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2022 ◽  
Vol 40 (2) ◽  
pp. 1-24
Author(s):  
Ruihong Qiu ◽  
Zi Huang ◽  
Tong Chen ◽  
Hongzhi Yin

For present e-commerce platforms, it is important to accurately predict users’ preference for a timely next-item recommendation. To achieve this goal, session-based recommender systems are developed, which are based on a sequence of the most recent user-item interactions to avoid the influence raised from outdated historical records. Although a session can usually reflect a user’s current preference, a local shift of the user’s intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user’s initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness . Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.


2022 ◽  
Author(s):  
Han Yu ◽  
Yong Li ◽  
Junhao Zhang ◽  
Dongyu Yang ◽  
Tianhao Ruan ◽  
...  

Abstract Non-mechanical ptychographic encoding (NPE) transforms the secret information into a series of diffractive patterns through a spatial light modulator, saving the need to fabricate the secret objects. Conventionally, the shares in extended visual cryptography (EVC) are printed on transparent sheets or fabricated with diffractive optical elements and metasurface, but these methods are expensive and disposable. To solve these problems, we proposed an optical image encryption scheme that combines EVC and NPE. In the encryption process, the secret image is decomposed into multiple shares that are digitally loaded on the spatial light modulator, and the ciphertexts are generated according to the ptychographic encoding scheme. The decryption is performed by superimposing the shares reconstructed from the ciphertexts. We present optical experiments to demonstrate the feasibility and effectiveness of the proposed method.


Author(s):  
Samuel Yen-Chi Chen ◽  
Chih-Min Huang ◽  
Chia-Wei Hsing ◽  
Hsi-Sheng Goan ◽  
Ying-Jer Kao

Abstract Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here we present two frameworks of deep quantum RL tasks using a gradient-free evolution optimization: First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using the amplitude encoding; Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL on the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7936
Author(s):  
Tom Lawrence ◽  
Li Zhang ◽  
Kay Rogage ◽  
Chee Peng Lim

Automated deep neural architecture generation has gained increasing attention. However, exiting studies either optimize important design choices, without taking advantage of modern strategies such as residual/dense connections, or they optimize residual/dense networks but reduce search space by eliminating fine-grained network setting choices. To address the aforementioned weaknesses, we propose a novel particle swarm optimization (PSO)-based deep architecture generation algorithm, to devise deep networks with residual connections, whilst performing a thorough search which optimizes important design choices. A PSO variant is proposed which incorporates a new encoding scheme and a new search mechanism guided by non-uniformly randomly selected neighboring and global promising solutions for the search of optimal architectures. Specifically, the proposed encoding scheme is able to describe convolutional neural network architecture configurations with residual connections. Evaluated using benchmark datasets, the proposed model outperforms existing state-of-the-art methods for architecture generation. Owing to the guidance of diverse non-uniformly selected neighboring promising solutions in combination with the swarm leader at fine-grained and global levels, the proposed model produces a rich assortment of residual architectures with great diversity. Our devised networks show better capabilities in tackling vanishing gradients with up to 4.34% improvement of mean accuracy in comparison with those of existing studies.


2021 ◽  
Author(s):  
Yujie Wang ◽  
Weibing Kuang ◽  
Mingtao Shang ◽  
Zhen-Li Huang

AbstractMulti-color super-resolution localization microscopy (SRLM) provides great opportunities for studying the structural and functional details of biological samples. However, current multi-color SRLM methods either suffer from medium to high crosstalk, or require a dedicated optical system and a complicated image analysis procedure. To address these problems, here we propose a completely different method to realize multi-color SRLM. This method is built upon a customized RGBW camera with a repeated pattern of filtered (Red, Green, Blue and Near-infrared) and unfiltered (White) pixels. With a new insight that RGBW camera is advantageous for color recognition instead of color reproduction, we developed a joint encoding scheme of emitter location and color. By combing this RGBW camera with the joint encoding scheme and a simple optical set-up, we demonstrated two-color SRLM with ∼20 nm resolution and < 2% crosstalk (which is comparable to the best reported values). This study significantly reduces the complexity of two-color SRLM (and potentially multi-color SRLM), and thus offers good opportunities for general biomedical research laboratories to use multi-color SRLM, which is currently mastered only by well-trained researchers.


2021 ◽  
Author(s):  
Suzhen Wu ◽  
Jiapeng Wu ◽  
Zhirong Shen ◽  
Zhihao Zhang ◽  
Zuocheng Wang ◽  
...  

2021 ◽  
Vol 10 (5) ◽  
pp. 2607-2616
Author(s):  
Abdel Rahman Idrais ◽  
Inad Aljarrah ◽  
Osama Al-Khaleel

Image compression is vital for many areas such as communication and storage of data that is rapidly growing nowadays. In this paper, a spatial lossy compression algorithm for gray scale images is presented. It exploits the inter-pixel and the psycho-visual data redundancies in images. The proposed technique finds paths of connected pixels that fluctuate in value within some small threshold. The path is calculated by looking at the 4-neighbors of a pixel then choosing the best one based on two conditions; the first is that the selected pixel must not be included in another path and the second is that the difference between the first pixel in the path and the selected pixel is within the specified threshold value. A path starts with a given pixel and consists of the locations of the subsequently selected pixels. Run-length encoding scheme is applied on paths to harvest the inter-pixel redundancy. After applying the proposed algorithm on several test images, a promising quality vs. compression ratio results have been achieved.


Author(s):  
Liangyou Liu ◽  
Zhaotong Li ◽  
Zeru Zhang ◽  
Yifan Xia ◽  
Sha Li ◽  
...  

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