The Performance Matching of Inverter Room Air Conditioner

Some key parameter like suction superheat, compressor frequency, indoor and outdoor air volume, have been explored using simulation tools for the performance matching of the inverter room air conditioner. It was found that the all above parameters can be further optimized in every matching condition (including intermediate cooling, rated cooling, maximum cooling, middle heating and rated heating, etc.). The optimum range for all above parameter has been found and can be used in the variable frequency control module to ensure optimal overall performance of the system

Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection

Current 3D object detection models follow a single dataset-specific training and testing paradigm, which often faces a serious detection accuracy drop when they are directly deployed in another dataset. In this paper, we study the task of training a unified 3D detector from multiple datasets. We observe that this appears to be a challenging task, which is mainly due to that these datasets present substantial data-level differences and taxonomy-level variations caused by different LiDAR types and data acquisition standards. Inspired by such observation, we present a Uni3D which leverages a simple data-level correction operation and a designed semantic-level coupling-and-recoupling module to alleviate the unavoidable data-level and taxonomy-level differences, respectively. Our method is simple and easily combined with many 3D object detection baselines such as PV-RCNN and Voxel-RCNN, enabling them to effectively learn from multiple off-the-shelf 3D datasets to obtain more discriminative and generalizable representations. Experiments are conducted on many dataset consolidation settings including Waymo-nuScenes, nuScenes-KITTI, Waymo-KITTI, and Waymo-nuScenes-KITTI consolidations. Their results demonstrate that Uni3D exceeds a series of individual detectors trained on a single dataset, with a 1.04x parameter increase over a selected baseline detector. We expect this work will inspire the research of 3D generalization since it will push the limits of perceptual performance.Comment: Accepted by CVPR-2023, and our code is available at https://github.com/PJLab-ADG/3DTran

Suppressing the vortex-induced vibration of a bridge deck via suction

This paper presents experimental and numerical study with the objective of exploring the effect of suction control on vortex-induced vibration (VIV) of a bridge deck. The vertical and torsional responses of the model with or without suction control during this experiment were measured. The results demonstrate that the suction decreases the vibration amplitudes. The suction holes arranged on the undersurface near the leeward of the model has the best effect. To study the mechanism of the suction control, the aerodynamic stability of the model is analysed by the forced vibration method. The results demonstrate that the aerodynamic stability of the model is increased by the suction control

Uplift, Climate and Biotic Changes at the Eocene-Oligocene Transition in Southeast Tibet

The uplift history of southeastern Tibet is crucial to understanding processes driving the tectonic evolution of the Tibetan Plateau and surrounding areas. Underpinning existing palaeoaltimetric studies has been regional mapping based in large part on biostratigraphy that assumes a Neogene modernisation of the highly diverse, but threatened, Asian biota. Here, with new radiometric dating and newly-collected plant fossil archives, we quantify the surface height of part of Tibet’s southeastern margin of Tibet in the latest Eocene (~34 Ma) to be ~3 km and rising, possibly attaining its present elevation (3.9 km) in the early Oligocene. We also find that the Eocene-Oligocene transition in southeastern Tibet witnessed leaf size diminution and a floral composition change from sub-tropical/warm temperate to cool temperate, likely reflective of both uplift and secular climate change, and that by the latest Eocene floral modernization on Tibet had already taken place implying modernization was deeply-rooted in the Paleogene

Separation of Normal and Premalignant Cervical Epithelial Cells Using Confocal Light Absorption and Scattering Spectroscopic Microscopy Ex Vivo

Confocal light absorption and scattering spectroscopic (CLASS) microscopy can detect changes in biochemicals and the morphology of cells. It is therefore used to detect high-grade cervical squamous intraepithelial lesion (HSIL) cells in the diagnosis of premalignant cervical lesions. Forty cervical samples from women with abnormal Pap smear test results were collected, and twenty cases were diagnosed as HSIL; the rest were normal or low-grade cervical squamous intraepithelial lesion (LSIL). The enlarged and condensed nuclei of HSIL cells as viewed under CLASS microscopy were much brighter and bigger than those of non-HSIL cells. Cytological elastic scattered light data was then collected at wavelengths between 400 and 1000 nm. Between 600 nm to 800 nm, the relative elastic scattered light intensity of HSIL cells was higher than that of the non-HSIL. Relative intensity peaks occurred at 700 nm and 800 nm. CLASS sensitivity and specificity results for HSIL and non-HSIL compared to cytology diagnoses were 80% and 90%, respectively. This study demonstrated that CLASS microscopy could effectively detect cervical precancerous lesions. Further study will verify this conclusion before the method is used in clinic for early detection of cervical cancer

