recent years, pre-trained language models (PLMs) based approaches have become the de-facto standard in NLP since they learn generic knowledge from a large corpus. The knowledge embedded in PLMs may be useful for SI and SG tasks. Nevertheless, there are few works to explore it. In this paper, we probe simile knowledge from PLMs to solve the SI and SG tasks in the unified framework of simile triple completion for the first time. The backbone of our framework is to construct masked sentences with manual patterns and then predict the candidate words in the masked position. In this framework, we adopt a secondary training process (Adjective-Noun mask Training) with the masked language model (MLM) loss to enhance the prediction diversity of candidate words in the masked position. Moreover, pattern ensemble (PE) and pattern search (PS) are applied to improve the quality of predicted words. Finally, automatic and human evaluations demonstrate the effectiveness of our framework in both SI and SG tasks., Comment: Long paper accepted at ACL 2022

Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners. To this end, we introduce a simple, efficient, and effective two-stage detector, termed as Ret3D. At the core of Ret3D is the utilization of novel intra-frame and inter-frame relation modules to capture the spatial and temporal relations accordingly. More Specifically, intra-frame relation module (IntraRM) encapsulates the intra-frame objects into a sparse graph and thus allows us to refine the object features through efficient message passing. On the other hand, inter-frame relation module (InterRM) densely connects each object in its corresponding tracked sequences dynamically, and leverages such temporal information to further enhance its representations efficiently through a lightweight transformer network. We instantiate our novel designs of IntraRM and InterRM with general center-based or anchor-based detectors and evaluate them on Waymo Open Dataset (WOD). With negligible extra overhead, Ret3D achieves the state-of-the-art performance, being 5.5% and 3.2% higher than the recent competitor in terms of the LEVEL 1 and LEVEL 2 mAPH metrics on vehicle detection, respectively.

Supplemental Document (revised)

Topological systems are inherently robust to disorder and continuous perturbations, resulting in dissipation-free edge transport of electrons in quantum solids, or reflectionless guiding of photons and phonons in classical wave systems characterized by topological invariants. Despite considerable efforts, direct experimental demonstration of theoretically predicted robust, lossless energy transport in topological insulators operating at terahertz frequencies is needed further investigations to shed affirmative light on the unique properties enabled by topological protection. Here, we introduce Kagome lattice that exhibits a new class of symmetry-protected topological phases with very low Berry curvature but nontrivial bulk polarization, and fabricate an optical topological insulator that provide the valley hall effect. Theoretical analysis show that four type edge states can be obtained. Measurements of THz-TDs with high time-resolution demonstrate that terahertz wave propagating along the straight topological edge and Z-shape edge with sharp turns have almost same high transmission in 0.440 THz to 0.457 THz domain range. Those results quantitatively illustrate the suppression of backscattering due to the non-trivial topology of the structure. The THz-TDs measurement yields amplitude and phase information, showing significant advantage compared to general broadband infrared, single wavelength continuous-wave THz measurements and visible spectroscopy. It allows further exploration of the effective refractive index, group velocity and dispersion relations of edge states. Our work offers possibilities for advanced control of the propagation and manipulation of THz waves, and facilitates the applications including sixth-generation (6G) wireless communication, terahertz integrated circuits, and interconnects for intrachip and interchip communication.

Table S1 and Figures S1-S3

The uncertainty in the photometric redshift estimation is one of the major systematics in weak lensing cosmology. The self-calibration method is able to reduce this systematics without assuming strong priors. We improve the recently proposed self-calibration algorithm to enhance the stability and robustness with the noisy measurement. The improved algorithm is tested on the power spectra measured from the simulated catalogues constructed according to DECaLS DR8 photometric catalogue. For the fiducial analysis with 5 equal-width redshift bins over $0, Comment: 15 pages, 13 figures, accepted for publication in MNRAS

Aesthetic assessment of images can be categorized into two main forms: numerical assessment and language assessment. Aesthetics caption of photographs is the only task of aesthetic language assessment that has been addressed. In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images. If we give a question of images aesthetics, model can predict the answer. We use images from \textit{www.flickr.com}. The objective QA pairs are generated by the proposed aesthetic attributes analysis algorithms. Moreover, we introduce subjective QA pairs that are converted from aesthetic numerical labels and sentiment analysis from large-scale pre-train models. We build the first aesthetic visual question answering dataset, AesVQA, that contains 72,168 high-quality images and 324,756 pairs of aesthetic questions. Two methods for adjusting the data distribution have been proposed and proved to improve the accuracy of existing models. This is the first work that both addresses the task of aesthetic VQA and introduces subjectiveness into VQA tasks. The experimental results reveal that our methods outperform other VQA models on this new task., Comment: 13 pages, 7 figures, on going research

