Harmonizing Output Imbalance for semantic segmentation on extremely-imbalanced input data

Semantic segmentation is a high level computer vision task that assigns a label for each pixel of an image. It is challenging to deal with extremely-imbalanced data in which the ratio of target pixels to background pixels is lower than 1:1000. Such severe input imbalance leads to output imbalance for poor model training. This paper considers three issues for extremely-imbalanced data: inspired by the region-based Dice loss, an implicit measure for the output imbalance is proposed, and an adaptive algorithm is designed for guiding the output imbalance hyperparameter selection; then it is generalized to distribution-based loss for dealing with output imbalance; and finally a compound loss with our adaptive hyperparameter selection algorithm can keep the consistency of training and inference for harmonizing the output imbalance. With four popular deep architectures on our private dataset from three different input imbalance scales and three public datasets, extensive experiments demonstrate the competitive/promising performance of the proposed method.Comment: 18 pages, 13 figures, 2 appendixe

PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling

Pre-routing timing prediction has been recently studied for evaluating the quality of a candidate cell placement in chip design. It involves directly estimating the timing metrics for both pin-level (slack, slew) and edge-level (net delay, cell delay), without time-consuming routing. However, it often suffers from signal decay and error accumulation due to the long timing paths in large-scale industrial circuits. To address these challenges, we propose a two-stage approach. First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist. Second, we use a novel node updating scheme for message passing on GCN, following the topological sorting sequence of the learned graph embedding and circuit graph. This scheme residually models the local time delay between two adjacent pins in the updating sequence, and extracts the lookup table information inside each cell via a new attention mechanism. To handle large-scale circuits efficiently, we introduce an order preserving partition scheme that reduces memory consumption while maintaining the topological dependencies. Experiments on 21 real world circuits achieve a new SOTA R2 of 0.93 for slack prediction, which is significantly surpasses 0.59 by previous SOTA method. Code will be available at: https://github.com/Thinklab-SJTU/EDA-AI.Comment: 13 pages, 5 figures, The 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions

Noise cancellation in transient coherent population trapping by differential detection

We report a damped coherent population trapping (CPT) process, which has the very same mechanism as the coherent population beating (CPB). A differential CPB scheme is proposed based on the optical path delay, through which the left- and right-hand circularly polarized lights are modulated with a phase difference of π/2. The CPB signals oscillate with the frequency in a radio frequency (RF) range, which makes the detuning frequency equal to the splitting frequency between the hyperfine energy level of the ground state of cesium. The differential CPB signal with non-zero amplitude is obtained after the two signals being subtracted from each other, thereby improving the signal-to-noise ratio of the CPB. The measurements are explained well with a simple, four-level model and are interpreted as a simple harmonic oscillator interaction with four light fields. The Allan variance of the CPB atomic clock is measured in the conventional and the differential detection configurations. The 1-s stability of the differential CPB scheme is $6.6 \times 10^{-11}$ , which is better than that of $3.2 \times 10^{-10}$ under the conventional configuration. The results confirm that the differential CPB scheme can improve the signal-to-noise ratio and hence the short-term stability

Mobilization-based characteristic value of shear strength for ultimate limit states

10.1080/17499518.2020.1859121Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards163413-43

Micro Rb atomic vapor cells for the chip-scale atomic clock

Nowadays, the portable atomic clocks with small size and low power consumption have attracted much attention. With the development of the coherent population trapping (CPT) and the micro-electro-mechanical systems (MEMS) technologies, it is possible to fabricate the miniature atomic clocks with power consumption less than 100mW. Researchers have already made great progress in the miniaturization of VCSELs, RF circuits, and photodiodes, which are the key technologies of the chip-scale atomic clock (CSAC). However, the fabrication of micro alkali atomic vapor cells is still to be improved. In this article, we describe the fabrication of micro Rb atomic vapor cells with inner dimensions of 3 mm length and 3 mm radius using anodic bonding and chemical reaction between wBaN(6) and RbCl. The optical absorption and CPT resonances of the cells were measured slightly above the room temperature. In this condition, the consumption in the temperature control will be very low. Moreover, the long life (more than four years) of the cells makes it possible to use them in CSACs.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000346295600027&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Engineering, Electrical & ElectronicPhysics, AppliedTelecommunicationsCPCI-S(ISTP)