Few-Shot Object Detection via Synthetic Features with Optimal Transport

Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of novel classes that lack diversity. Hence, they may not be sufficient to capture the data distribution. To address that limitation, in this paper, we propose a novel approach in which we train a generator to generate synthetic data for novel classes. Still, directly training a generator on the novel class is not effective due to the lack of novel data. To overcome that issue, we leverage the large-scale dataset of base classes. Our overarching goal is to train a generator that captures the data variations of the base dataset. We then transform the captured variations into novel classes by generating synthetic data with the trained generator. To encourage the generator to capture data variations on base classes, we propose to train the generator with an optimal transport loss that minimizes the optimal transport distance between the distributions of real and synthetic data. Extensive experiments on two benchmark datasets demonstrate that the proposed method outperforms the state of the art. Source code will be available

MirrorNet: Bio-Inspired Camouflaged Object Segmentation

Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively. The output from the mirror stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts. Project Page: https://sites.google.com/view/ltnghia/research/camoComment: Under Revie

MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism depends on prototypes (\eg mean of $K-$shot) for prediction, leading to performance instability. To overcome the disadvantage of the point estimation mechanism, we propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask, which is conditioned on an object region and $K-$shot information. Inspired by augmentation approaches that perturb data with Gaussian noise for populating low data density regions, we model the mask distribution with a diffusion probabilistic model. We also propose to utilize classifier-free guided mask sampling to integrate category information into the binary mask generation process. Without bells and whistles, our proposed method consistently outperforms state-of-the-art methods on both base and novel classes of the COCO dataset while simultaneously being more stable than existing methods. The source code is available at: https://github.com/minhquanlecs/MaskDiff.Comment: Accepted at AAAI 2024 (oral presentation

Simultaneous quantitative analyses of Tanshinone I, Cryptotanshinone, and Tanshinone IIA in Danshen (Salvia miltiorrhiza Bunge) cultivated in Vietnam using LC-MS/MS

By using chromatography methods, the principal compounds tanshinon I, cryptotanshinone, tanshinone IIA were isolated from danshen (Salvia miltiorrhiza Bunge). Based on the spectroscopic data (1H-NMR, 13C-NMR and ESI-MS mass spectra), the structures were determined. The compound was purified (purity > 99.8%) by Agilent 218 purification system, which was used as the standard for analyzing tanshinon I, cryptotanshinone, tanshinone IIA in six samples. In this study, one LC-MS/MS method was developed for the simultaneous quantitative determination of three bioactive principles, tanshinone I, cryptotanshinone, and tanshinone IIA in Radix Salviae miltiorrhizae (RSM, the root of S. miltiorrhiza). The quantification of these diterpenoids is based on the fragments of [M+H]+ under collision-activated conditions and in selected reaction monitoring (SRM) mode. The quantitative method is validated by determining the mean recovery from fortified samples of tanshinone I, cryptotanshinone, and tanshinone IIA as higher than 98%. The established method is successfully applied to the quality assessment of six batches of RSM samples collected from different regions of Vietnam. The results show that Lam Dong sample has the highest tanshinone I content (4.4286±0.0009 µg/mg), meanwhile Muong Long sample has the lowest (1.2717±0.0013µg/mg). Lam Dong sample has the highest cryptotanshinone content (8.1589±0.0006 µg/mg), whereas Guangxi-China sample has the lowest (2.8630±0.0008 µg/mg). Ha Giang sample has the highest tanshinone IIA content (13.0252±0.0004 µg/mg), whereas Muong Long sample has the lowest (3.8278±0.0003 µg/mg)

Simultaneous quantitative analyses of Tanshinone I, Cryptotanshinone, and Tanshinone IIA in Danshen (Salvia miltiorrhiza Bunge) cultivated in Vietnam using LC-MS/MS

74-83By using chromatography methods, the principal compounds tanshinon I, cryptotanshinone, tanshinone IIA were isolated from danshen (Salvia miltiorrhiza Bunge). Based on the spectroscopic data (1H-NMR, 13C-NMR and ESI-MS mass spectra), the structures were determined. The compound was purified (purity > 99.8%) by Agilent 218 purification system, which was used as the standard for analyzing tanshinon I, cryptotanshinone, tanshinone IIA in six samples. In this study, one LC-MS/MS method was developed for the simultaneous quantitative determination of three bioactive principles, tanshinone I, cryptotanshinone, and tanshinone IIA in Radix Salviae miltiorrhizae (RSM, the root of S. miltiorrhiza). The quantification of these diterpenoids is based on the fragments of [M+H]+ under collision-activated conditions and in selected reaction monitoring (SRM) mode. The quantitative method is validated by determining the mean recovery from fortified samples of tanshinone I, cryptotanshinone, and tanshinone IIA as higher than 98%. The established method is successfully applied to the quality assessment of six batches of RSM samples collected from different regions of Vietnam. The results show that Lam Dong sample has the highest tanshinone I content (4.4286±0.0009 µg/mg), meanwhile Muong Long sample has the lowest (1.2717±0.0013µg/mg). Lam Dong sample has the highest cryptotanshinone content (8.1589±0.0006 µg/mg), whereas Guangxi-China sample has the lowest (2.8630±0.0008 µg/mg). Ha Giang sample has the highest tanshinone IIA content (13.0252±0.0004 µg/mg), whereas Muong Long sample has the lowest (3.8278±0.0003 µg/mg)

The Vietnam Initiative on Zoonotic Infections (VIZIONS): A Strategic Approach to Studying Emerging Zoonotic Infectious Diseases

The effect of newly emerging or re-emerging infectious diseases of zoonotic origin in human populations can be potentially catastrophic, and large-scale investigations of such diseases are highly challenging. The monitoring of emergence events is subject to ascertainment bias, whether at the level of species discovery, emerging disease events, or disease outbreaks in human populations. Disease surveillance is generally performed post hoc, driven by a response to recent events and by the availability of detection and identification technologies. Additionally, the inventory of pathogens that exist in mammalian and other reservoirs is incomplete, and identifying those with the potential to cause disease in humans is rarely possible in advance. A major step in understanding the burden and diversity of zoonotic infections, the local behavioral and demographic risks of infection, and the risk of emergence of these pathogens in human populations is to establish surveillance networks in populations that maintain regular contact with diverse animal populations, and to simultaneously characterize pathogen diversity in human and animal populations. Vietnam has been an epicenter of disease emergence over the last decade, and practices at the human/animal interface may facilitate the likelihood of spillover of zoonotic pathogens into humans. To tackle the scientific issues surrounding the origins and emergence of zoonotic infections in Vietnam, we have established The Vietnam Initiative on Zoonotic Infections (VIZIONS). This countrywide project, in which several international institutions collaborate with Vietnamese organizations, is combining clinical data, epidemiology, high-throughput sequencing, and social sciences to address relevant one-health questions. Here, we describe the primary aims of the project, the infrastructure established to address our scientific questions, and the current status of the project. Our principal objective is to develop an integrated approach to the surveillance of pathogens circulating in both human and animal populations and assess how frequently they are exchanged. This infrastructure will facilitate systematic investigations of pathogen ecology and evolution, enhance understanding of viral cross-species transmission events, and identify relevant risk factors and drivers of zoonotic disease emergence

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke