In this paper, A CMOS voltage reference (CVR) with two-stage self-biased stacked diode connected MOS transistors (SDMTs) which actively compensates for process, voltage, and temperature (PVT) variations via a tandem differential structure (TDS) is proposed. The SDMTs core biased by new pseudo cascode current mirror guarantees better suppression against supply change and generates two reference voltages. The TDS, composed of two tandem NMOS transistors, serves as an output stage, differentially processing these voltages and compensating the final reference against PVT variations. Thus, the deviation of final reference voltage from PVT change is largely reduced. The proposed CVR is fabricated in a 0.18-$\mu $m CMOS process occupying a total area of$0.0048~\mathbf {mm^{2} } $. Measurement results from 7 chips demonstrate that the design can achieve an average temperature coefficient of 67 ppm/° C from −40° C to 140° C without trimming networks. Line sensitivity and power supply rejection ratio are 0.009 %/V with a supply range of 1.3V to 2.5 V and -83 dB at 100 Hz. 1% settling time only takes 0.28ms. The average reference voltage is 294 mV.

This brief presents an ultralow-energy and low-propagation-delay balanced voltage level shifter (VLS) with a wide voltage conversion range. The proposed VLS achieves low propagation delay while operating with subthreshold voltage, utilizing a newly introduced dynamic biasing scheme (DBS). This scheme improves both turn-on and turn-off speeds by reducing the threshold voltage of the input device circularly. The biasing-related power consumption is largely reduced by well-designed structure. The function of the proposed VLS design has been validated by using a standard 130-nm CMOS technology, taking into account process, voltage, and temperature (PVT) variations. With the implementation of the DBS, the delay is reduced to 6.5 ns, dynamic energy is lowered to 21.7 fJ, the minimum supply voltage is 0.18 V, and the average static power consumption is 2.8 nW for a conversion from 0.3 to 1.2 V.

This addresses errors in the Early Access version of [1]. Due to a publisher error, the terms Q1, Q2, and Q3 were used (with subscripts) instead of Q1, Q2, and Q3 (no subscript). They represent nodes on the schematic, not variables.

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://github.com/SCNU-RISLAB/MF-MOS.

LiDAR-based Moving Object Segmentation (MOS) aims to locate and segment moving objects in point clouds of the current scan using motion information from previous scans. Despite the promising results achieved by previous MOS methods, several key issues, such as the weak coupling of temporal and spatial information, still need further study. In this paper, we propose a novel LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model, termed MambaMOS. Firstly, we develop a novel embedding module, the Time Clue Bootstrapping Embedding (TCBE), to enhance the coupling of temporal and spatial information in point clouds and alleviate the issue of overlooked temporal clues. Secondly, we introduce the Motion-aware State Space Model (MSSM) to endow the model with the capacity to understand the temporal correlations of the same object across different time steps. Specifically, MSSM emphasizes the motion states of the same object at different time steps through two distinct temporal modeling and correlation steps. We utilize an improved state space model to represent these motion differences, significantly modeling the motion states. Finally, extensive experiments on the SemanticKITTI-MOS and KITTI-Road benchmarks demonstrate that the proposed MambaMOS achieves state-of-the-art performance. The source code is publicly available at https://github.com/Terminal-K/MambaMOS

Social events are due to gradually changing relations between entities including citizens, organizations, and national governments.Predicting multiple co-occurring events of different types in the future can help analysts understand social dynamics better and make quick and accurate decisions in advance.However, due to the overlook of the knowledge (e.g., event actors and different relations between them), existing methods are insufficient to model the structural and temporal dependence of events with different types simultaneously to better realize the prediction of future multiple co-occurring events.In the paper, we propose a novel Knowledge-aware attention based temporal Graph Convolutional Network (KatGCN) for predicting multiple co-occurring events of different types.We model social events as temporal event graph and extract static features (e.g., event background, topic keywords) from event content to enhance semantic of event graph.We design knowledge-aware attention based graph aggregation method to capture the structure dependence of co-occurring events with different types.We apply temporal encoding to capture the temporal dependence between temporally adjacent events.Empirical results on five-country datasets show that KatGCN outperforms state-of-the-art methods.Further studies verify the effectiveness and interpretability of our model.

Event detection from Twitter has attracted attention from researchers in the past decade due to the widespread use of social media.By leveraging the knowledge derived from these events, it is possible to understand what consumers are interested in and give the opportunity for organizations to make better decisions.Numerous studies have proven the advantage of burst detection methods in detecting events in Twitter streams.However, some burst detection methods mainly focus on the bursty characteristics caused by events while the elements in events are not fully utilized.In this paper, we focus on the elements in When, Where, Who, and What (4W) dimensions of events and propose a 4W-oriented event detection method called BEHIND.BEHIND jointly uses Bursty Elements and Heterogeneous Information Network(HIN) for event detection.Bursty Elements are calculated through probability distribution and they are used to select tweets with bursty elements.HIN is used to enhance relevance judgment in 4W dimensions between tweets to help cluster tweets.The tweet clusters are corresponding to events we detected.We used a benchmark dataset to evaluate our method.Experimental results demonstrate that our method achieves higher precision and less duplication rate, and detects more events than the state-of-the-art methods.