ODN: Opening the Deep Network for Open-set Action Recognition

In recent years, the performance of action recognition has been significantly improved with the help of deep neural networks. Most of the existing action recognition works hold the \textit{closed-set} assumption that all action categories are known beforehand while deep networks can be well trained for these categories. However, action recognition in the real world is essentially an \textit{open-set} problem, namely, it is impossible to know all action categories beforehand and consequently infeasible to prepare sufficient training samples for those emerging categories. In this case, applying closed-set recognition methods will definitely lead to unseen-category errors. To address this challenge, we propose the Open Deep Network (ODN) for the open-set action recognition task. Technologically, ODN detects new categories by applying a multi-class triplet thresholding method, and then dynamically reconstructs the classification layer and "opens" the deep network by adding predictors for new categories continually. In order to transfer the learned knowledge to the new category, two novel methods, Emphasis Initialization and Allometry Training, are adopted to initialize and incrementally train the new predictor so that only few samples are needed to fine-tune the model. Extensive experiments show that ODN can effectively detect and recognize new categories with little human intervention, thus applicable to the open-set action recognition tasks in the real world. Moreover, ODN can even achieve comparable performance to some closed-set methods.Comment: 6 pages, 3 figures, ICME 201

Modified Volterra model-based non-linear model predictive control of IC engines with real-time simulations

Modelling of non-linear dynamics of an air manifold and fuel injection in an internal combustion (IC) engine is investigated in this paper using the Volterra series model. Volterra model-based non-linear model predictive control (NMPC) is then developed to regulate the air–fuel ratio (AFR) at the stoichiometric value. Due to the significant difference between the time constants of the air manifold dynamics and fuel injection dynamics, the traditional Volterra model is unable to achieve a proper compromise between model accuracy and complexity. A novel method is therefore developed in this paper by using different sampling periods, to reduce the input terms significantly while maintaining the accuracy of the model. The developed NMPC system is applied to a widely used IC engine benchmark, the mean value engine model. The performance of the controlled engine under real-time simulation in the environment of dSPACE was evaluated. The simulation results show a significant improvement of the controlled performance compared with a feed-forward plus PI feedback control

[μ-1,3-Bis(3,5-dimethyl-1H-pyrazol-1-yl-κN 2)propan-2-olato-κ2 O:O]bis­[(ethanol-κO)zinc(II)] bis­(perchlorate)

In the centrosymmetric dinuclear title complex, [Zn2(C13H19N4O)2(C2H5OH)2](ClO4)2, the ZnII atom is in a distorted trigonal-bipyramidal coordination geometry. The equatorial plane is constructed by one N atom and one O atom from two 1,3-bis­(3,5-dimethyl­pyrazol-1-yl)propan-2-olate (bppo) ligands and one O atom from an ethanol mol­ecule. One N atom and one O atom from the two bppo ligands occupy the axial positions. Inter­molecular O—H⋯O hydrogen bonds between the ethanol mol­ecules and perchlorate anions, and O⋯π inter­actions between the perchlorate anions and pyrazole rings [O⋯centroid distances = 3.494 (3) and 3.413 (3) Å], lead to a chain structure along [010]

Development of a Diagnostic Marker for \u3cem\u3ePhlebotomus papatasi\u3c/em\u3e to Initiate a Potential Vector Surveillance Program in North America

Phlebotomus papatasi, an Old World sand fly species, is primarily responsible for the transmission of leishmaniasis, a highly infectious and potentially lethal disease. International travel, especially military rotations, between domestic locations and P. papatasi-prevalent regions in the Middle East poses an imminent threat to the public health of US citizens. Because of its small size and cryptic morphology, identification of P. papatasi is challenging and labor-intensive. Here, we developed a ribosomal DNA-polymerase chain reaction (PCR)-based diagnostic assay that is capable of detecting P. papatasi genomic DNA from mixed samples containing multiple sand flies native to the Americas. Serial dilution of P. papatasi samples demonstrated that this diagnostic assay could detect one P. papatasi from up to 255 non-target sand flies. Due to its simplicity, sensitivity and specificity, this rapid identification tool is suited for a long-term surveillance program to screen for the presence of P. papatasi in the continental United States and to reveal geographical regions potentially vulnerable to sand fly-borne diseases