Using local ecological knowledge to assess the status of the Critically Endangered Chinese giant salamander Andrias davidianus in Guizhou Province, China

The Critically Endangered Chinese giant salamander Andrias davidianus, the world's largest amphibian, is severely threatened by unsustainable exploitation of wild individuals. However, field data with which to assess the salamander's status, population trends, or exploitation across its geographical range are limited, and recent field surveys using standard ecological field techniques have typically failed to detect wild individuals. We conducted community-based fieldwork in three national nature reserves (Fanjingshan, Leigongshan and Mayanghe) in Guizhou Province, China, to assess whether local ecological knowledge constitutes a useful tool for salamander conservation. We collected a sample of dated salamander sighting records and associated data from these reserves for comparative assessment of the relative status of salamander populations across the region. Although Fanjingshan and Leigongshan are still priority sites for salamander conservation, few recent sightings were recorded in either reserve, and respondents considered that salamanders had declined locally at both reserves. The species may already be functionally extinct at Mayanghe. Although respondent data on threats to salamanders in Guizhou are more difficult to interpret, overharvesting was the most commonly suggested explanation for salamander declines, and it is likely that the growing salamander farming industry is the primary driver of salamander extraction from Guizhou's reserves. Questionnaire-based surveys can collect novel quantitative data that provide unique insights into the local status of salamander populations, and we advocate wide-scale incorporation of this research approach into future salamander field programmes

A New Species of the Asian Toad Genus Megophrys sensu lato(Amphibia: Anura: Megophryidae) from Guizhou Province, China

We describe a new species of the genus Megophrys sensu lato from Guizhou Province, China. Molecular phylogenetic analyses based on mitochondrial DNA and nuclear DNA sequences all strongly supported the new species as an independent lineage in Megophrys (Panophrys) clade. The new species is distinguished from its congeners by a combination of the following morphological characteristics: (1) small body size with SVL < 38.8 mm in male and SVL < 42.3 mm in female; (2) vomerine teeth absent; (3) tongue not notched behind; (4) a small horn-like tubercle at the edge of each upper eyelid; (5) tympanum distinctly visible, rounded; (6) two metacarpal tubercles in hand; (7) relative finger lengths: II < I < V < III; (8) toes with rudimentary webbing at bases; (9) heels overlapping when thighs are positioned at right angles to the body; (10) tibiotarsal articulation reaching the level between tympanum to eye when leg stretched forward; (11) an internal single subgular vocal sac in male; (12) in breeding male, the nuptial pads with black nuptial spines on the dorsal bases of the first and second fingers

GraphMoco:a Graph Momentum Contrast Model that Using Multimodel Structure Information for Large-scale Binary Function Representation Learning

In the field of cybersecurity, the ability to compute similarity scores at the function level is import. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining on erroneous labels. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale BCSD. To overcome the aforementioned problems, we propose GraphMoco: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. Our approach employs a CNN-based model and departs from the usage of memory-intensive pre-trained models. We adopt an unsupervised learning strategy that effectively use the intrinsic structural information present in the binary code. Our approach eliminates the need for manual labeling of similar or dissimilar information.Importantly, GraphMoco demonstrates exceptional performance in terms of both efficiency and accuracy when operating on extensive datasets. Our experimental results indicate that our method surpasses the current SOTA approaches in terms of accuracy.Comment: 22 pages,7 figure

Time-Optimal Control for High-Order Chain-of-Integrators Systems with Full State Constraints and Arbitrary Terminal States

Time-optimal control for high-order chain-of-integrators systems with full state constraints and arbitrary given terminal states remains a challenging problem in the optimal control theory domain, yet to be resolved. To enhance further comprehension of the problem, this paper establishes a novel notation system and theoretical framework, successfully providing the switching manifold for high-order problems in the form of switching law. Through deriving properties of switching laws on signs and dimension, this paper proposes a definite condition for time-optimal control. Guided by the developed theory, a trajectory planning method named the manifold-intercept method (MIM) is developed. The proposed MIM can plan time-optimal jerk-limited trajectories with full state constraints, and can also plan near-optimal higher-order trajectories with negligible extra motion time. Numerical results indicate that the proposed MIM outperforms all baselines in computational time, computational accuracy, and trajectory quality by a large gap

Deep Learning with Convolutional Neural Networks for Motor Brain-Computer Interfaces based on Stereo-electroencephalography (SEEG)

Objective: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals. Methods: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN. Results: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 ± 6.1%, 38 ± 4.9%, 60 ± 3.9%, 60 ± 3.3%, 61 ± 3.2%, and 63 ± 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain. Conclusion: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives. Significance: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.</p

Advertisement calls of Leptobrachella suiyangensis and Leptobrachella bashaensis (Anura, Megophryidae)

In this study, the advertisement calls of Leptobrachella suiyangensis and Leptobrachella bashaensis are described. The advertisement call of L. suiyangensis includes simple and complex calls, with four different call types and a dominant frequency ranging 4.13–4.82 kHz. The advertisement call of L. bashaensis consists of a single note, with a dominant frequency 6.03–6.46 kHz. We compare the advertisement calls with other species in the genus Leptobrachella, and discuss the definitions of primary advertisement calls and secondary advertisement calls. Our results provide basic data for further acoustic, taxonomic and ecological studies in the genus Leptobrachella

A new species of the odorous frog genus Odorrana (Amphibia, Anura, Ranidae) from southwestern China

The genus Odorrana is widely distributed in the mountains of East and Southeastern Asia. An increasing number of new species in the genus have been recognized especially in the last decade. Phylogenetic studies of the O. schmackeri species complex with wide distributional range also revealed several cryptic species. Here, we describe a new species in the species complex from Guizhou Province of China. Phylogenetic analyses based on mitochondrial DNA indicated the new species as a monophyly clustered into the Odorrana clade and sister to O. schmackeri, and nuclear DNA also indicated it as an independent lineage separated from its related species. Morphologically, the new species can be distinguished from its congeners based on a combination of the following characters: (1) having smaller body size in males (snout-vent length (SVL) <43.3 mm); (2) head longer than wide; (3) dorsolateral folds absent; (4) tympanum of males large and distinct, tympanum diameter twice as long as width of distal phalanx of finger III; (5) two metacarpal tubercles; (6) relative finger lengths: II < I < IV < III; (7) tibiotarsal articulation reaching to the level between eye to nostril when leg stretched forward; (8) disks on digits with circum-marginal grooves; (9) toes fully webbed to disks; (10) the first subarticular tubercle on fingers weak; (11) having white pectoral spinules, paired subgular vocal sacs located at corners of throat, light yellow nuptial pad on the first finger in males