Twenty years of Chinese vascular plant novelties,2000 through 2019

From 2000 to 2019,11,895 new names or new additions to the Chinese vascular flora were proposed by 4226 individuals(4086 articles and 140 books),as documented in the Chinese Plant Names Index(CPNI).During those 20 years,4407 new taxa of vascular plants were described from China,including 7 new families,132 new genera,3543 new species,68 new subspecies,497 new varieties and 160 new forms.Additionally,3562 new combinations and names at new rank and 306 new replacement names were also proposed.Among these various new names were 150 invalid names and 108 illegitimate names,including some that have not been resolved.Six hundred and forty three vascular plants were reported as new to China,while 2349 names were reduced to synonyms of 1406 taxa.The data show that the Chinese flora increased in size at the rate of about 200 taxa annually during those years.Despite the increased attention given to biodiversity in recent years,the evidence indicates that a large number of species in China have yet to be discovered.Further basic investigation of the Chinese flora is needed.Additionally,in the past two decades only 8.5%of the newly published species have been based on molecular evidence,but in the past five years such data have increased significantly,reaching about 20%.Molecular data will undoubtedly become increasingly significant in the discovery of new species in the coming years.Yunnan,Guangxi,Sichuan,Xizang and Taiwan were important sources of new discoveries,with more than 3300 new taxa and records from these five provinces.By area,Taiwan and Hainan,two islands in southern China,have the highest density of newly discovered species.Regional plant surveys are still needed,especially in areas in the southwest and on the southern islands.

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Supervised fault diagnosis typically assumes that all the types of machinery failures are known.However,in practice unknown types of defect,i.e.,novelties,may occur,whose detection is a challenging task.In this paper,a novel fault diagnostic method is developed for both diagnostics and detection of novelties.To this end,a sparse autoencoder-based multi-head Deep Neural Network(DNN)is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data.The detection of novelties is based on the reconstruction error.Moreover,the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function,instead of performing the pre-training and fine-tuning phases required for classical DNNs.The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer.The results show that its performance is satisfactory both in detection of novelties and fault diagnosis,outperforming other state-of-the-art methods.This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect,but also detect unknown types of defects.

How do lateral septum projections to the ventral CA1 influence sociability?

Social dysfunction is a risk factor for several neuropsychiatric illnesses.Previous studies have shown that the lateral septum(LS)-related pathway plays a critical role in mediating social behaviors.Howeve r,the role of the connections between the LS and its downstream brain regions in social behavio rs remains unclea r.In this study,we conducted a three-chamber test using electrophysiological and chemogenetic approaches in mice to determine how LS projections to ventral CA1(vCA1)influence sociability.Our res ults showed that gamma-aminobutyric acid(GABA)-e rgic neuro ns were activated following social experience,and that social behavio rs were enhanced by chemogenetic modulation of these neurons.Moreover,LS GABAergic neurons extended their functional neural connections via vCA1 glutamatergic pyramidal neurons,and regulating LSGABA→vCA1Gluneural projections affected social behaviors,which were impeded by suppressing LSprojecting vCA1 neuronal activity or inhibiting GABAAreceptors in vCA1.These findings support the hypothesis that LS inputs to the vCA1 can control social prefe rences and social novelty behaviors.These findings provide new insights rega rding the neural circuits that regulate sociability.

一种Winnow线性分类器及其在TREC Novelty任务中的应用

文本检索会议(TextREtrievalConference,TREC)是目前国际上信息检索领域最重要的学术交流与国际评测活动。笔者等人代表中科院计算所参加了2003年TREC的Novelty任务。在该任务中,实现了Winnow线性分类器在检测relevant句子和novel句子中的应用。实验表明,这种简单的分类方法表现了较好的性能。

A revision of Dryopteris sect.Diclisodon(Dryopteridaceae)based on morphological and molecular evidence with description of a new species

Dryopteris sect.Diclisodon is a small section of ferns with about 12 species mainly distributed in East Asia.Here,we carried out morphological and phylogenetic analyses of this section.A new species from southwest China,D.gaoligongensis,is described and illustrated.Dryopteris gaoligongensis resembles D.indonesiana and D.sparsa,but differs by having a creeping rhizome and large 4-pinnate fronds.We also show that D.glabrior Ching&Z.Y.Liu is a distinct species;however,because it is a later homonym of D.glabrior Copel.,it should be renamed D.renchangiana.We conclude that a species previously known as D.nitidula,also an illegitimate homonym,should be recognized with a new name,D.sinonepalensis.We resolve the phylogenetic position of D.yoroii as sister to other sampled species of D.sect Diclisodon.Our phylogenetic analyses confirm the distinctiveness of D.gaoligongensis,D.renchangiana,and D.sinonepalensis.A key to species of D.sect Diclisodon in China is provided.

Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders

Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors' knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract LogMel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multilayer perceptron(MLP) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory(LSTM) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine(OC-SVM) algorithm. The performance has been evaluated in terms area under curve(AUC) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OCSVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11 %.

Temperature effect on the structural strains of an ancient Tibetan building based on long-term monitoring data

Many studies have indicated that structural strain will be significantly influenced by temperature variations,and a good understanding of the effect of temperature on structural strain is essential.A structural health monitoring system has been installed in a typical Tibetan timber building to measure the structural strains and ambient temperature since 2012.This paper presents the correlation between temperature and strain data from the monitored structure.A method combining singular spectrum analysis and polynomial regression is proposed for modeling the temperature induced strains in the structure.Singular spectrum analysis is applied to smooth the temperature data,and the correlation between the resulting temperature time series and the measured strains is obtained by polynomial regression.Parameters of the singular spectrum analysis and the regression model are selected to have the least regression error.Results show that the proposed method has both good reproduction and prediction capabilities for temperature induced strains,and that the method is accurate and effective for eliminating the effect of temperature from the measured strain.A standardized Novelty Index based on the residual strain is also used for the condition assessment of the structure.

Diversity, novelty, antimicrobial activity, and new antibiotics of cultivable endophytic actinobacteria isolated from psammophytes collected from Taklamakan Desert

Three hundred and twenty endophytic actinobacterial strains were isolated from psammophytes collected from Taklamakan Desert and identified. Among them, three strains already had been identified as new species of two genera and sixteen isolates showed relatively low 16 S rRNA similarities < 98.6% to validly described species. Seventy-five of the isolates were selected as representative strains to screen antibacterial activity and mechanism. Forty-seven strains showed antagonistic activity against at least one of the indicator bacteria. Two Streptomyces strains produced bioactive compounds inducing DNA damage, and two Streptomyces strains produced bioactive compounds with inhibitory activity on protein biosynthesis. Notably, the strain Streptomyces sp. 8P21H-1 that demonstrated both strong antibacterial activity and inhibitory activity on protein biosynthesis was prioritized for exploring new antibiotics.Under the strategy of integrating genetics-based discovery program and MS/MS-based molecular networking, two new streptogramin-type antibiotics, i.e., acetyl-griseoviridin and desulphurizing griseoviridin, along with known griseoviridin, were isolated from the culture broth of strain 8P21H-1. Their chemical structures were determined by HR-MS, and 1D and 2D NMR. Desulphurizing griseoviridin and griseoviridin exhibited antibacterial activities by inhibiting translation.

Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

Novelty Detection Methods and Novel Fault Class Detection

The ability to detect a new fault class can be a useful feature for an intelligent fault classification and diagnosis system.We adopt two novelty detection methods,the support vector data description (SVDD) and the Parzen density estimation, to represent known fault class samples,and to detect new fault class samples.The experiments on real multi-class bearing fault data show that the SVDD can give both high novelty detection rate and target recognition rate,respectively for the prescribed’ un- known’ fault samples and the known fault samples by choosing the appropriate SVDD algorithm parameters;but the Parzen densi- ty estimation only give a better novelty detection rate in our experiments.

Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current research.In this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is proposed.The edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users.RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender system.We verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.

