Micro-video label prediction plays a pivotal role on contemporary video-sharing platforms, such as Kwai and Tiktok. The emergence of video content lacking labels presents a formidable challenge for conventional user interest prediction methods. This paper addresses the challenge of micro-video label prediction, particularly for unseen videos, by proposing a zero-shot method called Class Semantic Relation Learning (CSRL). Unlike traditional user interest prediction models, CSRL leverages the pre-trained Large Language Model (LLM) to enhance prediction accuracy for unlabeled videos. The novelty of CSRL lies in its integration of three key components: a raw feature autoencoder, LLM-enhanced features, and a decomposed graph network. The decomposed graph network is specifically designed to disentangle the relationships between labeled and unlabeled videos, offering a significant improvement over previous methods. By fusing hidden topics with LLM-enhanced text, CSRL effectively handles sparse video features. Experiments on large-scale datasets from the Kwai platform show that CSRL achieves state-of-the-art results, with up to 44.64% improvement in Hit Ratio (HR), highlighting its superiority over existing zero-shot recommendation models in predicting user interests within the user-video network.

Biological age is a more direct reflection of physiological status than chronological age, serving as a vital measure to evaluate health risks and aging interventions. While steroid metabolomics offers rich information for exploring aging mechanisms, the complex and nonlinear interactions within metabolic networks remain challenging in modeling. Here, we propose and describe METRON as a deep learning framework to predict biological ages from steroid metabolomics. Specifically, a Metabolite Interaction Perception Module (MIPM) is proposed to capture the interactions. Subsequently, a Group-Rational Kolmogorov-Arnold Network is also integrated to capture intricate dependencies and enhance the representation capability. We demonstrate that METRON achieves promising performance as compared to other machine learning and deep learning methods. Beyond performance, METRON offers interpretability by recovering the established markers such as Dehydroepiandrosterone (DHEA) and identifying 17-hydroxyprogesterone (17-OH-P4) as the key signature linked to hypothalamic-pituitary-adrenal axis dynamics. These results support the capacity of METRON not only to estimate biological age but also to uncover underappreciated metabolic drivers behind aging.

Large Language Models (LLMs) demonstrate promising capabilities in natural language understanding and reasoning with enormous parameter spaces and vast amounts of training data. These attributes have facilitated their deployment into diverse application domains. However, the underlying parameters implicitly assume decision-making boundaries, resulting in a significant number of decision spaces not covered by training data. This makes them susceptible to adversarial manipulations through carefully crafted inputs. To illuminate the vulnerabilities of LLMs, we propose a unified token-level jailbreaking attack that makes victim models generate responses for potentially harmful queries. Specifically, we propose an evolutionary algorithm to evolve perturbation sets, utilizing gradient-based and crossover-based operators to enhance performance under multiple scenarios. Furthermore, we develop a repository for reusing past perturbations and conduct an analysis of token sensitivity within LLMs, facilitating zero-shot attacks with convergence. Extensive benchmark experiments validate the effectiveness of our method on three different models, achieving increases of 62.36%, 57.89%, and 64.81% in attack success rates compared to baseline methods under white-box scenarios. In addition, the evaluation experiments demonstrate our method is effective for multiple scenarios and different size of models. This research reveals vulnerabilities of LLMs, provides theoretical foundations for developing more robust defense strategies, and contributes to building more reliable AI systems.

Few-shot semantic segmentation has attracted growing interest for its ability to generalize to novel object categories using only a few annotated samples. To address data scarcity, recent methods incorporate multiple foundation models to improve feature transferability and segmentation performance. However, they often rely on dual-branch architectures that combine pre-trained encoders to leverage complementary strengths, a design that limits flexibility and efficiency. This raises a fundamental question: "can we build a unified model that integrates knowledge from different foundation architectures?" Achieving this is, however, challenging due to the misalignment between class-agnostic segmentation capabilities and fine-grained discriminative representations. To this end, we present UINO-FSS (pronounced //), a novel framework built on the key observation that early-stage DINOv2 features exhibit distribution consistency with SAM's output embeddings. This consistency enables the integration of both models' knowledge into a single-encoder architecture via coarse-to-fine multimodal distillation. In particular, our segmenter consists of three core components: a bottleneck adapter for embedding alignment, a meta-visual prompt generator that leverages dense similarity volumes and semantic embeddings, and a mask decoder. Using hierarchical cross-model distillation, we effectively transfer SAM's knowledge into the segmenter, further enhanced by Mamba-based 4D correlation mining on support-query pairs. Extensive experiments show that UINO-FSS achieves new state-of-the-art results on COCO- $20^{i}$ under the 1-shot setting, with an mIoU of 64.5% (+2.2%), while also delivering competitive performance on PASCAL- $5^{i}$ .

