Point-Supervised Object Detection (PSOD) in a discriminative style has recently gained significant attention for its impressive detection performance and cost-effectiveness. However, accurately predicting high-quality pseudo-box labels for drone-view images, which often feature densely packed small objects, remains a challenge. This difficulty arises primarily from the limitation of rigid sampling strategies, which hinder the pseudo-box optimization process. To address this, we propose PointSR, an effective and robust point-supervised object detection framework with self-regularized sampling that integrates temporal and informative constraints throughout the pseudo-box generation process. Specifically, the framework comprises three key components: Temporal-Ensembling Encoder (TE Encoder), Coarse Pseudo-box Prediction, and Pseudo-box Refinement. The TE Encoder builds an anchor prototype library by aggregating temporal information for dynamic anchor adjustment. In Coarse Pseudo-box Prediction, anchors are refined using the prototype library, and a set of informative samples is collected for subsequent refinement. During Pseudo-box Refinement, these informative negative samples are used to suppress low-confidence candidate positive samples, thereby improving the quality of the pseudo-boxes. Experimental results on benchmark datasets demonstrate that PointSR significantly outperforms state-of-the-art methods, achieving up to 2.6% ∼ 7.2% higher AP50using only point supervision. Additionally, it exhibits strong robustness to perturbation in human-labeled points.

Knowledge distillation (KD) stands as an efficient technique for compressing models, typically employing a teacher-student framework. Nevertheless, optimizing KD to yield models with reduced parameters and enhanced performance remains an area warranting deeper investigation. In this article, we recognize the significance of preserving structural consistency to enhance knowledge transfer efficiency between networks. Leveraging this insight, we introduce a novel approach termed Student-Driven Knowledge Distillation (SDKD), which integrates a proxy teacher intermediary between the primary teacher and student model. Specifically, we construct the architecture of the proxy teacher entirely based on the student network to generate logits that closely align with the distribution of the student network. Besides, we propose a Feature Fusion Block (FFB) to integrate features from the teacher network into the proxy teacher. FFB can not only provide high-quality feature-based knowledge for distillation but also impart response-based knowledge to facilitate the learning process. Extensive experiments illustrate that SDKD outperforms 29 state-of-the-art methods on several tasks, including image classification, semantic segmentation, and depth estimation.

Hybrid PLLs (HPLLs) leverage the advantages of conventional analog and digital PLLs to cater to the high integration demand inherent with the advanced CMOS nodes, among which the charge-sharing (CS) integrator-based HPLL featuring ultra-compact area and ultra-low power consumption exhibits promising prospects. This work presents a comprehensive analysis concerning the CS integrator-based HPLL for the first time. The behavioral modeling is first conducted with a time-domain event-driven modeling method to simulate the piecewise-linear modulations on the oscillator frequency A noise model considering the slow-fast clock domain transitions and the digital-analog signal transitions inherent with the architecture is further proposed. The proposed models offer precise PN PSD estimations over the specified frequency range under all simulated conditions, whose integrated jitters differ by less than 5 fs compared with circuit simulations. The additional nonlinearity induced by the CS integrator is also considered, and an analytical prediction approach for the nonlinearity-induced spurs is presented, showing a capability of predicting the locations and amplitudes of the most significant spurious tones with a less than 4 % deviation in the relative offset frequency and a less than 5 dBc deviation in the relative amplitude.

Cross-view object localization (CVOGL) offers fine-grained geographic information regarding the specified object of interest, in comparison to typical cross-view geo-location at the image level. However, it faces more challenges, primarily due to significant visual appearance changes induced by viewpoint discrepancies and the difficulty of accurately identifying corresponding objects within reference images containing multiple targets. To address these challenges, we propose a novel CVOGL model termed HALO. First, we introduce a cascade cross-view feature interaction module that enables effective local-to-global feature fusion across different viewpoints, thereby enhancing the feature representation of objects in the reference image. Second, to mitigate the scale variations and feature distribution discrepancies between the query and reference images, we propose an adaptive feature hierarchization and aggregate module. It extracts more representative global descriptors by adaptively hierarchizing and aggregating features. Lastly, utilizing the learned global descriptors, we perform cross-view CL and introduce a hard sample mining strategy to further enhance the discriminative ability of the network. Through these advancements, we significantly improve the discriminative representations of the query objects at both views. Extensive experiments on the CVOGL dataset demonstrate the effectiveness and robustness of the proposed method. In the CVOGL task of drone→satellite, HALO improves 2.36% in [email protected] on the test sets. Similarly, HALO shows an increase of 2.49% in [email protected] on the validation set for the CVOGL task of ground→satellite. Our codes are publicly available at https://github.com/ZehaoZhang-Uestc/HALO.

