Detecting the Unexpected via Image Resynthesis

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes can appear at test time. The main trends in this area either leverage the notion of prediction uncertainty to flag the regions with low confidence as unknown, or rely on autoencoders and highlight poorly-decoded regions. Having observed that, in both cases, the detected regions typically do not correspond to unexpected objects, in this paper, we introduce a drastically different strategy: It relies on the intuition that the network will produce spurious labels in regions depicting unexpected objects. Therefore, resynthesizing the image from the resulting semantic map will yield significant appearance differences with respect to the input image. In other words, we translate the problem of detecting unknown classes to one of identifying poorly-resynthesized image regions. We show that this outperforms both uncertainty- and autoencoder-based methods

VISAPP 2015.pdf

Accepted for Visapp 2015

A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways

The steady increase in the number of vehicles operating on the highways continues to exacerbate congestion, accidents, energy consumption, and greenhouse gas emissions. Emerging mobility systems, e.g., connected and automated vehicles (CAVs), have the potential to directly address these issues and improve transportation network efficiency and safety. In this paper, we consider a highway merging scenario and propose a framework for coordinating CAVs such that stop-and-go driving is eliminated. We use a decentralized form of the actor-critic approach to deep reinforcement learning$-$multi-agent deep deterministic policy gradient. We demonstrate the coordination of CAVs through numerical simulations and show that a smooth traffic flow is achieved by eliminating stop-and-go driving. Videos and plots of the simulation results can be found at this supplemental $\href{https://sites.google.com/view/ud-ids-lab/MADRL}{\text{site}}$.Comment: 6 pages, 6 figure

A sensitive UPLC-MS/MS method for the simultaneous assay and trace level genotoxic impurities quantification of SARS-CoV-2 inhibitor-Molnupiravir in its pure and formulation dosage forms using fractional factorial design

Two potential genotoxic impurities were identified (PGTIs)-viz. 4-amino-1-((2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)tetrahydrofuran-2-yl)pyrimidin-2(1H)-one (PGTI-1), and 1-(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)tetrahydrofuran-2-yl)pyrimidin-2,4(1H,3H)-one (PGTI-II) in the Molnupiravir (MOPR) synthetic routes. COVID-19 disease was treated with MOPR when mild to moderate symptoms occurred. Two (Q)-SAR methods were used to assess the genotoxicity, and projected results were positive and categorized into Class-3 for both PGTIs. A simple, accurate and highly sensitive ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) method was optimized for the simultaneous quantification of the assay, and these impurities in MOPR drug substance and formulation dosage form. The multiple reaction monitoring (MRM) technique was utilized for the quantification. Prior to the validation study, the UPLC-MS method conditions were optimised using fractional factorial design (FrFD). The optimized Critical Method Parameters (CMPs) include the percentage of Acetonitrile in MP B, Concentration of Formic acid in MP A, Cone Voltage, Capillary Voltage, Collision gas flow and Desolvation temperature were determined from the numerical optimization to be 12.50 %, 0.13 %, 13.6 V, 2.6 kV, 850 L/hr and 375 °C, respectively. The optimized chromatographic separation achieved on Waters Acquity HSS T3 C18 column (100 mm × 2.1 mm, 1.8 µm) in a gradient elution mode with 0.13% formic acid in water and acetonitrile as mobile phases, column temperature kept at 35 °C and flow rate at 0.5 mL/min. The method was successfully validated as per ICH guidelines, and demonstrated excellent linearity over the concentration range of 0.5–10 ppm for both PGTIs. The Pearson correlation coefficient of each impurity and MOPR was found to be higher than 0.999, and the recoveries were in between the range of 94.62 to 104.05% for both PGTIs and 99.10 to 100.25% for MOPR. It is also feasible to utilise this rapid method to quantify MOPR accurately in biological samples