Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition

Motion representation plays a vital role in human action recognition in videos. In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach. The OFF is derived from the definition of optical flow and is orthogonal to the optical flow. The derivation also provides theoretical support for using the difference between two frames. By directly calculating pixel-wise spatiotemporal gradients of the deep feature maps, the OFF could be embedded in any existing CNN based video action recognition framework with only a slight additional cost. It enables the CNN to extract spatiotemporal information, especially the temporal information between frames simultaneously. This simple but powerful idea is validated by experimental results. The network with OFF fed only by RGB inputs achieves a competitive accuracy of 93.3% on UCF-101, which is comparable with the result obtained by two streams (RGB and optical flow), but is 15 times faster in speed. Experimental results also show that OFF is complementary to other motion modalities such as optical flow. When the proposed method is plugged into the state-of-the-art video action recognition framework, it has 96:0% and 74:2% accuracy on UCF-101 and HMDB-51 respectively. The code for this project is available at https://github.com/kevin-ssy/Optical-Flow-Guided-Feature.Comment: CVPR 2018. code available at https://github.com/kevin-ssy/Optical-Flow-Guided-Featur

Biosynthetic engineering of new pactamycins

Among the myriad of naturally occurring bioactive compounds are the aminocyclopentitol-containing natural products that represent a family of sugar-derived microbial secondary metabolites, such as the antibiotics pactamycin, allosamidin, and trehazolin. Pactamycin, a structurally unique aminocyclitol antibiotic isolated from Streptomyces pactum, consists of a 5-membered ring aminocyclitol (cyclopentitol) unit, two aromatic rings (6-methylsalicylic acid (6-MSA) and 1-(3-Amino-phenyl)-ethanone or 3-aminoacetophenone) and a 1,1-dimethylurea. It has pronounced antibacterial, antitumor, antiviral, and antiplasmodial activities, but its development as a clinical drug was hampered by its broad cytotoxicity. Efforts to modulate its pharmacological and toxicity properties by structural modifications using synthetic organic chemistry have been difficult due to the complexity of its chemical structure. As part of our ongoing studies on the biosynthesis of aminocyclitol-derived bioactive natural products, we have identified the biosynthetic gene cluster of pactamycin in S. pactum ATCC 27456, which paves the way for a better understanding of pactamycin biosynthesis and generating novel pactamycin analogs through biosynthetic engineering. Through gene inactivations, feeding experiments, and in vitro enzymatic assay, we studied the biosynthesis of pactamycin, which include the modes of formation of the unique cyclopentitol unit, the 3-aminoacetophenone and the 6-methyl salicylic acid moieties. Armed with the tools needed to genetically engineer target strains of S. pactum, we were able to produce novel analogs of this untapped-class of natural products. TM-026 was generated from a ΔptmH (a radical SAM C-methyltransferase gene) mutant, whereas TM-025 was generated from a ΔptmH/ΔptmQ (a polyketide synthase gene) double knockout mutant. Both compounds show potent antimalarial activity, but lack significant antibacterial activity, and are about 10-30 times less toxic than pactamycin toward mammalian cells. The results suggest that distinct ribosomal binding selectivity or new mechanism(s) of action may be involved in their plasmodial growth inhibition, which may lead to the discovery of new antimalarial drugs and identification of new molecular targets within malarial parasites. TM-035 was also isolated from a ΔptmH mutant. However, we found that TM-035 showed no activity against bacteria, malarial parasites, and most tested mammalian cells, but it has potent growth inhibitory activity against two well-established human head and neck squamous cell carcinomas (SCC025 and SCC104) (IC₅₀ 725 nM) in an in vitro assay. More intriguingly, the compound is significantly less active against human primary epidermal keratinocytes (HPEK), demonstrating an interesting biological phenomenon and outstanding cell type selectivity, which may lead to the development of new anticancer chemotherapy. The production yield of pactamycin and its congeners under laboratory conditions is relatively low. This has hampered both mechanistic and preclinical studies of these promising compounds. To deepen our understanding of pactamycin biosynthesis and engineer mutant strains with improved production yields, we investigated pathway specific regulatory genes, ptmF and ptmE. Based on gene inactivation and RT-PCR studies, we found that the PtmF-PtmE system controls the transcription of the whole biosynthetic gene cluster. The results provide important insight into regulation of pactamycin biosynthesis and will contribute to future studies that aim at engineering high producing strains of S. pactum