Sensation/novelty seeking in psychotic disorders:A review of the literature

The evaluation of personality traits is important for the better understanding of the person suffering from psychosis and for treatment individualization. However literature on patients' personality and character in such disorders is limited. The aim of this review was to summarize the literature on sensation/novelty seeking(SNS), a trait which is biologically based and highly heritable and is associated with dopamine activity, and refers to a person's tendency to seek varied, novel, complex, and intense sensations and experiences. A total of 38 studies were included in this review, involving 2808 patients and 2039 healthy controls. There is consistent evidence that this trait is independentlyassociated with alcohol and substance abuse in patients with schizophrenia and related disorders. The estimation of SNS would help clinicians to identify patients at risk for abuse. There is also some evidence that higher SNS levels may relate to medication non-adherence and seem to increase the risk of patients' aggressive and violent behavior, but studies are scarce. SNS was found not to be related to suicidality, whereas in the fields of patients' quality of life and psychopathology results are contradictory, but most studies show no possible association. Several studies suggest that SNS is lower in psychotic patients compared to controls, whereas most yield no differences. The evidence for this trait as a potential endophenotype of schizophrenia is weak. SNS may be implicated in psychotic disorders' course and prognosis in several ways and should be always inquired for. This trait can be reliably measured with the use of easily applicable self-rated instruments, and patients' accounts could inform clinicians when planning management and delivering individualized treatment.

The behaviours of optical novelty filter based on bacteriorhodopsin film

The quality of the novelty filter image is investigated at different intensities of the incident blue and yellow beams irradiating a bacteriorhodopsin （bR） film. The relationship between the transmitted blue beams and the incident yellow beams is established. The results show that the contrast of the novelty filter image depends on the lifetime of longest lived photochemical state （M state）. These results enable one to identify the direction of a moving object and to improve the quality of the novel filter image by prolonging the lifetime of M state.

A Reflection on the Posthuman Human:The Postranshominescent

The essay presents a reflection on the current human condition,the novelty of which reveals itself to be an-already-always-been that continually begins and renews itself in the space of generative and inventive relationality,expressed,in its toti-potentiality,by Posthumanism and by Michel Serres.In this horn of abundance,pre-existing and passing beyond form a single body,seeking their expression in an inchoative composed neologism as postranshominescent.

Envisioning the Profile of Chinese Culture——from the Perspective of the Social Value Theory

For a few decades China has undergone an enduring, drastic and complicated transformation. The structure and form of Chinese culture has been profoundly shaped and re-shaped during this process. With careful analysis of the history and the actualities, this paper presents a sketch of Chinese culture that highlights three significant traits; an elegant temperament featuring both classicality and novelty, an intrinsic value focusing on nature and freedom, and an encompassing structure marked by harmonious diversity. A new Chinese culture has already revealed her profile against the sunglow at the dawn of the new era. Beautiful and promising as it is, Chinese people believe that she deserves being heartily applauded, warmly welcomed, and boldly embraced.

Single Parameter Range and Whole Parameter Range, and Assessment of Novelty

It is a relatively common phenomenon to limit technical features with parameter range in patent claims. It is argued in this article that the parameter range should be distinctly divided into single parameter range and whole parameter range depending on the different mode and function of limitation. Each and every parameter in a single parameter range may independently achieve a technical effect, and limit one embodiment alone; while a single parameter in the whole parameter range cannot independently ac...

How to Use Problem-Based Learning in Classroom

Education at current stage is about developing intelligence.Professor Howard Gardner of Harvard University,one of theforemost psychologists,described intelligence as the ability to solve problems in one's particular context and culture.Many educa-tors support the need to develop the multiple intelligences,but few realize that one of the best ways to draw forth these intelligences is to make use of problem scenarios.Intelligence in the real world involves not only learning how to do things and actually doing them, but also the ability to deal with novelty as well as the capacity to adapt, select and shape our interactions with the environment (Sternberg,1985,1986,1990).Problem-based learning is about harnessing the kinds of intelligences needed in confronting real world challenges:the ability to deal with novelty and complexity (Tan,2000c).Therefore,problem-based learning is becoming one of a necessary approach in educators' mind and practical usage in daily teaching.

