Transcribing Content from Structural Images with Spotlight Mechanism

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18

Efficient and convenient synthesis of symmetrical carboxylic anhydrides from carboxylic acids with sulfated zirconia by phase transfer catalysis

An efficient and convenient procedure for the synthesis of symmetrical carboxylic anhydrides from carboxylic acids with sulfated zirconia by PEG-1000 phase transfer catalysis has been developed. The reactions proceeded under mild and solvent-free conditions to provide the carboxylic anhydrides in good to excellent yields. The product can be isolated by a simple extraction with organic solvent, and the catalyst system can be recycled or reused without any significant loss of catalytic activity.KEY WORDS: Carboxylic anhydrides, Carboxylic acids, SO42-/ZrO2, Phase transfer catalysis Bull. Chem. Soc. Ethiop. 2011, 25(2), 255-262

FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture

Neural Network (NN) accelerators with emerging ReRAM (resistive random access memory) technologies have been investigated as one of the promising solutions to address the \textit{memory wall} challenge, due to the unique capability of \textit{processing-in-memory} within ReRAM-crossbar-based processing elements (PEs). However, the high efficiency and high density advantages of ReRAM have not been fully utilized due to the huge communication demands among PEs and the overhead of peripheral circuits. In this paper, we propose a full system stack solution, composed of a reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and its software system including neural synthesizer, temporal-to-spatial mapper, and placement & routing. We highly leverage the software system to make the hardware design compact and efficient. To satisfy the high-performance communication demand, we optimize it with a reconfigurable routing architecture and the placement & routing tool. To improve the computational density, we greatly simplify the PE circuit with the spiking schema and then adopt neural synthesizer to enable the high density computation-resources to support different kinds of NN operations. In addition, we provide spiking memory blocks (SMBs) and configurable logic blocks (CLBs) in hardware and leverage the temporal-to-spatial mapper to utilize them to balance the storage and computation requirements of NN. Owing to the end-to-end software system, we can efficiently deploy existing deep neural networks to FPSA. Evaluations show that, compared to one of state-of-the-art ReRAM-based NN accelerators, PRIME, the computational density of FPSA improves by 31x; for representative NNs, its inference performance can achieve up to 1000x speedup.Comment: Accepted by ASPLOS 201

k-semistratifiable spaces and expansions of set-valued mappings

[EN] In this paper, the concept of k-upper semi-continuous set-valued mappings is introduced. Using this concept, we give characterizations of k-semistratifiable and k-MCM spaces, which answers a question posed by Xie and Yan.Yan, P.; Hu, X.; Xie, L. (2018). k-semistratifiable spaces and expansions of set-valued mappings. Applied General Topology. 19(1):145-153. doi:10.4995/agt.2018.7883SWORD14515319

Alterations of dendritic cell subsets in the peripheral circulation of patients with cervical carcinoma

Patients with cervical carcinoma (CC) are frequently immunocompromised. Dendritic cells (DCs) are potent antigen-presenting cells. Using multicolor flow cytometry, the percentages of CD11c+ (DC1) and CD123+ (DC2) subsets, were determined in the peripheral blood of 37 patients with cervical carcinoma (CC), 54 patients with CIN, and 62 healthy individuals. A substantial reduction of circulating dendritic cells and accordingly immunodepression may be associated with increased IL-6 and TGF-β in serum. These findings could give expression to the immunosuppression of circulating dendritic cells in patients with CC and CIN, thus, may indicate novel aspects of cervical carcinoma immune evasion