Phage therapy (PT) has become a promising alternative for treating infections with the increase of antimicrobial resistance. PT utilizes phages to bind to specific receptors on bacterial surfaces via receptor-binding proteins (RBPs), enabling precise destruction of targeted hosts. In PT, a key issue is the phage host prediction (PHP), which tries to match therapeutic phages to pathogenic hosts. However, traditional PHP methods are often hindered by the time-consuming and expensive wet-lab experiments, while recent computational methods neglect the evolutionary diversity and local feature patterns of RBPs. In this article, we propose a novel deep neural network (called PHPRBP) for PHP based on phage RBPs. In PHPRBP, we first utilize pre-trained protein language models (i.e., ESM2 and ProtT5) to learn the multi-source embedding representations from these RBPs, revealing diverse and complementary features. Then, we employ an adaptive synthetic technique to augment minority class samples, addressing the data scarcity issue. Subsequently, we design a deep neural network architecture, which uses a convolutional neural network to capture local sequence features, and applies a squeeze-and-excitation attention mechanism to enhance the contribution of important features. Finally, a fully connected network is used for host prediction. Experimental results show that PHPRBP outperforms the state-of-the-arts in host prediction at both genus and species levels.

Training graph neural networks (GNNs) for graph representation has received increasing concerns due to its outstanding performance in the link prediction and node classification tasks, but it incurs much time and storage for tackling large-scale graphs. To alleviate this issue, graph condensation has been emerged to condense the large graph into a small but highly-informative graph, while achieving comparable performance of GNNs trained on the small graph and large graph. However, existing works mainly focus on the gradient or distribution matching under GNN training trajectories to condense simple link structures, while overlooking the structure matching for condensing signed graph that exists conflict links and structural balance among nodes. To bridge this gap, we propose a novel Structure Balance and Gradient Matching-Based Signed Graph Condensation (SGSGC) method for condensing signed graph with node attributes, conflict links and structural balance into informative smaller ones. Specifically, we first propose a structure-balanced matching to match the structural balance between the original and condensed signed graph, and then combine it with the gradient matching to condense signed graph for the link sign prediction task, while preserving both conflicting link structures and node attributes. Moreover, we use the feature smoothing and the graph sparsification technique to improve the robustness for the GNN training, respectively. Finally, a bi-level optimization technique is proposed to simultaneously find the optimal node attributes and conflict structure of the condensed graph. Experiments on six datasets demonstrate that SGSGC achieves excellent performance. On Epinions, 94% test accuracy of training on the original signed graph, while reducing their graph size by 99.95% - 99.99%, and there exist 2.24% – 6.26% accuracy improvements for link sign prediction compared to the state-of-the-arts.

Sequential recommendation (SR) aims to predict users’ next-item preferences by analyzing their historical interaction sequences. Most existing SR models primarily focus on user preferences for a single objective of item relevance; they often overlook users’ personalized preferences for diversified item attributes. Although recent studies have attempted to address this limitation, efficiently resolving the inherent accuracy-diversity trade-off remains an open challenge. Drawing inspirations from multiobjective optimization and evolutionary multitasking, this paper proposes a multiobjective clustering-guided multitasking framework (MCGM-SR) that reconciles the two competing objectives in sequential recommendation. To enable knowledge transfer across homologous preference patterns, a user-level multiobjective clustering mechanism is introduced to group users with similar behavioral trajectories into objective-oriented clusters. Within each cluster, a multiobjective multitasking algorithm facilitates synergistic optimization across tasks to improve convergence efficiency. Extensive experiments on two real-world public datasets demonstrate that MCGM-SR achieves significant improvements in both accuracy and diversity metrics, compared to state-of-the-art sequential models and their diversified baselines. Further ablation studies reveal that the proposed clustering method significantly enhances optimization performance by effectively facilitating knowledge transfer.

