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Control Logic Routing for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2025年第4期62卷 950-962页

作者： Cai, Huayang Huang, Xing Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

With the advancement of electronic design automation, continuous-flow microfluidic biochips have become one of the most promising platforms for biochemical experiments. This chip manipulates fluid samples in milliliters or nanoliters by utilizing internal microvalves and microchannels, and thus automatically performs basic biochemical experiments, such as mixing and detection. To achieve the correct bioassay function, the microvalves deployed inside the chip are usually managed by a multiplexer-based control logic, and valves receive control signals from a core input through the control channel for accurate switching. Since biochemical reactions typically require high sensitivity, the length of control paths connecting each valve needs to be reduced to ensure immediate signal propagation, and thus to reduce the signal propagation delay. In addition, to reduce the fabrication cost of chips, a vital issue to be addressed in the logic architecture design is how to effectively reduce the total channel length within the control logic. To address the above issues, we propose a deep reinforcement learning-based control logic routing algorithm to minimize the signal propagation delay and total control channel length, thereby automatically constructing an efficient control channel network. The algorithm employs the dueling deep Q-network architecture as the agent of the deep reinforcement learning framework to evaluate the tradeoff between signal propagation delay and total channel length. Besides, the diagonal channel routing is implemented for the first time for control logic, thus fundamentally improving the efficiency of valve switching operations and reducing the fabrication cost of the chip. The experimental results demonstrate that the proposed algorithm can effectively construct a high-performance and low-cost control logic architecture. © 2025 Science Press. All rights reserved.

关键词： Deep reinforcement learning

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Robust DOA Estimation in RIS-Assisted MISO Systems

Robust DOA Estimation in RIS-Assisted MISO Systems

引用

International Symposium on Wireless Communication Systems (ISWCS)

作者： Canping Yu Wudang Xiao Yingsong Li Dingcheng Zou Nikola Zlatanov Rodrigo C. de Lamare Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia Centre for Telecommunications Research (CETUC) Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Gávea Brazil

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

Reconfigurable intelligent Surfaces (RIS) are recognized for their ability to actively control signal propagation, presenting a wide array of applications in 5G and the forthcoming 6G networks. In this paper, we design a RIS-assisted multi-input single-output (MISO) model and propose a corresponding direction of arrival (DOA) estimation algorithm. The proposed DOA estimation algorithm leverages the non-convex Ekblom norm to serve as the error criterion, ensuring robustness against impulsive noises, while the smoothed L1 norm is implemented as the regularization constraint to further enhance the performance of the algorithm. Distinct from prior research, the system under consideration operates with a single receive antenna, facilitating DOA estimation through the adjustment of RIS reflection coefficients, thereby markedly reducing system hardware costs. Moreover, we devise an alternating minimization strategy to obtain the optimal solution to the problem. A series of simulation results indicate that the proposed algorithm surpasses existing methods in terms of accuracy and robustness.

关键词： Wireless communication Direction-of-arrival estimation Accuracy Simulation Noise Estimation Reconfigurable intelligent surfaces

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Investigation on a quantum algorithm for linear differential equations

arXiv

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arXiv 2024年

作者： Dong, Xiaojing Peng, Yizhe Tang, Qili Yang, Yin Yu, Yue Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

Ref. [BCOW17] introduced a pioneering quantum approach (coined BCOW algorithm) for solving linear differential equations with optimal error tolerance. Originally designed for a specific class of diagonalizable linear differential equations, the algorithm was extended by Krovi in [Kro23] to encompass broader classes, including non-diagonalizable and even singular matrices. Despite the common misconception, the original algorithm is indeed applicable to non-diagonalizable matrices, with diagonalisation primarily serving for theoretical analyses to establish bounds on condition number and solution error. By leveraging basic estimates from [Kro23], we derive bounds comparable to those outlined in the Krovi algorithm, thereby reinstating the advantages of the BCOW approach. Furthermore, we extend the BCOW algorithm to address time-dependent linear differential equations by transforming non-autonomous systems into higher-dimensional autonomous ones, a technique also applicable for the Krovi algorithm. © 2024, CC BY-NC-ND.

关键词： Consensus algorithm

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A further study on the mass formula for linear codes with prescribed hull dimension

arXiv

引用

arXiv 2024年

作者： Li, Shitao Shi, Minjia Li, Yang Ling, San Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China School of Mathematics Hefei University of Technology Hefei230601 China School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore

Finding a mass formula for a given class of linear codes is a fundamental problem in combinatorics and coding theory. In this paper, we consider the action of the unitary (resp. symplectic) group on the set of all Hermitian (resp. symplectic) linear complementary dual (LCD) codes, prove that all Hermitian (resp. symplectic) LCD codes are on a unique orbit under this action, and determine the formula for the size of the orbit. Based on this, we develop a general technique to obtain a closed mass formula for linear codes with prescribed Hermitian (resp. symplectic) hull dimension, and further obtain some asymptotic results. Copyright © 2024, The Authors. All rights reserved.

关键词： Hulls (ship)

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Conditional Video-Text Reconstruction Network with Cauchy Mask for Weakly Supervised Temporal Sentence Grounding

Conditional Video-Text Reconstruction Network with Cauchy Ma...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jueqi Wei Yuanwu Xu Mohan Chen Yuejie Zhang Rui Feng Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Information Technology Deakin University

Temporal sentence grounding aims to detect the target segment most related to a given query in an untrimmed video. To alleviate the expensive annotation cost for temporal labels, researchers paid more attention to weakly supervised setting. Prior studies neglected the utilization of video representation reconstruction, which led to an unbalanced alignment learning. Moreover, they used different strategies to generate proposals which ignored the temporal structure in a query. In this paper, we propose a novel Conditional Video-Text Reconstruction Network (CVTRN). It supports conditional reconstruction of video and text representation. Specifically, video and text features are fused to compute semantic alignment, which is the condition of reconstruction. A new mask strategy for mask conditioned sentence reconstruction is also devised. This strategy focuses more on boundary regions than the widely used Gaussian mask in previous methods. Experimental results on two public benchmark datasets show that our CVTRN outperforms the state-of-the-art methods.

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SPTNET: Span-based Prompt Tuning for Video Grounding

SPTNET: Span-based Prompt Tuning for Video Grounding

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiren Zhang Yuanwu Xu Mohan Chen Yuejie Zhang Rui Feng Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Information Technology Deakin University

When a Pre-trained Language Model (PLM) is adopted in video grounding task, it usually acts as a text encoder without having its knowledge fully utilized. Also, there exists an inconsistency problem between the pre-training and downstream objectives. To solve the issues, we propose a new paradigm, named Span-based Prompt Tuning (SPTNet). It can convert the video grounding task into a cloze form. Specifically, a query is first changed into a form with mask token by a template, then the video and the query embeddings are integrated through a cross-modal transformer. The start and end points of the query matching time span are predicted with the embedding of the mask token. Experimental results on two public benchmarks ActivityNet Captions and Charades-STA show that our SPTNet achieves surpassing performance compared with state-of-the-art methods.

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Investigation of double-gyroid grain boundaries beyond twinning

arXiv

引用

arXiv 2024年

作者： Chen, Jing Sun, Zhangpeng Jiang, Kai Xu, Jie Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Chinese Academy of Sciences Beijing China

We study four double-gyroid (DG) grain boundaries (GBs) with different orientations numerically using the Landau–Brazovskii free energy, including the (422) twin boundary studied recently, a network switching GB, and two tilt GBs. Topological variations and geometric deformations are investigated. It is found that deviations in strut lengths and dihedral angles from the bulk DG substantially exceed changes in strut angles and nodal coplanarity. We also examine the spectra along the contact plane of two grains and utilize them to evaluate the GB widths. Of the four GBs we study, the network switching GB changes to the least extent topologically and geometrically, meanwhile has the lowest energy and the smallest GB width. Copyright © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

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DASpeech: directed acyclic transformer for fast and high-quality speech-to-speech translation 23

DASpeech: directed acyclic transformer for fast and high-qua...