V2V Routing in VANET Based on Heuristic Q-Learning

Designing efficient routing algorithms in vehicular ad hoc networks (VANETs) plays an important role in the emerging intelligent transportation systems. In this paper, a routing algorithm based on the improved Q-learning is proposed for vehicle-to-vehicle (V2V) communications in VANETs. Firstly, a link maintenance time model is established, and the maintenance time is taken as an important parameter in the design of routing algorithm to ensure the reliability of each hop link. Aiming at the low efficiency and slow convergence of Q-learning, heuristic function and evaluation function are introduced to accelerate the update of Q-value of current optimal action, reduce unnecessary exploration, accelerate the convergence speed of Q-learning process and improve learning efficiency. The learning task is dispersed in each vehicle node in the new routing algorithm and it maintains the reliable routing path by periodically exchanging beacon information with surrounding nodes, guides the node’s forwarding action by combining the delay information between nodes to improve the efficiency of data forwarding. The performance of the algorithm is evaluated by NS2 simulator. The results show that the algorithm has a good effect on the package delivery rate and end-to-end delay

Modification of Transition-Metal Redox by Interstitial Water in Hexacyanometalate Electrodes for Sodium-Ion Batteries.

A sodium-ion battery (SIB) solution is attractive for grid-scale electrical energy storage. Low-cost hexacyanometalate is a promising electrode material for SIBs because of its easy synthesis and open framework. Most hexacyanometalate-based SIBs work with aqueous electrolyte, and interstitial water in the material has been found to strongly affect the electrochemical profile, but the mechanism remains elusive. Here we provide a comparative study of the transition-metal redox in hexacyanometalate electrodes with and without interstitial water based on soft X-ray absorption spectroscopy and theoretical calculations. We found distinct transition-metal redox sequences in hydrated and anhydrated NaxMnFe(CN)6·zH2O. The Fe and Mn redox in hydrated electrodes are separated and are at different potentials, leading to two voltage plateaus. On the contrary, mixed Fe and Mn redox in the same potential range is found in the anhydrated system. This work reveals for the first time how transition-metal redox in batteries is strongly affected by interstitial molecules that are seemingly spectators. The results suggest a fundamental mechanism based on three competing factors that determine the transition-metal redox potentials. Because most hexacyanometalate electrodes contain water, this work directly reveals the mechanism of how interstitial molecules could define the electrochemical profile, especially for electrodes based on transition-metal redox with well-defined spin states

I2^2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation

Recent progresses on self-supervised 3D human action representation learning are largely attributed to contrastive learning. However, in conventional contrastive frameworks, the rich complementarity between different skeleton modalities remains under-explored. Moreover, optimized with distinguishing self-augmented samples, models struggle with numerous similar positive instances in the case of limited action categories. In this work, we tackle the aforementioned problems by introducing a general Inter- and Intra-modal Mutual Distillation (I$^2$MD) framework. In I$^2$MD, we first re-formulate the cross-modal interaction as a Cross-modal Mutual Distillation (CMD) process. Different from existing distillation solutions that transfer the knowledge of a pre-trained and fixed teacher to the student, in CMD, the knowledge is continuously updated and bidirectionally distilled between modalities during pre-training. To alleviate the interference of similar samples and exploit their underlying contexts, we further design the Intra-modal Mutual Distillation (IMD) strategy, In IMD, the Dynamic Neighbors Aggregation (DNA) mechanism is first introduced, where an additional cluster-level discrimination branch is instantiated in each modality. It adaptively aggregates highly-correlated neighboring features, forming local cluster-level contrasting. Mutual distillation is then performed between the two branches for cross-level knowledge exchange. Extensive experiments on three datasets show that our approach sets a series of new records.Comment: submitted to IJCV. arXiv admin note: substantial text overlap with arXiv:2208.1244