GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers

Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge in computer vision, where state-of-the-art methods are mainly based on full supervision. As annotating ground truth dense instance masks is tedious and expensive, solving 3DIS with weak supervision has become more practical. In this paper, we propose GaPro, a new instance segmentation for 3D point clouds using axis-aligned 3D bounding box supervision. Our two-step approach involves generating pseudo labels from box annotations and training a 3DIS network with the resulting labels. Additionally, we employ the self-training strategy to improve the performance of our method further. We devise an effective Gaussian Process to generate pseudo instance masks from the bounding boxes and resolve ambiguities when they overlap, resulting in pseudo instance masks with their uncertainty values. Our experiments show that GaPro outperforms previous weakly supervised 3D instance segmentation methods and has competitive performance compared to state-of-the-art fully supervised ones. Furthermore, we demonstrate the robustness of our approach, where we can adapt various state-of-the-art fully supervised methods to the weak supervision task by using our pseudo labels for training. The source code and trained models are available at https://github.com/VinAIResearch/GaPro.Comment: Accepted to ICCV 202

Deep Learning-Aided Multicarrier Systems

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity

Autumn rainfall increasing trend in south central Vietnam and its association with changes in Vietnam’s East Sea surface temperature

Certain parts of Southeast Asia, such as central Vietnam, experience heavy rainfall in the boreal autumn from September to December (SOND). The 52-year SOND rainfall over Vietnam from 1961 to 2012 shows increasing trends over the south central region (SR). Along the central coastal regions and SR, SOND rainfall as well as heavy rainfall indices, such as the number of heavy rainfall days, have increased significantly since the late 1980s and early 1990s. In contrast, a decreasing trend is observed in stations located north of 17°N. Tropical cyclone-induced rainfall exhibits an increasing trend over the SR. The increasing trend of SOND rainfall is associated with the recent sea surface temperature (SST) warming after the late 1980s over the South Vietnam East Sea (SVES). Owing to the recent SST warming and grand La Niña-like pattern after the 1990s, the SVES surface temperature has increased by 0.8–1.2 °C over the period 1961–2012, leading to enhanced moisture flux convergence over the SR. Moreover, the SVES warming strengthens the anomalous northeasterly winds that affect the SR. Consequently, SR has become more prone to deep convection and heavy rainfall events

Efficient Detectors based on Group Detection for Massive MIMO systems

In Multiple Input Multiple Output (MIMO) systems, the complexities of detectors depend on the size of the channel matrix. In Massive MIMO systems, detection complexity becomes remarkably higher because the dimensions of the channel matrix get much larger. In order to recover the signals in the up-link of a Massive MIMO system at reduced complexities, we first divide the system into two sub-systems. After that, we apply the Minimum Mean Square Error (MMSE) and Bell Laboratory Layer Space Time (BLAST) detectors to each subsystem, resulting in the so-called MMSE-GD and BLAST-GD detectors, respectively. To further enhance the BER performance of Massive MIMO systems under the high-load conditions, we propose two additional detectors, called MMSE-IGD and BLAST-IGD by respectively applying the conventional MMSE and BLAST on the sub-systems in an iterative manner. It is shown via computer simulation and analytical results that the proposed detectors enable the system to achieve not only higher BER performance but also low detection complexities as compared to the conventional linear detectors. Moreover, the MMSE-IGD and BLAST-IGD can significantly improve BER performance of Massive MIMO systems

The potential for REDD+ to reduce forest degradation in Vietnam

Natural forests in Vietnam have experienced rapid declines in the last 70 years, as a result of degradation from logging and conversion of natural forests to timber and rubber plantations. Degradation of natural forests leads to loss of biodiversity and ecosystem services, impacting the livelihoods of surrounding communities. Efforts to address ongoing loss of natural forests, through mechanisms such as Reduced Emissions from Deforestation and Degradation (REDD+), require an understanding of the links between forest degradation and the livelihoods of local communities, which have rarely been studied in Vietnam. We combined information from livelihood surveys, remote sensing and forest inventories around a protected natural forest area in North Central Vietnam. For forest-adjacent communities, we found natural forests contributed an average of 28% of total household income with plantation forests contributing an additional 15%. Although officially prohibited, logging contributed more than half of the total income derived from natural forests. Analysis of Landsat images over the period 1990 to 2014 combined with forest inventory data, demonstrates selective logging was leading to ongoing degradation of natural forests resulting in loss of 3.3±0.8 Mg biomass ha-1 yr-1 across the protected area. This is equivalent to 1.5% yr-1 of total forest biomass, with rates as high as 3% yr-1 in degraded and easily accessible parts of the protected area. We estimate that preventing illegal logging would incur local opportunity costs of USD $4.10±0.90 per Mg CO2, similar to previous estimates for tropical forest protected areas and substantially less than the opportunity costs in timber or agricultural concessions. Our analysis suggests activities to reduce forest degradation in protected areas are likely to be financially viable through Vietnam's REDD+ program

Evaluation of EBNA-1 (epstein-barr virus nuclear antigen-1) gene prevalence in nasopharyngeal carcinoma in Vietnamese patients

This study examined the presence of Epstein-Barr virus (EBV) in nasopharyngeal carcinoma (NPC) based on the detection of EBNA-1 (Epstein-Barr virus nuclear antigen-1) by Polymerase Chain Reaction (PCR), in Vietnamese population. Firstly, we systematically analyzed the mean of percentage weighted of the presence of EBNA-1 in previous relevant studies. Experimentally, 31 nasopharyngeal cancer biopsies and 20 healthy samples were enrolled in current to evaluate the frequency of candidate genes. As the results, the frequency of EBNA-1 was 77.42%, whereas, none of any cases of healthy samples were found to positive to target gene. The p value < 0.05 (p = 0.0001) showed that it was significant correlation between the presence of this candidate gene and nasopharyngeal cancer. Moreover, a high odds ratio (OR) and relative risk (RR) of candidate gene, (OR = 68.16, RR = 2.41) were calculated. Therefore, the detection of EBNA-1, which performed by PCR, could serve as a good supplement to early diagnosis and prognosis of NPC in Vietnamese population