Numerical analyses of the flow past a short rotating cylinder

This work studies the three-dimensional flow dynamics around a rotating circular cylinder of finite length, whose axis is positioned perpendicular to the streamwise direction. Direct numerical simulations and global stability analyses are performed within a parameter range of Reynolds number $Re=DU_\infty/\nu<500$ (based on cylinder diameter $D$, uniform incoming flow velocity $U_\infty$), length-to-diameter ratio $AR=L/D\leq2$ and dimensionless rotation rate $\alpha=D\Omega/2U_\infty\leq2$ (where $\Omega$ is rotation rate). By solving Nav\-ier--Sto\-kes equations, we investigated the wake patterns and explored the phase diagrams of the lift and drag coefficients. For a cylinder with $AR=1$, we found that when the rotation effect is weak ($0\leq\alpha\lesssim0.3$), the wake pattern is similar to the unsteady wake past the non-rotating finite-length cylinder, but with a new linear unstable mode competing to dominate the saturation state of the wake. The flow becomes stable for $0.3\lesssim\alpha\lesssim0.9$ when $Re<360$. When the rotation effect is strong ($\alpha\gtrsim0.9$), new low-frequency wake patterns with stronger oscillations emerge. Furthermore, the stability analyses based on the time-averaged flows and on the steady solutions demonstrate the existence of multiple unstable modes undergoing Hopf bifurcation, greatly influenced by the rotation effect. The shapes of these global eigenmodes are presented and compared, as well as their structural sensitivity, visualising the flow region important for the disturbance development with rotation. This research contributes to our understanding of the complex bluff-body wake dynamics past this critical configuration.Comment: 35 pages, 29 figures, the version of record of this article is accepted in Journal of Fluid Mechanic

Incremental Neural Implicit Representation with Uncertainty-Filtered Knowledge Distillation

Recent neural implicit representations (NIRs) have achieved great success in the tasks of 3D reconstruction and novel view synthesis. However, they suffer from the catastrophic forgetting problem when continuously learning from streaming data without revisiting the previously seen data. This limitation prohibits the application of existing NIRs to scenarios where images come in sequentially. In view of this, we explore the task of incremental learning for NIRs in this work. We design a student-teacher framework to mitigate the catastrophic forgetting problem. Specifically, we iterate the process of using the student as the teacher at the end of each time step and let the teacher guide the training of the student in the next step. As a result, the student network is able to learn new information from the streaming data and retain old knowledge from the teacher network simultaneously. Although intuitive, naively applying the student-teacher pipeline does not work well in our task. Not all information from the teacher network is helpful since it is only trained with the old data. To alleviate this problem, we further introduce a random inquirer and an uncertainty-based filter to filter useful information. Our proposed method is general and thus can be adapted to different implicit representations such as neural radiance field (NeRF) and neural SDF. Extensive experimental results for both 3D reconstruction and novel view synthesis demonstrate the effectiveness of our approach compared to different baselines

The value of IGF-1 and IGFBP-1 in patients with heart failure with reduced, mid-range, and preserved ejection fraction

Background: Previous studies have reported inconsistent results regarding the implications of deranged insulin-like growth factor 1 (IGF-1)/insulin-like growth factor-binding protein 1 (IGFBP-1) axis in patients with heart failure (HF). This study evaluates the roles of IGF1/IGFBP-1 axis in patients with HF with reduced ejection fraction (HFrEF), mid-range ejection fraction (HFmrEF), or preserved ejection fraction (HFpEF). Methods: Consecutive patients with HFrEF, HFmrEF, and HFpEF who underwent comprehensive cardiac assessment were included. The primary endpoint was the composite endpoint of all-cause death and HF rehospitalization at one year. Results: A total of 151 patients with HF (HFrEF: n = 51; HFmrEF: n = 30; HFpEF: n = 70) and 50 control subjects were included. The concentrations of IGFBP-1 (p < 0.001) and IGFBP-1/IGF-1 ratio (p < 0.001) were significantly lower in patients with HF compared to controls and can readily distinguish patients with and without HF (IGFBP-1: areas under the curve (AUC): 0.725, p < 0.001; IGFBP-1/IGF-1 ratio: AUC:0.755, p < 0.001; respectively). The concentrations of IGF-1, IGFBP-1, and IGFBP-1/IGF-1 ratio were similar among HFpEF, HFmrEF, and HFrEF patients. IGFBP-1 and IGFBP-1/IGF-1 ratio positively correlated with N-terminal probrain natriuretic peptide (NT-proBNP) levels (r = 0.255, p = 0.002; r = 0.224, p = 0.007, respectively). IGF-1, IGFBP-1, and IGFBP-1/IGF-1 ratio did not predict the primary endpoint at 1 year for the whole patients with HF and HF subtypes on both univariable and multivariable Cox regression. Conclusion: The concentrations of plasma IGFBP-1 and IGFBP-1/IGF-1 ratio can distinguish patients with and without HF. In HF, IGFBP-1 and IGFBP-1/IGF-1 ratio positively correlated with NT-proBNP levels

