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   null (Ed.)

   Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the features extracted by offline networks, in this paper, we pro- pose an approach to jointly train the components of cross- modal retrieval framework with metadata, and enable the network to find optimal features. The proposed end-to-end framework is updated with three loss functions: 1) a novel cross-modal center loss to eliminate cross-modal discrepancy, 2) cross-entropy loss to maximize inter-class variations, and 3) mean-square-error loss to reduce modality variations. In particular, our proposed cross-modal center loss minimizes the distances of features from objects belonging to the same class across all modalities. Extensive experiments have been conducted on the retrieval tasks across multi-modalities including 2D image, 3D point cloud and mesh data. The proposed framework significantly outperforms the state-of-the-art methods for both cross-modal and in-domain retrieval for 3D objects on the ModelNet10 and ModelNet40 datasets. 

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2. Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition

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5. Cross-chain Transactions

   https://doi.org/10.1109/ICBC48266.2020.9169477  

   Shadab, Narges; Houshmand, Farzin; Lesani, Mohsen (May 2020, 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC))

   null (Ed.)

   The value of cryptocurrencies is highly volatile and investors require fast and reliable exchange systems. In cross-chain transactions, multiple parties exchange assets across multiple blockchains which can be represented as a directed graph with vertexes V as parties and edges E as asset transfers. In a simple form, cross-chain transactions are cross-chain swaps where each edge e transfers an asset that the head of e already owns. However, in general, a cross-chain transaction includes a sequence of exchanges at each blockchain. Further, transactions may have off-chain steps and hence may not be strongly connected. Given a transaction, protocols are desired that guarantee the following property called uniformity. If all parties conform to the protocol, all the assets should be transferred. Further, if any party deviates from the protocol, the conforming parties should not experience any loss. Previous work introduced a uniform protocol for strongly connected cross-chain swaps and showed that no uniform protocol exists for transactions that are not strongly connected. We present a uniform protocol for general cross-chain transactions with sequenced and off-chain steps when a few certain parties are conforming. Further, we prove a new property called end-to-end that guarantees that if the source parties pay, the sink parties are paid. We present a synthesis tool called XCHAIN that given a high-level description of a cross-transaction can automatically generate smart contracts in Solidity for all the parties. 

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