Self-supervised learning has been gaining attention in the field of sleep stage classification. It learns representations with unlabeled electroencephalography (EEG) signals, which alleviates the cost of labeling for specialists. However, most self-supervised approaches assume only the two augmented views from the same EEG sample is a positive pair, which suffers from the false negative problem. Therefore, we propose a new model named Temporal Augmentation and False Negative Suppression (TA-FNS) to solve the problem. Specifically, it first generates two augmented views for each EEG sample. Then the temporal augmentation module is proposed to learn temporal features during sleep from augmented views. Based on temporal features, intra-view and inter-view sample similarity matrices are calculated. Finally, the false negative suppression module identifies and eliminates potential false negatives according to the consistency between intra-view and inter-view similarity matrices. TA-FNS not only achieves state-of-the-art performances on Sleep-EDF and ISRUC datasets, but also learns semantic representation from EEG of different sleep stages, which demonstrates the effectiveness of it in mitigating the false negative problem.

3D reconstruction of Unmanned Aerial Vehicle (UAV) scenes is vital for agriculture, environmental protection, urban planning, and disaster response, to name a few. However, data acquisition can be constrained and hazardous under hostile environments, which limits the image data available in real-world applications. In this work, we propose a text-driven online editing framework for UAV scenes, which can generate novel views of existing scenes with abundant editing types. Compared with small single-object scenes, large-scale UAV scene editing suffers from several particular challenges: 1) broader capturing scope exhibits illumination variation and complicated objects that reduce the 3D scene consistency after editing; and 2) high-resolution 2D editing and 3D reconstruction can be computationally expensive with tremendous GPU memory. To tackle these issues, we first design a dual-branch compact NeRF structure to reduce memory usage and enhance accuracy for 3D reconstruction. We then introduce a sub-pixel sampling scheme to expedite the generation of low-resolution images for 2D editing, followed by a super-resolution module that restores the fine details of rendered images. Additionally, we develop a grouped content filtering mechanism to improve the 3D scene consistency of the model by matching the rendering images and text descriptions, which also significantly reduces memory usage during editing. Extensive experiments demonstrate that the proposed method can achieve various editing effects, including different seasons, weather conditions, times of the day, disaster scenarios, etc. Our technique is computationally efficient and conveniently expandable for large-scale UAV scenes, alleviating data scarcity in harsh scenarios.

In this brief, a charge-prestored 21.2-MHz relaxation oscillator is proposed for ultralow-power applications. It occupies only 0.0043 mm2in 0.18-$\mu \text{m}$CMOS by resistor reusing and is reference-free. The simulated temperature coefficient (TC) of the output frequency is 15.2 ppm/° from −30 °C to 125 °C. By generating an asymmetric capacitor charging swing, our charge-prestored technique reduces significantly the power consumed by the swing-boostingRCnetwork during the charging phase. Also, the R-RCstructure further improves the energy efficiency. The total power consumption of the oscillator core is$1.806 \mu \text{W}$at 0.8 V, corresponding to an energy efficiency of$0.085 \mu \text{W}$/MHz that compares favorably with the state of the art.

The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.

In this article, we design and implement a cooperative shingle-aware file system, called CosaFS , on heterogeneous storage devices that mix solid-state drives (SSDs) and shingled magnetic recording (SMR) technology to improve the overall performance of storage systems. The basic idea of CosaFS is to classify objects as hot or cold objects based on a proposed Lookahead with Recency Weight scheme. If an object is identified as a hot (small) object, then it will be served by SSD. Otherwise, cold (large) objects are stored on SMR. For an SMR, large objects can be accessed in large sequential blocks, rendering the performance of their accesses comparable with that of accessing the same large sequential blocks as if they were stored on a hard drive. Small objects, such as inodes and directories, are stored on the SSD where “seeks” for such objects are nearly free. With thorough empirical studies, we demonstrate that CosaFS, as a cooperative shingle-aware file system, with metadata separation and cache-assistance, is a very effective way to handle the disk-based data demanded by the shingled writes and outperforms the device- and host-side shingle-aware file systems in terms of throughput, IOPS, and access latency as well.