GNeSF: Generalizable Neural Semantic Fields

3D scene segmentation based on neural implicit representation has emerged recently with the advantage of training only on 2D supervision. However, existing approaches still requires expensive per-scene optimization that prohibits generalization to novel scenes during inference. To circumvent this problem, we introduce a generalizable 3D segmentation framework based on implicit representation. Specifically, our framework takes in multi-view image features and semantic maps as the inputs instead of only spatial information to avoid overfitting to scene-specific geometric and semantic information. We propose a novel soft voting mechanism to aggregate the 2D semantic information from different views for each 3D point. In addition to the image features, view difference information is also encoded in our framework to predict the voting scores. Intuitively, this allows the semantic information from nearby views to contribute more compared to distant ones. Furthermore, a visibility module is also designed to detect and filter out detrimental information from occluded views. Due to the generalizability of our proposed method, we can synthesize semantic maps or conduct 3D semantic segmentation for novel scenes with solely 2D semantic supervision. Experimental results show that our approach achieves comparable performance with scene-specific approaches. More importantly, our approach can even outperform existing strong supervision-based approaches with only 2D annotations. Our source code is available at: https://github.com/HLinChen/GNeSF.Comment: NeurIPS 202

System optimization of an all-silicon IQ modulator : achieving 100 Gbaud dual polarization 32QAM

We experimentally demonstrate the highest, to the best of our knowledge, reported net rate in a SiP IQ modulator. At 100 Gbaud 32QAM (quadrature amplitude modulation), and assuming 20% FEC (forward error correction) overhead, we achieved a dual polarization net rate of 833 Gb/s. This record was achieved by adapting digital signal processing to the challenging pattern dependent distortion encountered in the nonlinear and bandwidth limited regime. First the Mach Zehnder modulator (MZM) operating point (trading off modulation efficiency and 3 dB bandwidth) and linear compensation (electrical and optical) are jointly optimized. Next, the key application of nonlinear preand post-compensation are explored. We show that nonlinear processing at the transmitter, in our case an iterative learning control (ILC) method, is essential as post-processing alone could not achieve reliable communications at 100 Gbaud. Nonlinear post-compensation algorithms pushed the performance under the FEC threshold with the introduction of structured intersymbol interference in post processing and a simple one-step maximum likelihood sequence detector. We provide detailed descriptions of our methodology and results

Collaborative Policy of the Supply-Hub for Assemble-to-Order Systems with Delivery Uncertainty

This paper considers the collaborative mechanisms of the Supply-Hub in the Assemble-to-Order system (ATO system hereafter) with upstream delivery uncertainty. We first propose a collaborative replenishment mechanism in the ATO system, and construct a replenishment model with delivery uncertainty in use of the Supply-Hub. After transforming the original model into a one-dimensional optimization problem, we derive the optimal assembly quantity and reorder point of each component. In order to enable the Supply-Hub to conduct collaborative replenishment with each supplier, the punishment and reward mechanisms are proposed. The numerical analysis illustrates that service level of the Supply-Hub is an increasing function of both punishment and reward factors. Therefore, by adjusting the two factors, suppliers’ incentives of collaborative replenishment can be significantly enhanced, and then the service level of whole ATO system can be improved