1. Morphometrics are fundamental for the analysis of size and shape in fossils, particularly because soft parts or DNA are rarely preserved and hard parts such as shells are commonly the only source of information. Geometric morphometrics, that is, landmark analysis, is well established for the description of shape but it exhibits a couple of shortcomings resulting from subjective choices during landmarking (number and position of landmarks) and from difficulties in resolving shape at the level of micro-sculpture. 2. With the aid of high-resolution 3D scanning technology and analyses of fractal dimensions, we test whether such shortcomings of linear and landmark morphometrics can be overcome. As a model group, we selected a clade of modern viviparid gastropods from Lake Lugu, with shells that show a high degree of sculptural variation. Linear and landmark analyses were applied to the same shells in order to establish the fractal dimensions. The genetic diversity of the gastropod clade was assessed. 3. The genetic results suggest that the gastropod clade represents a single species. The results of all morphometric methods applied are in line with the genetic results, which is that no specific morphotype could be delimited. Apart from this overall agreement, landmark and fractal dimension analyses do not correspond to each other but represent data sets with different information. Generally, the fractal dimension values quantify the roughness of the shell surface, the resolution of the 3D scans determining the level. In our approach, we captured the micro-sculpture but not the first-order sculptural elements, which explains that fractal dimension and landmark data are not in phase. 4. We can show that analyzing fractal dimensions of gastropod shells opens a window to more detailed information that can be considered in evolutionary and ecological contexts. We propose that using low-resolution 3D scans may successfully substitute landmark analyses because it overcomes the subjective landmarking. Analyses of 3D scans with higher resolution than used in this study will provide surface roughness information at the mineralogical level. We suggest that fractal dimension analyses of a combination of differently resolved 3D models will significantly improve the quality of shell morphometrics.

The wave of digital reform boosts the layout and construction of university informatization. However, in view of the current problems of university informatization construction, such as inadequate overall planning of system construction, ineffective interoperability of some business system data, high construction cost, and unclear division of responsibilities of system management, the article explores a general idea of digital reform based on the reality of “information islands” and “data chimney” in school education big data, In combination with the actual situation of the school, it has planned and put into practice the idea of the school’s digital reform, built the school’s “digital intelligent medical university” information ecosystem, designed and formed the “1614” digital reform work theoretical system from the top planning, consolidated the construction of the school’s education big data warehouse, committed to data collection, governance and sharing, gathered data resources, and formed a brain-like system to help teaching decision-making, Build a “cloud” supported teaching resource supply system to help borderless teaching, and finally build a personalized teaching ecological model based on education big data.

Deep convolutional networks have become ubiquitous for image segmentation. But relatively little is known on how to leverage unlabeled data to further improve their performance. Here we introduce a new approach named as Self-supervised deep learning for Segmentation (SeSe-Net) that uses a ‘Worker’ ( W ) neural network to segment input images and a ‘Supervisor’ ( S ) neural network to evaluate the quality of the segmentation results. We further propose a two-stage training process. In the first stage, W learns to segment with standard dataset and generates training sets for S . Based on this, S learns how well the W performs for the segmentation task. In the second stage, S transfers the knowledge from the first stage to supervise the learning process of W on unlabeled datasets towards better segmentation performance. We show that SeSe-Net leads to a significant performance boosting with additional unlabeled data. Furthermore, with less than 5% of labeled training dataset, SeSe-Net performs on par with challenging baseline trained on full training dataset in terms of Dice Metric (DM) on three different datasets. Theoretical analysis is also given to establish the convergence of SeSe-Net.

As an acute zoonosis, rabies has a fatality rate of nearly 100%. Since rabies surveillance data over the years show that the rabies epidemic areas in China expand from southern provinces to central and northern provinces, we selected Hubei Province, an area with a high incidence of rabies in Central China, to investigate national rabies prevention and control work by developing a visual analysis platform (VAP-HRHB: http://www.combio-lezhang.online/rabies/index.html). VAP-HRHB employs bioinformatics methods to predict the future developmental trend for rabies incidence and exposure numbers, thereby comprehensively improving the prevention and control of infectious diseases in China.