Self-Supervised Visual Representation Learning with Semantic Grouping

In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still, they commonly rely on hand-crafted objectness priors or specialized pretext tasks to build a learning framework, which may harm generalizability. Instead, we propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning. The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots. Based on the learned data-dependent slots, a contrastive objective is employed for representation learning, which enhances the discriminability of features, and conversely facilitates grouping semantically coherent pixels together. Compared with previous efforts, by simultaneously optimizing the two coupled objectives of semantic grouping and contrastive learning, our approach bypasses the disadvantages of hand-crafted priors and is able to learn object/group-level representations from scene-centric images. Experiments show our approach effectively decomposes complex scenes into semantic groups for feature learning and significantly benefits downstream tasks, including object detection, instance segmentation, and semantic segmentation. Code is available at: https://github.com/CVMI-Lab/SlotCon.Comment: Accepted at NeurIPS 202

MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition

Multilingual text recognition (MLTR) systems typically focus on a fixed set of languages, which makes it difficult to handle newly added languages or adapt to ever-changing data distribution. In this paper, we propose the Incremental MLTR (IMLTR) task in the context of incremental learning (IL), where different languages are introduced in batches. IMLTR is particularly challenging due to rehearsal-imbalance, which refers to the uneven distribution of sample characters in the rehearsal set, used to retain a small amount of old data as past memories. To address this issue, we propose a Multiplexed Routing Network (MRN). MRN trains a recognizer for each language that is currently seen. Subsequently, a language domain predictor is learned based on the rehearsal set to weigh the recognizers. Since the recognizers are derived from the original data, MRN effectively reduces the reliance on older data and better fights against catastrophic forgetting, the core issue in IL. We extensively evaluate MRN on MLT17 and MLT19 datasets. It outperforms existing general-purpose IL methods by large margins, with average accuracy improvements ranging from 10.3% to 35.8% under different settings. Code is available at https://github.com/simplify23/MRN.Comment: Accepted by ICCV 202

SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation

High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine non-convex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results

Initial Velocity Effect on Acceleration Fall of a Spherical Particle through Still Fluid

A spherical particle’s acceleration fall through still fluid was investigated analytically and experimentally using the Basset-Boussinesq-Oseen equation. The relationship between drag coefficient and Reynolds number was studied, and various parameters in the drag coefficient equation were obtained with respect to the small, medium, and large Reynolds number zones. Next, some equations were used to derive the finite fall time and distance equations in terms of certain assumptions. A simple experiment was conducted to measure the fall time and distance for a spherical particle falling through still water. Sets of experimental data were used to validate the relationship between fall velocity, time, and distance. Finally, the initial velocity effect on the total fall time and distance was discussed with different terminal Reynolds numbers, and it was determined that the initial velocity plays a more important role in the falling motion for small terminal Reynolds numbers than for large terminal Reynolds number scenarios

A van der Waals pn heterojunction with organic/inorganic semiconductors

van der Waals (vdW) heterojunctions formed by two-dimensional (2D) materials have attracted tremendous attention due to their excellent electrical/optical properties and device applications. However, current 2D heterojunctions are largely limited to atomic crystals, and hybrid organic/inorganic structures are rarely explored. Here, we fabricate hybrid 2D heterostructures with p-type dioctylbenzothienobenzothiophene (C8-BTBT) and n-type MoS2. We find that few-layer C8-BTBT molecular crystals can be grown on monolayer MoS2 by vdW epitaxy, with pristine interface and controllable thickness down to monolayer. The operation of the C8-BTBT/MoS2 vertical heterojunction devices is highly tunable by bias and gate voltages between three different regimes: interfacial recombination, tunneling and blocking. The pn junction shows diode-like behavior with rectifying ratio up to 105 at the room temperature. Our devices also exhibit photovoltaic responses with power conversion efficiency of 0.31% and photoresponsivity of 22mA/W. With wide material combinations, such hybrid 2D structures will offer possibilities for opto-electronic devices that are not possible from individual constituents.Comment: 16 pages, 4 figure

Availability of essential medicines, progress and regional distribution in China: a systematic review and meta-analysis

BackgroundEssential medicines are the backbone of healthcare and meet the priority healthcare needs of the population. However, approximately one-third of the global population does not have access to essential medicines. Although China formulated essential medicine policies in 2009, the progress of availability of essential medicines and regional variations remains unknown. Therefore, this study was conducted to evaluate the availability of essential medicines, their progress, and regional distribution in China in the last decade.MethodsWe searched eight databases from their inception to February 2022, relevant websites, and reference lists of included studies. Two reviewers selected studies, extracted data, and evaluated the risk of bias independently. Meta-analyses were performed to quantify the availability of essential medicines, their progress, and regional distribution.ResultsOverall 36 cross-sectional studies conducted from 2009 to 2019 were included, with regional data for 14 provinces. The availability of essential medicines in 2015–2019 [28.1%, 95% confidence interval (CI): 26.4–29.9%] was similar to that in 2009–2014 (29.4%, 95% CI: 27.5–31.3%); lower in the Western region (19.8%, 95% CI: 18.1–21.5%) than Eastern (33.8%, 95% CI: 31.6–36.1%) and Central region (34.5%, 95% CI: 30.6–38.5%); very low for 8 Anatomical Therapeutic Chemical (ATC) categories (57.1%), and low for 5 categories (35.7%) among all ATC groups.ConclusionThe availability of essential medicines in China is low compared with the World Health Organization goal, has not changed much in the last decade, is unequal across regions, and lacks data for half of provinces. For policy-making, the monitoring system of the availability of essential medicines is to be strengthened to enable long-term surveillance, especially in provinces where the data has been missing. Meanwhile, Joint efforts from all stakeholders are warranted to improve the availability of essential medicines in China toward the universal health coverage target.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=315267, identifier: PROSPERO CRD42022315267