A parallel tabu search for the unconstrained binary quadratic programming problem

International audienceAlthough several sequential heuristics have been proposed for dealing with the Unconstrained Binary Quadratic Programming (UBQP), very little effort has been made for designing parallel algorithms for the UBQP. This paper propose a novel decentralized parallel search algorithm, called Parallel Elite Biased Tabu Search (PEBTS). It is based on D2TS, a state-of-the-art sequential UBQP metaheuristic. The key strategies in the PEBTS algorithm include: (i) a lazy distributed cooperation procedure to maintain diversity among different search processes and (ii) finely tuned bit-flip operators which can help the search escape local optima efficiently. Our experiments on the Tianhe-2 supercomputer with up to 24 computing cores show the accuracy of the efficiency of PEBTS compared with a straightforward parallel algorithm running multiple independent and non-cooperating D2TS processes

Using Parallel Strategies to Speed Up Pareto Local Search

International audiencePareto Local Search (PLS) is a basic building block in many state-of-the-art multiobjective combinatorial optimization algorithms. However, the basic PLS requires a long time to find high-quality solutions. In this paper, we propose and investigate several parallel strategies to speed up PLS. These strategies are based on a parallel multi-search framework. In our experiments, we investigate the performances of different parallel variants of PLS on the multiobjective unconstrained binary quadratic programming problem. Each PLS variant is a combination of the proposed parallel strategies. The experimental results show that the proposed approaches can significantly speed up PLS while maintaining about the same solution quality. In addition, we introduce a new way to visualize the search process of PLS on two-objective problems, which is helpful to understand the behaviors of PLS algorithms

Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere

Due to the lack of more efficient diagnostic tools for monkeypox, its spread remains unchecked, presenting a formidable challenge to global health. While the high efficacy of deep learning models for monkeypox diagnosis has been demonstrated in related studies, the overlook of inference speed, the parameter size and diagnosis performance for early-stage monkeypox renders the models inapplicable in real-world settings. To address these challenges, we proposed an ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possesses only 0.27M parameters and can process input images at 68 frames per second (FPS) on the CPU. To counteract the diagnostic performance limitation brought about by the small model capacity, it integrates the attention-based feature fusion module and the multiple auxiliary losses enhancement strategy for better detecting subtle image changes and optimizing weights. Using transfer learning and five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on the Mpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. By adopting data augmentation, our model's Accuracy rises to 98.40% and attains a Practicality Score (A new metric for measuring model practicality in real-time diagnosis application) of 0.80. We also developed an application system named Mpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2 features ultrafast responses, offline functionality, and easy deployment, enabling accurate and real-time diagnosis for both the public and individuals in various real-world settings, especially in populous settings during the outbreak. Our work could potentially mitigate future monkeypox outbreak and illuminate a fresh paradigm for developing real-time diagnostic tools in the healthcare field

A Dopa Decarboxylase Modulating the Immune Response of Scallop Chlamys farreri

BACKGROUND: Dopa decarboxylase (DDC) is a pyridoxal 5-phosphate (PLP)-dependent enzyme that catalyzes the decarboxylation of L-Dopa to dopamine, and involved in complex neuroendocrine-immune regulatory network. The function for DDC in the immunomodulation remains unclear in invertebrate. METHODOLOGY: The full-length cDNA encoding DDC (designated CfDDC) was cloned from mollusc scallop Chlamys farreri. It contained an open reading frame encoding a polypeptide of 560 amino acids. The CfDDC mRNA transcripts could be detected in all the tested tissues, including the immune tissues haemocytes and hepatopancreas. After scallops were treated with LPS stimulation, the mRNA expression level of CfDDC in haemocytes increased significantly (5.5-fold, P<0.05) at 3 h and reached the peak at 12 h (9.8-fold, P<0.05), and then recovered to the baseline level. The recombinant protein of CfDDC (rCfDDC) was expressed in Escherichia coli BL21 (DE3)-Transetta, and 1 mg rCfDDC could catalyze the production of 1.651±0.22 ng dopamine within 1 h in vitro. When the haemocytes were incubated with rCfDDC-coated agarose beads, the haemocyte encapsulation to the beads was increased significantly from 70% at 6 h to 93% at 24 h in vitro in comparison with that in the control (23% at 6 h to 25% at 24 h), and the increased haemocyte encapsulation was repressed by the addition of rCfDDC antibody (which is acquired via immunization 6-week old rats with rCfDDC). After the injection of DDC inhibitor methyldopa, the ROS level in haemocytes of scallops was decreased significantly to 0.41-fold (P<0.05) of blank group at 12 h and 0.47-fold (P<0.05) at 24 h, respectively. CONCLUSIONS: These results collectively suggested that CfDDC, as a homologue of DDC in scallop, modulated the immune responses such as haemocytes encapsulation as well as the ROS level through its catalytic activity, functioning as an indispensable immunomodulating enzyme in the neuroendocrine-immune regulatory network of mollusc

Agents: An Open-source Framework for Autonomous Language Agents

Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents.Comment: Code available at https://github.com/aiwaves-cn/agent