On Geodesic Rays of Newtonian Gravitational Systems

In this paper, we focus on the set of geodesics rays of the Newtonian N-body problem. We find that the limits of geodesic rays are also geodesic rays, hence they are not dense in the space of initial conditions. As a result, there are many motions whose domain is the half real line has non-negative total energy, and they are not geodesic rays, the set of such motions has positive measure, we think this set gives a large space to accommodate many solutions with "bad" behaviors. As an application of weak KAM theory for N-body problem, we give a brief proof of the existence of complete parabolic orbit starting from any given initial position

NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models

Prompt-based learning is vulnerable to backdoor attacks. Existing backdoor attacks against prompt-based models consider injecting backdoors into the entire embedding layers or word embedding vectors. Such attacks can be easily affected by retraining on downstream tasks and with different prompting strategies, limiting the transferability of backdoor attacks. In this work, we propose transferable backdoor attacks against prompt-based models, called NOTABLE, which is independent of downstream tasks and prompting strategies. Specifically, NOTABLE injects backdoors into the encoders of PLMs by utilizing an adaptive verbalizer to bind triggers to specific words (i.e., anchors). It activates the backdoor by pasting input with triggers to reach adversary-desired anchors, achieving independence from downstream tasks and prompting strategies. We conduct experiments on six NLP tasks, three popular models, and three prompting strategies. Empirical results show that NOTABLE achieves superior attack performance (i.e., attack success rate over 90% on all the datasets), and outperforms two state-of-the-art baselines. Evaluations on three defenses show the robustness of NOTABLE. Our code can be found at https://github.com/RU-System-Software-and-Security/Notable

Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis

Naturally fractured gas reservoirs have contributed significantly to global gas reserves and production. The classical gas-well decline analysis relies largely on Arps’ empirical decline models, or modern production decline analysis associating with pseudo-variables. The explicit original gas in place determination methodology is extended from homogeneous reservoir to naturally fractured reservoir under constant or variable bottom-hole pressure conditions in gas-well rate decline analysis. Then, the relationship between gas flow rate and average reservoir pseudo-pressure in the boundary-dominated flow period is re-derived. This formula is in the same format with the equation for homogeneous reservoir by due to the introduction of a new productivity index parameter that captures the inter-porosity flow between fracture and matrix in the natural fractured reservoir. The proposed step-by-step procedures are applied here, which enable the estimation of decline exponent and the explicit and straightforward determination of the original gas in place without any iterative calculations. Four simulated cases prove that our methodology can be successfully used in heterogeneous naturally fractured reservoirs with irregular boundary under constant or variable bottom-hole pressure conditions.Document Type: Original articleCited as: Wang, Y., Wang, J., Zhao, W., Ji, P., Cheng, S., Yu, H. Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis. Advances in Geo-Energy Research, 2023, 9(2): 117-124. https://doi.org/10.46690/ager.2023.08.0

Survey of Deep Learning Based Multimodal Emotion Recognition

Multimodal emotion recognition aims to recognize human emotional states through different modalities related to human emotion expression such as audio, vision, text, etc. This topic is of great importance in the fields of human-computer interaction, a.pngicial intelligence, affective computing, etc., and has attracted much attention. In view of the great success of deep learning methods developed in recent years in various tasks, a variety of deep neural networks have been used to learn high-level emotional feature representations for multimodal emotion recog-nition. In order to systematically summarize the research advance of deep learning methods in the field of multi-modal emotion recognition, this paper aims to present comprehensive analysis and summarization on recent multi-modal emotion recognition literatures based on deep learning. First, the general framework of multimodal emotion recognition is given, and the commonly used multimodal emotional dataset is introduced. Then, the principle of representative deep learning techniques and its advance in recent years are briefly reviewed. Subsequently, this paper focuses on the advance of two key steps in multimodal emotion recognition: emotional feature extraction methods related to audio, vision, text, etc., including hand-crafted feature extraction and deep feature extraction; multi-modal information fusion strategies integrating different modalities. Finally, the challenges and opportunities in this field are analyzed, and the future development direction is pointed out

Knee loading inhibits osteoclast lineage in a mouse model of osteoarthritis

Osteoarthritis (OA) is a whole joint disorder that involves cartilage degradation and periarticular bone response. Changes of cartilage and subchondral bone are associated with development and activity of osteoclasts from subchondral bone. Knee loading promotes bone formation, but its effects on OA have not been well investigated. Here, we hypothesized that knee loading regulates subchondral bone remodeling by suppressing osteoclast development, and prevents degradation of cartilage through crosstalk of bone-cartilage in osteoarthritic mice. Surgery-induced mouse model of OA was used. Two weeks application of daily dynamic knee loading significantly reduced OARSI scores and CC/TAC (calcified cartilage to total articular cartilage), but increased SBP (subchondral bone plate) and B.Ar/T.Ar (trabecular bone area to total tissue area). Bone resorption of osteoclasts from subchondral bone and the differentiation of osteoclasts from bone marrow-derived cells were completely suppressed by knee loading. The osteoclast activity was positively correlated with OARSI scores and negatively correlated with SBP and B.Ar/T.Ar. Furthermore, knee loading exerted protective effects by suppressing osteoclastogenesis through Wnt signaling. Overall, osteoclast lineage is the hyper responsiveness of knee loading in osteoarthritic mice. Mechanical stimulation prevents OA-induced cartilage degeneration through crosstalk with subchondral bone. Knee loading might be a new potential therapy for osteoarthritis patients

Reservoir Permeability Prediction Based on Analogy and Machine Learning Methods: Field Cases in DLG Block of Jing’an Oilfield, China

AbstractReservoir permeability, generally determined by experimental or well testing methods, is an essential parameter in the oil and gas field development. In this paper, we present a novel analogy and machine learning method to predict reservoir permeability. Firstly, the core test and production data of other 24 blocks (analog blocks) are counted according to the DLG block (target block) of Jing’an Oilfield, and the permeability analogy parameters including porosity, shale content, reservoir thickness, oil saturation, liquid production, and production pressure difference are optimized by Pearson and principal component analysis. Then, the fuzzy matter element method is used to calculate the similarity between the target block and analog blocks. According to the similarity calculation results, reservoir permeability of DLG block is predicted by reservoir engineering method (the relationship between core permeability and porosity of QK-D7 in similar blocks) and machine learning method (random forest, gradient boosting decision tree, light gradient boosting machine, and categorical boosting). By comparing the prediction accuracy of the two methods through the evaluation index determination coefficient (R2) and root mean square error (RMSE), the CatBoost model has higher accuracy in predicting reservoir permeability, with R2 of 0.951 and RMSE of 0.139. Finally, the CatBoost model is selected to predict reservoir permeability of 121 oil wells in the DLG block. This work uses simple logging and production data to quickly and accurately predict reservoir permeability without coring and testing. At the same time, the prediction results are well applied to the formulation of DLG block development technology strategy, which provides a new idea for the application of machine learning to predict oilfield parameters