High- and Low-Rearing Rats Differ in the Brain Excitability Controlled by the Allosteric Benzodiazepine Site in the GABA_(A) Receptor

Rearing is an exploratory behavior induced by novelty, such as exposure to an open field. Stimulation of certain brain regions, including the hippocampus, induces both rearing and clonic convulsions. Brain excitability is controlled by gamma-aminobutyric acid (GABA) inhibitory neurotransmission through its ionotropic GABAA/allosteric benzodiazepine site. Drugs that decrease GABAA receptor fast inhibitory neurotransmission induce clonic convulsions and rearing when injected into the hippocampus. Therefore, individual differences in rearing behavior may be related to the susceptibility to clonic convulsions, which could involve differences in brain excitability controlled by GABAA/allosteric benzodiazepine site receptors. Adult, male Wistar rats were divided into high- (HR) and low-rearing (LR) groups based on the number of rearings in the open field test. Groups of HR and LR rats were challenged with convulsant drugs that antagonize GABA neurotransmission via different mechanisms of action (3-mercaptopropionic acid, a glutamate decarboxilase inhibitor;bicuculline, a GABAA receptor antagonist;pentylenetetrazol and picrotoxin, both GABAA receptor chloride channel blockers and DMCM, a benzodiazepine inverse agonist). The convulsant doses that induced 50% of clonic convulsions were determined for each drug. The LR rats had a higher susceptibility (a lower convulsant dose 50%) to clonic convulsions induced by DMCM than the HR rats, but there were no differences between the groups in the susceptibility to tonic convulsions induced by the same drug. There were no significant differences in the convulsant dose 50% for clonic convulsions between the groups for all other drugs injected. In another experiment, additional HR and LR rats were injected with a sedative-hypnotic dose of diazepam, which caused a significantly higher hypnotic effect (sleeping-time) in the LR rats than in the HR rats. The LR group was also shown to have a significantly lower density of [3H]-Flunitrazepam bound to the GABAA receptor in hippocampal membranes. Our data suggest that inter-individual differences in rearing are related, at least in part, to the GABA inhibitory neurotransmission controlled by the benzodiazepine allosteric site in the GABAA receptor.

Safe operation of online learning data driven model predictive control of building energy systems

Model predictive control is a promising approach to reduce the CO 2 emissions in the building sector.However,the vast modeling effort hampers the widescale practical application.Here,data-driven process models,like artificial neural networks,are well-suited to automatize the modeling.However,the underlying data set strongly determines the quality and reliability of artificial neural networks.In general,the validity domain of a machine learning model is limited to the data that was used to train it.Predictions based on system states outside that domain,so-called extrapolations,are unreliable and can negatively influence the control quality.We present a safe operation approach combined with online learning to deal with extrapolation in data-driven model predictive control.Here,the k-nearest neighbor algorithm is used to detect extrapolation to switch to a robust fallback controller.By continuously retraining the artificial neural networks during operation,we successively increase the validity domain of the artificial neural networks and the control quality.We apply the approach to control a building energy system provided by the BOPTEST framework.We compare controllers based on two data sets,one with extensive system excitation and one with baseline operation.The system is controlled to a fixed temperature set point in baseline operation.Therefore,the artificial neural networks trained on this data set tend to extrapolate in other operating points.We show that safe operation in combination with online learning significantly improves performance.

Spatio-temporal patterns of zooplankton in a main-stem dam affected tributary: a case study in the Xiangxi River of the Three Gorges Reservoir, China

As the ecologically important recipient channels for riverine ecosystems, tributaries provide unique microhabitats for microorganisms, among which zooplankton constitutes the most important heterotrophic organisms. In particular, the reduced water velocity caused by dams is more favorable for zooplankton development;therefore, dammed rivers are expected to support extremely diverse and abundant zooplankton communities and notably different spatiotemporal distribution patterns. So far,however, only very few molecular studies support these assumptions. Using high-throughput sequencing, a high number of 350 operational taxonomic units(OTUs;97% cutoff) were retrieved from 30 samples collected in the Xiangxi River, the nearest large tributary upstream of the Three Gorges Dam. Zooplankton did not show significant spatial distribution in the channel. Instead,the community structures varied significantly over sampling dates, corroborating the seasonal patterns found in lakes and ponds in the subtropical zone. As expected, the community compositions were deterministically governed by environmental filtering processes(phylogenetic clustering), in which water velocity appeared to be much less important than other investigated environmental factors. Moreover, most of the detected phylotypes(OTUs) had a relatively high(>90%) sequence similarity to previously deposited sequences, suggesting a mediocre degree of genetic novelty within the zooplankton communities in the Xiangxi River.