Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model

Development of Novel Cardiac Indices and Assessment of Factors Affecting Cardiac Activity in a Bivalve Mollusc Chlamys farreri

Cardiac activity has been widely used in marine molluscs as an indicator for their physiological status in response to environmental changes, which is, however, largely less studied in scallops. Here, we monitored cardiac performance of Zhikong scallop Chlamys farreri using an infrared-based method, and evaluated the effects of several biotic (shell height, total weight, and age) and environmental factors (circadian rhythm and temperature) on scallop heart rate (HR), amplitude (HA), and rate-amplitude product (RAP). Results revealed that size has a significant effect on both HR (negative) and HA (positive), but RAP values are similar in different sized scallops. Age also affects scallop cardiac performance, significantly for HR, but not for HA or RAP. Circadian rhythm affects cardiac activity, with significant elevation of HR, HA and RAP during 1:00–8:00 and 17:00–19:00. With seawater temperature elevation, HR peaks at 30.03 ± 0.23°C, HA at 15.08 ± 0.02°C, and RAP at 15.10 ± 0.19 and 30.12 ± 0.28°C. This suggests HR is a good indicator for thermal limit, whereas HA may indicate optimal growth temperature, and RAP could be an index of myocardial oxygen consumption to indicate myocardium stress. Our study provides basic information on the factors that may affect scallop cardiac performance. It also elucidates the feasibility of HA and RAP as cardiac indices in marine molluscs

Dynamic Anti-Counterfeiting Labels with Enhanced Multi-Level Information Encryption

Information encryption is an important means to improve the security of anti-counterfeiting labels. At present, it is still challenging to realize an anti-counterfeiting label with multi-function, high security factor, low production cost, and easy detection and identification. Herein, using inkjet and screen printing technology, we construct a multi-dimensional and multi-level dynamic optical anti-counterfeiting label based on instantaneously luminescent quantum dots and long afterglow phosphor, whose color and luminous intensity varied in response to time. Self-assembled quantum dot patterns with intrinsic fingerprint information endow the label with physical unclonable functions (PUFs), and the information encryption level of the label is significantly improved in view of the information variation in the temporal dimension. Furthermore, the convolutional residual neural networks are used to decode the massive information of PUFs, enabling fast and accurate identification of the anti-counterfeit labels