To efficiently tackle problems with multiple conflicting objectives, several Multi-Objective Reinforcement Learning (MORL) algorithms utilize a universal policy network that takes preference weights as input to represent optimal policies for all different preferences. However, it is quite challenging to train such a universal policy as it is easy to forget or fail to learn skills for some preferences. To alleviate this issue, we propose an efficient Population-Based MORL (PB-MORL) method that trains multiple agents with universal policy networks using a shared replay buffer. Each agent is biased towards optimizing specific objectives by applying differentiated weights to the rewards sampled from the buffer. Therefore, the policy of each agent only needs to handle the specific part of the preference space rather than the entire space, simplifying the training task. Meanwhile, the experiences in the common buffer facilitate the information sharing among individuals, which can significantly reduce the number of interaction steps for training multiple agents. Experiments on both continuous and discrete tasks demonstrate the superiority of PB-MORL over several state-of-the-art MORL methods.

Antimicrobial peptides (AMPs) have emerged as a promising substitution to antibiotics thanks to their boarder range of activities, less likelihood of drug resistance, and low toxicity. Traditional biochemical methods for AMP discovery are costly and inefficient. Deep generative models, including the long-short term memory model, variational autoencoder model, and generative adversarial model, have been widely introduced to expedite AMP discovery. However, these models tend to suffer from the lack of diversity in generating AMPs. The denoising diffusion probabilistic model serves as a good candidate for solving this issue. We proposed a three-stage Text-Guided Conditional Denoising Diffusion Probabilistic Model (TG-CDDPM) to generate novel and homologous AMPs. In the first two stages, contrastive learning and inferring models are crafted to create better conditions for guiding AMP generation, respectively. In the last stage, a pre-trained conditional denoising diffusion probabilistic model is leveraged to enrich the peptide knowledge and fine-tuned to learn feature representation in downstream. TG-CDDPM was compared to the state-of-the-art generative models for AMP generation, and it demonstrated competitive or better performance with the assistance of text description as supervised information. The membrane penetration capabilities of the identified candidate AMPs by TG-CDDPM were also validated through molecular weight dynamics experiments.

Machine learning-based (ML-based) scoring functions (SFs) for protein–ligand binding affinity prediction have exhibited remarkable performance in the field of structure-based drug discovery. However, little attention has been given to the interpretability of these SFs. In this study, we propose a software called BoostSF-SHAP for protein–ligand binding affinity prediction. Specifically, we employed gradient boosting decision trees (GBDTs) to construct the ML-based SF. Forty-one intermolecular interaction features were used as the input of this SF. Notably, the proposed software can provide local and global explanations for the SF by using the SHapley Additive exPlanations (SHAP) approach. This paper presents a description of the architecture, functionalities, and implementation details of the proposed software. An assessment and illustrative examples of how to use this software are also provided. BoostSF-SHAP is written in Python and available on GitHub under the Apache License.

Network Intrusion Detection System (NIDS) has become more important as a large number of diverse devices connect to the Internet of Things (IoT). Generally, training an effective NIDS requires a large amount of high-quality and centralized attack data. However, in real-world scenarios, it is difficult to centralize the distributed data for training NIDS in the IoT due to the privacy concerns and data format heterogeneity. To solve this problem, a novel NIDS combining federated learning and cost-sensitive learning is proposed, named FIDS-CL. Specifically, federated learning with dynamic weights aggregation tackles the problem of non-clusterable data, where multiple clients collaboratively enhance the overall performance while dynamically aggregating weights to maximize the retention of high-performing client models. Moreover, cost-sensitive learning is employed to alleviate the problem of class imbalance in NIDS by dynamically adjusting the gradient descent weights of different classes in the loss function, thereby emphasizing the importance of minority classes. Therefore, our method can effectively handle data imbalance while safeguarding data privacy of clients, which is more effective to detect network intrusions. The experiments conducted across various scenarios validate the superior detection capabilities and computational efficiency of FIDS-CL when compared to other state-of-the-art NIDSs.

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field.

Anticancer peptides (ACPs) have emerged as one of the most promising therapeutic agents for cancer treatment. They are bioactive peptides featuring broad-spectrum activity and low drug-resistance. The discovery of ACPs via traditional biochemical methods is laborious and costly. Accordingly, various computational methods have been developed to facilitate the discovery of ACPs. However, the data resources and knowledge of ACPs are still very scarce, and only a few of them are clinically verified, which limits the competence of computational methods. To address this issue, in this article, we propose an ACP prediction model based on multi-domain transfer learning, namely MDTL-ACP, to discriminate novel ACPs from plentiful inactive peptides. In particular, we collect abundant antimicrobial peptides (AMPs) from four well-studied peptide domains and extract their inherent features as the input of MDTL-ACP. The features learned from multiple source domains of AMPs are then transferred into the target prediction task of ACPs via artificial neural network-based shared-extractor and task-specific classifiers in MDTL-ACP. The knowledge captured in the transferred features enhances the prediction of ACPs in the target domain. Experimental results demonstrate that MDTL-ACP can outperform the traditional and state-of-the-art ACP prediction methods.

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets.

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

Recently, the application of transfer learning within dynamic multiobjective evolutionary algorithms (DMOEAs) has shown significant potential to solve dynamic multiobjective optimization problems (DMOPs). This approach utilizes the transfer learning technique which reuses information from previous environments to accelerate the search process in the new environment. However, the risk of negative transfer can lead to an erroneous search direction and inefficient use of computational resources. To address this issue, this paper proposes a novel multistage transfer learning DMOEA, named MSTL. The algorithm is designed to enhance the convergence and diversity of the population through the combination of the pre-transfer learning stage and the transfer learning and validation stage when environmental changes occur. In the pre-transfer learning stage, a suitable target domain, source domain, and validation classifier sample sets are generated guided by historical information. In the transfer learning and validation stage, the TrAdaboost technique to transfer knowledge from the target to the source domain, with the results verified by two validation classifiers to mitigate negative transfer. Experimental results demonstrate that our proposed algorithm outperforms four competing algorithms in terms of diversity and convergence on a widely recognized benchmark suite.

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases.

Path planning is the core of autonomous robot navigation, which helps the robot to find a collision-free path to the destination based on the environment information. Most current path planning methods only consider the path length, but the optimal path may deviate from the shortest when considering other environmental factors such as uneven terrain or regions with varying traversal costs. Similarly, in scenarios prioritizing energy efficiency, a sole focus on path length may lead to suboptimal solutions. In this paper, an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) with adaptive weight vector, external archive, and constrained update strategy namely the MOEA/D-EAWA is proposed. This algorithm not only considers the path length but also four additional objectives such as smoothness, traveling time, terrain (elevation), and speed limit (expected delay). In addition, MOEA/D-EAWA is better suited for such many-objective path planning problem which has an irregular, discrete, and sparse Pareto front. The simulation results from 90 map instances demonstrate that the proposed method outperforms the existing approaches.

Direction of arrival (DOA) estimation employing low-resolution analog-to-digital convertors (ADCs) has emerged as a challenging and intriguing problem, particularly with the rise in popularity of large-scale arrays. The substantial quantization distortion complicates the extraction of signal and noise subspaces from the quantized data. To address this issue, this paper introduces a novel approach that leverages the Transformer model to aid the subspace estimation. In this model, multiple snapshots are processed in parallel, enabling the capture of global correlations that span them. The learned subspace empowers us to construct the MUSIC spectrum and perform gridless DOA estimation using a neural network-based peak finder. Additionally, the acquired subspace encodes the vital information of model order, allowing us to determine the exact number of sources. These integrated components form a unified algorithmic framework referred to as TransMUSIC. Numerical results demonstrate the superiority of the TransMUSIC algorithm, even when dealing with one-bit quantized data. The results highlight the potential of Transformer-based techniques in DOA estimation.

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

Energy-efficiency is one of the most important design criteria for mobile systems, such as smartphones and tablets. But current mobile systems always over-provision resources to satisfy users. The root cause is that, we have no knowledge on how much of system performance/energy will exactly satisfy users. Psychophysics defines the quantified link between physical stimuli and human-perceived stimuli. So, we will leverage psychophysics to study the quantified correlation between computer architecture resources (i.e., physical stimuli) and user satisfaction (i.e., human-perceived stimuli). We then exploit such correlation to precisely apportion resources to operate tasks and accurately satisfy users. Benefiting from our precisely-defined user satisfaction criteria and well-designed algorithms, we can reduce energy consumption of computer architectures by up to 42.9% without harming user experience. To the best of our knowledge, we for the first time theoretically and accurately model such substantial correlation. Our work opens a new research domain for fundamentally improving mobiles’ energy-efficiency.

MOTIVATION: The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs. RESULTS: To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR-pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset. AVAILABILITY AND IMPLEMENTATION: The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.

