Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

Historical demography and climate driven distributional changes in a widespread Neotropical freshwater species with high economic importance

The Neotropical region exhibits the greatest worldwide diversity and the diversification history of several clades is related to the puzzling geomorphologic and climatic history of this region. The freshwater Amazon ecoregion contains the main hydrographic basins of the Neotropical region that are highly dendritic and ecologically diverse. It contains a rich and endemic fish fauna, including one of its most iconic and economically important representatives, the bony-tongue Arapaima gigas (Teleostei, Osteoglossiformes). Here, we evaluated the projected distribution of the genus in different historical periods (Present, Last Glacial Maximum, Last Interglacial Maximum and Near Future) and interpreted these results in light of the genomic diversity and modeled historical demography. For that, we combined species distribution models, population genetic analysis using SNPs and deep learning model selection. We analyzed a representative sample of the genus from the two basins where it naturally occurs, four localities in the Amazon (Am) and three in the Tocantins-Araguaia (To-Ar) basin, as well as individuals from three fish farms. We inferred a potentially smaller distribution in the glacial period, with a possible refuge in central Am. Our genetic data agrees with this result, suggesting a higher level of genetic diversity in the Am basin, compared to that observed in To-Ar. Our deep learning model comparison indicated that the To-Ar basin was colonized by the population from the Am basin. Considering a global warming scenario in the near future, A. gigas could reach an even larger range, especially if anthropogenic related dispersal occurs, potentially invading new areas and impacting their communities.     

中国苦苣苔科植物新资料——小花线柱苣苔

该文报道了产自中国西藏自治区墨脱县境内的线柱苣苔属(Rhynchotechum Blume)中国分布新记录——小花线柱苣苔(R. parviflorum Blume)。该新记录种常生长在林中溪流附近的崖壁以及次生林下阴湿生境中,主要辨别特征为叶基本对生,花萼裂片被绢毛,花梗被黄褐色绒毛,花冠筒较小,子房具短柔毛,果无毛至微柔毛。印度学者于2020年记载为印度新分布,而原始文献中记录的凭证标本采集点位于中国西藏自治区墨脱县境内,故对原文记述的产地信息提出质疑。同时,在前人的研究中部分馆藏的线柱苣苔属植物标本被认定为该新记录种,在此一并提出该新记录种在中国的分布地理信息和详细描述。     

四川黑竹沟地区非飞行小型兽类物种多样性与群落组成

四川黑竹沟国家级自然保护区位于生物多样性丰富的凉山山系, 在保护和维持区域生物多样性方面具有极其重要的地位, 然而对该自然保护区兽类群落的研究却极为缺乏。为了了解该自然保护区及周边的小型兽类群落,本文作者在2018年4-10月调查了该区域的非飞行小型兽类物种多样性及其群落组成。在海拔1,537-3,830 m间共设置样方184个, 布设鼠夹9,016铗日, 捕获小型兽类536只, 隶属4目7科13属21种。包括滇攀鼠(Vernaya fulva)和等齿鼩鼹(Uropsilus aequodonenia)两个稀有物种在内的共计9个物种为该地区首次报道, 丰富了物种分布记录。结合历史资料, 黑竹沟地区共记录小型兽类43种, 隶属4目9科28属。在43种小型兽类中, 东洋界物种37种, 占绝对优势(86%), 分布型以喜马拉雅-横断山型占优, 为18种(占东洋界48.6%)。本次调查捕获的21种小型兽类中, 群落优势种为中华姬鼠(Apodemus draco, 33.2%)、北社鼠(Niviventer confucianus, 21.3%)和中华绒鼠(Eothenomys chinensis, 12.7%)。随着海拔上升, 群落优势种组成发生变化, 由北社鼠 + 中华姬鼠 + 针毛鼠(Niviventer fulvescens) + 短尾鼩(Anourosorex squamipes)群落, 转变为中华绒鼠 + 中华姬鼠 + 凉山沟牙田鼠(Proedromys liangshanensis) + 西南绒鼠(Eothenomys custos)群落。群落物种区系的分布型组成随着海拔升高而发生变化, 喜马拉雅-横断山型物种随海拔升高所占比例增大; 分布于喜马拉雅-横断山的特有种分布的下限、中点和上限平均海拔都高于非特有种, 且特有种和非特有种的中点和下限平均海拔差异显著(n = 21, df = 19, P = 0.013; n = 21, df = 19, P < 0.01), 说明该区域物种具有明显的东洋界特征, 中高海拔主要由特有种构成。本研究丰富了黑竹沟地区小型兽类及群落多样性的数据资料, 有利于该地区物种多样性的研究和保护。     

宁夏兽类一属、种新纪录——淡灰豹鼩（Pantherina griselda Thomas，1912）

正淡灰豹鼩(Pantherina griselda)隶属于鼩形目(Soricomorpha)鼩鼱亚科(Soricinae)豹鼩属(Pantherina),其模式产地为中国甘肃省临潭县(Thomas,1912)。由于淡灰豹鼩整体形态与黑齿鼩鼱属(Blarinella)中的原甘肃川鼩(B.griselda)相似,且二者同域分布,曾被记录为黑齿鼩鼱属中的一个隐存种Blarinella sp.(Bannikova et al.,2017);He等     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis.Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function.ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset.ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.     

A Wearable System for In-Home and Long-Term Assessment of Fetal Movement

Objectives: This paper presents a novel wearable system for in-home and long-term fetal movement monitoring on a reliable and easily accessible basis.Material and methods: The system mainly consists of four accelerometers for fetal movement signal acquisition, a microcontroller for signal processing and an Android-based device interacting with the microcontroller via Bluetooth Low Energy (BLE), providing the mother with information related to the fetal movement in an intelligible way.Results: The proposed system can deliver reliable results with a specificity of 0.99 and a sensitivity of 0.77 for fetal movement time series signal classification.Conclusion: The proposed wearable system will provide a good alternative to optimize the use of medical professionals and hospital resources, and has potential applications in the field of e-Health home care. Besides, the fetal movement acceleration signals acquired with volunteers (pregnant women) help establish an initial database for future medical analysis of sensor-recorded fetal behaviors.