ScratchDet: Training Single-Shot Object Detectors from Scratch

Current state-of-the-art object objectors are fine-tuned from the off-the-shelf networks pretrained on large-scale classification dataset ImageNet, which incurs some additional problems: 1) The classification and detection have different degrees of sensitivity to translation, resulting in the learning objective bias; 2) The architecture is limited by the classification network, leading to the inconvenience of modification. To cope with these problems, training detectors from scratch is a feasible solution. However, the detectors trained from scratch generally perform worse than the pretrained ones, even suffer from the convergence issue in training. In this paper, we explore to train object detectors from scratch robustly. By analysing the previous work on optimization landscape, we find that one of the overlooked points in current trained-from-scratch detector is the BatchNorm. Resorting to the stable and predictable gradient brought by BatchNorm, detectors can be trained from scratch stably while keeping the favourable performance independent to the network architecture. Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence. By extensive experiments and analyses on downsampling factor, we propose the Root-ResNet backbone network, which makes full use of the information from original images. Our ScratchDet achieves the state-of-the-art accuracy on PASCAL VOC 2007, 2012 and MS COCO among all the train-from-scratch detectors and even performs better than several one-stage pretrained methods. Codes will be made publicly available at https://github.com/KimSoybean/ScratchDet.Comment: CVPR2019 Oral Presentation. Camera Ready Versio

Improving the Model Consistency of Decentralized Federated Learning

To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its neighbors in a decentralized communication network. However, existing DFL suffers from high inconsistency among local clients, which results in severe distribution shift and inferior performance compared with centralized FL (CFL), especially on heterogeneous data or sparse communication topology. To alleviate this issue, we propose two DFL algorithms named DFedSAM and DFedSAM-MGS to improve the performance of DFL. Specifically, DFedSAM leverages gradient perturbation to generate local flat models via Sharpness Aware Minimization (SAM), which searches for models with uniformly low loss values. DFedSAM-MGS further boosts DFedSAM by adopting Multiple Gossip Steps (MGS) for better model consistency, which accelerates the aggregation of local flat models and better balances communication complexity and generalization. Theoretically, we present improved convergence rates $\small \mathcal{O}\big(\frac{1}{\sqrt{KT}}+\frac{1}{T}+\frac{1}{K^{1/2}T^{3/2}(1-\lambda)^2}\big)$ and $\small \mathcal{O}\big(\frac{1}{\sqrt{KT}}+\frac{1}{T}+\frac{\lambda^Q+1}{K^{1/2}T^{3/2}(1-\lambda^Q)^2}\big)$ in non-convex setting for DFedSAM and DFedSAM-MGS, respectively, where $1-\lambda$ is the spectral gap of gossip matrix and $Q$ is the number of MGS. Empirically, our methods can achieve competitive performance compared with CFL methods and outperform existing DFL methods.Comment: ICML202

Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy

To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise. However, existing DPFL methods tend to make a sharp loss landscape and have poor weight perturbation robustness, resulting in severe performance degradation. To alleviate these issues, we propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates Sharpness Aware Minimization (SAM) optimizer to generate local flatness models with improved stability and weight perturbation robustness, which results in the small norm of local updates and robustness to DP noise, thereby improving the performance. To further reduce the magnitude of random noise while achieving better performance, we propose DP-FedSAM-$top_k$ by adopting the local update sparsification technique. From the theoretical perspective, we present the convergence analysis to investigate how our algorithms mitigate the performance degradation induced by DP. Meanwhile, we give rigorous privacy guarantees with R\'enyi DP, the sensitivity analysis of local updates, and generalization analysis. At last, we empirically confirm that our algorithms achieve state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL.Comment: 20 pages. arXiv admin note: substantial text overlap with arXiv:2303.1124