Dynamic analysis of the longitudinal vibration on bottom drilling tools

With extreme complexity, the drilling process is a dynamic process which is severely influenced by longitudinal vibration. Longitudinal vibration, as one of the most important reason, is directly generated by the fatigue failure of the bottom hole assembly. In this paper, the natural frequencies of longitudinal vibration along the drillstring are analyzed by the finite element method. The deformed plot, stress nephogram, and displacement contour map under 1 to 4 ordered the natural frequency of the longitudinal vibration are obtained. The analysis results show that the maximum deformation always appears in the central part of the string so that some technological process on these positions is required to reduce the collision between the string and wellbore wall. Additionally, a time series of longitudinal vibration of a bottom rotating drillstring is extracted from real-time field data, which is measured while drilling near the drill bit. Then the time-frequency and energy spectrum analysis of the longitudinal vibration is carried out. The results of the statistical analysis show that, when the drillstring uniformly rotates, the longitudinal vibration can be considered as a kind of random vibration. However, if the stick-slip phenomenon occurs during the drilling process, the energy concentration will appear in the time series spectrum of the longitudinal vibration, by which means the vibration could be regarded as random no longer

Trajectory Data Collection with Local Differential Privacy

Trajectory data collection is a common task with many applications in our daily lives. Analyzing trajectory data enables service providers to enhance their services, which ultimately benefits users. However, directly collecting trajectory data may give rise to privacy-related issues that cannot be ignored. Local differential privacy (LDP), as the de facto privacy protection standard in a decentralized setting, enables users to perturb their trajectories locally and provides a provable privacy guarantee. Existing approaches to private trajectory data collection in a local setting typically use relaxed versions of LDP, which cannot provide a strict privacy guarantee, or require some external knowledge that is impractical to obtain and update in a timely manner. To tackle these problems, we propose a novel trajectory perturbation mechanism that relies solely on an underlying location set and satisfies pure $\epsilon$-LDP to provide a stringent privacy guarantee. In the proposed mechanism, each point's adjacent direction information in the trajectory is used in its perturbation process. Such information serves as an effective clue to connect neighboring points and can be used to restrict the possible region of a perturbed point in order to enhance utility. To the best of our knowledge, our study is the first to use direction information for trajectory perturbation under LDP. Furthermore, based on this mechanism, we present an anchor-based method that adaptively restricts the region of each perturbed trajectory, thereby significantly boosting performance without violating the privacy constraint. Extensive experiments on both real-world and synthetic datasets demonstrate the effectiveness of the proposed mechanisms.Comment: Accepted by VLDB 202

Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective

Linear scalarization, i.e., combining all loss functions by a weighted sum, has been the default choice in the literature of multi-task learning (MTL) since its inception. In recent years, there is a surge of interest in developing Specialized Multi-Task Optimizers (SMTOs) that treat MTL as a multi-objective optimization problem. However, it remains open whether there is a fundamental advantage of SMTOs over scalarization. In fact, heated debates exist in the community comparing these two types of algorithms, mostly from an empirical perspective. To approach the above question, in this paper, we revisit scalarization from a theoretical perspective. We focus on linear MTL models and study whether scalarization is capable of fully exploring the Pareto front. Our findings reveal that, in contrast to recent works that claimed empirical advantages of scalarization, scalarization is inherently incapable of full exploration, especially for those Pareto optimal solutions that strike the balanced trade-offs between multiple tasks. More concretely, when the model is under-parametrized, we reveal a multi-surface structure of the feasible region and identify necessary and sufficient conditions for full exploration. This leads to the conclusion that scalarization is in general incapable of tracing out the Pareto front. Our theoretical results partially answer the open questions in Xin et al. (2021), and provide a more intuitive explanation on why scalarization fails beyond non-convexity. We additionally perform experiments on a real-world dataset using both scalarization and state-of-the-art SMTOs. The experimental results not only corroborate our theoretical findings, but also unveil the potential of SMTOs in finding balanced solutions, which cannot be achieved by scalarization.Comment: Accepted at NeurIPS 202

HTP: Exploiting Holistic Temporal Patterns for Sequential Recommendation

Sequential recommender systems have demonstrated a huge success for next-item recommendation by explicitly exploiting the temporal order of users' historical interactions. In practice, user interactions contain more useful temporal information beyond order, as shown by some pioneering studies. In this paper, we systematically investigate various temporal information for sequential recommendation and identify three types of advantageous temporal patterns beyond order, including absolute time information, relative item time intervals and relative recommendation time intervals. We are the first to explore item-oriented absolute time patterns. While existing models consider only one or two of these three patterns, we propose a novel holistic temporal pattern based neural network, named HTP, to fully leverage all these three patterns. In particular, we introduce novel components to address the subtle correlations between relative item time intervals and relative recommendation time intervals, which render a major technical challenge. Extensive experiments on three real-world benchmark datasets show that our HTP model consistently and substantially outperforms many state-of-the-art models. Our code is publically available at https://github.com/623851394/HTP/tree/main/HTP-mai

Effect of annulus drilling fluid on lateral vibration of drillstring

Annular drilling fluid between the drillstring and borehole wall has a great influence on lateral vibration of drillstring and the influence involves the added mass. Assuming the drilling fluid is incompressible, we derive the added mass coefficient that annular drilling fluid influences on lateral vibration of drillstring in the case of axial flow of drilling fluid. When the axial flow of drilling fluid is considered, the added mass coefficient is difficult to solve. We apply CFD method and dynamic mesh technique to establish the calculation model for the flow in the annulus caused by the vibration of drillstring in the annulus. The pressure distribution and velocity distribution of annular drilling fluid are obtained. The added mass force of the drilling fluid acting on the drillstring along the direction of the drillstring is obtained from the pressure distribution, and the added mass coefficient of the lateral vibration of the drillstring is obtained. This paper provides the basis to solve the added mass coefficient of the lateral vibration of drillstring considering axial flow of drilling fluid

Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images

Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient in addressing over-segmentation, top-down instance segmentation suffers from over-crop problem. However, a complete segmentation mask is crucial for biological image analysis as it delivers important morphological properties such as shapes and volumes. In this paper, we propose a region proposal rectification (RPR) module to address this challenging incomplete segmentation problem. In particular, we offer a progressive ROIAlign module to introduce neighbor information into a series of ROIs gradually. The ROI features are fed into an attentive feed-forward network (FFN) for proposal box regression. With additional neighbor information, the proposed RPR module shows significant improvement in correction of region proposal locations and thereby exhibits favorable instance segmentation performances on three biological image datasets compared to state-of-the-art baseline methods. Experimental results demonstrate that the proposed RPR module is effective in both anchor-based and anchor-free top-down instance segmentation approaches, suggesting the proposed method can be applied to general top-down instance segmentation of biological images. Code is available

Improving Negative-Prompt Inversion via Proximal Guidance

DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifier-free guidance (CFG) scales being used for enhanced editing. Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control. Negative-prompt inversion (NPI) further offers a training-free closed-form solution of NTI. However, it may introduce artifacts and is still constrained by DDIM reconstruction quality. To overcome these limitations, we propose Proximal Negative-Prompt Inversion (ProxNPI), extending the concepts of NTI and NPI. We enhance NPI with a regularization term and reconstruction guidance, which reduces artifacts while capitalizing on its training-free nature. Our method provides an efficient and straightforward approach, effectively addressing real image editing tasks with minimal computational overhead.Comment: Code at https://github.com/phymhan/prompt-to-promp