引用

Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Qingkai Fang Yan Zhou Yang Feng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (ICT/CAS) and University of Chinese Academy of Sciences Beijing China

Direct speech-to-speech translation (S2ST) translates speech from one language into another using a single model. However, due to the presence of linguistic and acoustic diversity, the target speech follows a complex multimodal distribution, posing challenges to achieving both high-quality translations and fast decoding speeds for S2ST models. In this paper, we propose DASpeech, a non-autoregressive direct S2ST model which realizes both fast and high-quality S2ST. To better capture the complex distribution of the target speech, DASpeech adopts the two-pass architecture to decompose the generation process into two steps, where a linguistic decoder first generates the target text, and an acoustic decoder then generates the target speech based on the hidden states of the linguistic decoder. Specifically, we use the decoder of DA-Transformer as the linguistic decoder, and use FastSpeech 2 as the acoustic decoder. DA-Transformer models translations with a directed acyclic graph (DAG). To consider all potential paths in the DAG during training, we calculate the expected hidden states for each target token via dynamic programming, and feed them into the acoustic decoder to predict the target mel-spectrogram. During inference, we select the most probable path and take hidden states on that path as input to the acoustic decoder. Experiments on the CVSS Fr→En benchmark demonstrate that DASpeech can achieve comparable or even better performance than the state-of-the-art S2ST model Translatotron 2, while preserving up to 18.53× speedup compared to the autoregressive baseline. Compared with the previous non-autoregressive S2ST model, DASpeech does not rely on knowledge distillation and iterative decoding, achieving significant improvements in both translation quality and decoding speed. Furthermore, DASpeech shows the ability to preserve the speaker's voice of the source speech during translation. Audio samples are available at https://***/daspeech-demo/. Code is pu

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Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens

arXiv

引用

arXiv 2024年

作者： Sun, Dengdi Pan, Yajie Lu, Andong Li, Chenglong Luo, Bin The Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

Many RGBT tracking researches primarily focus on modal fusion design, while overlooking the effective handling of target appearance changes. While some approaches have introduced historical frames or fuse and replace initial templates to incorporate temporal information, they have the risk of disrupting the original target appearance and accumulating errors over time. To alleviate these limitations, we propose a novel Transformer RGBT tracking approach, which mixes spatio-temporal multimodal tokens from the static multimodal templates and multimodal search regions in Transformer to handle target appearance changes, for robust RGBT tracking. We introduce independent dynamic template tokens to interact with the search region, embedding temporal information to address appearance changes, while also retaining the involvement of the initial static template tokens in the joint feature extraction process to ensure the preservation of the original reliable target appearance information that prevent deviations from the target appearance caused by traditional temporal updates. We also use attention mechanisms to enhance the target features of multimodal template tokens by incorporating supplementary modal cues, and make the multimodal search region tokens interact with multimodal dynamic template tokens via attention mechanisms, which facilitates the conveyance of multimodal-enhanced target change information. Our module is inserted into the transformer backbone network and inherits joint feature extraction, search-template matching, and cross-modal interaction. Extensive experiments on three RGBT benchmark datasets show that the proposed approach maintains competitive performance compared to other state-of-the-art tracking algorithms while running at 39.1 FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Clustered Temporal Patch Transformer Network via Deep Local Feature Learning for Industrial Soft Sensor Applications

引用

IEEE Sensors Journal 2025年

作者： Xiao, Lin Wang, Pingping Fang, Yijing Hunan Normal University College of Information Science and Engineering Changsha410081 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

Accurate estimation of key quality indexes is critical for achieving optimal control in industrial processes. However, fluctuations in operating conditions, coupled with process lags, lead to multimodal data distributions and time-dependent correlations. These dynamics create fixed characteristic patterns within operating modes while propagating changes with delays, increasing the complexity of quality soft sensor modeling. To address these challenges, this paper proposes a novel Clustered Temporal Patch Transformer (CTPT-Former) for soft sensor modeling in industrial processes. CTPT-Former perceives coarse-grained process variability by identifying distinct operational modes in multimodal data. Each modal data is subsequently partitioned into patch units to enhance local feature extraction and capture fine-grained temporal dynamics. Futhermore, a time-dependent self-attention mechanism (CTPT-SA) is developed to strengthen the ability of model to uncover local and temporal correlations within and across modes, alleviating the complexities introduced by operational dependencies and spatial continuity of materials. Experimental results on real industrial datasets validate the superiority of the proposed approach compared to existing state-of-the-art methods. © 2001-2012 IEEE.

关键词： Model predictive control

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