Blood pressure (BP) is one of the most important indicators of health. BP that is too high or too low causes varying degrees of diseases, such as renal impairment, cerebrovascular incidents, and cardiovascular diseases. Since traditional cuff-based BP measurement techniques have the drawbacks of patient discomfort and the impossibility of continuous BP monitoring, noninvasive cuffless continuous BP measurement has become a popular topic. The common noninvasive approach uses machine-learning (ML) algorithms to estimate BP by using the features extracted from simultaneous photoplethysmogram (PPG) and electrocardiogram (ECG) signals, such as the pulse transit time and pulse wave velocity. This study investigates the BP estimation performance of the novel dendritic neural regression (DNR) method proposed by us. Unlike conventional neural networks, DNR utilizes the multiplication operator as the excitation function in each dendritic branch, inspired by biological neuron phenomena, and can effectively capture nonlinear relationships between distinct input features. In addition, AMSGrad is used as the optimization algorithm to further enhance the dendritic neural model's performance. The experimental results show that by being fed a combination of the raw features extracted from the ECG and PPG signals and the components of the BP mathematical models, DNR can increase the accuracy of systolic BP, diastolic BP, and mean arterial pressure estimation significantly, which are superior to the state-of-the-art ML techniques. According to the British Hypertension Society protocol, DNR achieves a grade of A for the long-term BP estimation. Considering its architectural simplicity and powerful performance, the proposed method can be regarded as a reliable tool for estimating long-term continuous BP in a noninvasive cuffless way.

The dendritic neural model (DNM) is computationally faster than other machine-learning techniques, because its architecture can be implemented by using logic circuits and its calculations can be performed entirely in binary form. To further improve the computational speed, a straightforward approach is to generate a more concise architecture for the DNM. Actually, the architecture search is a large-scale multiobjective optimization problem (LSMOP), where a large number of parameters need to be set with the aim of optimizing accuracy and structural complexity simultaneously. However, the issues of irregular Pareto front, objective discontinuity, and population degeneration strongly limit the performances of conventional multiobjective evolutionary algorithms (MOEAs) on the specific problem. Therefore, a novel competitive decomposition-based MOEA is proposed in this study, which decomposes the original problem into several constrained subproblems, with neighboring subproblems sharing overlapping regions in the objective space. The solutions in the overlapping regions participate in environmental selection for the neighboring subproblems and then propagate the selection pressure throughout the entire population. Experimental results demonstrate that the proposed algorithm can possess a more powerful optimization ability than the state-of-the-art MOEAs. Furthermore, both the DNM itself and its hardware implementation can achieve very competitive classification performances when trained by the proposed algorithm, compared with numerous widely used machine-learning approaches.

Molecular docking plays a vital role in modern drug discovery, by supporting predictions of the binding modes and affinities of ligands at the binding site of target proteins. Several docking programs have been developed for both commercial and academic applications. Typically, a docking program’s performance depends on the sampling algorithm used to generate the ligand’s potential conformations and the scoring function applied to evaluate and rank these conformations. Evolutionary algorithms are widely used as sampling algorithms in docking programs. However, both the linkage problem and the dimensionality degenerate the search ability of evolutionary algorithms in the docking process. Therefore, a newly designed docking program named AutoDock Koto was developed in this study, which adopts a novel gradient boosting differential evolution algorithm to effectively address these issues. Experimental results show that compared with commonly used docking programs, AutoDock Koto yields dramatic improvements in docking performance based on an extensive dataset of 285 protein–ligand complexes. In addition, due to its strong docking ability, AutoDock Koto was used to identify potential drugs for COVID-19 based on a virtual screening of all approved drugs in our experiments. Sixteen drugs are found to possess low binding energy to the main target protease of SARS-CoV-2 and, thus, have the potential to treat COVID-19 as antiviral drugs. The source code of AutoDock Koto can be downloaded for free fromhttps://github.com/codezhouj/Molecular_Docking.

As a recurrent spiking neural network, liquid state machine (LSM) has attracted more and more attention in neuromorphic computing due to its biological plausibility, computation power, and hardware implementation. However, the neural architecture of LSM, such as hidden neuron number, synaptic density, percentage connectivity, and connection state, has significant impact on its model performance. Manually defining a neural architecture will be ineffective and laborious in most cases. Therefore, based on a state-of-the-art differential evolution algorithm, an evolutionary neural architecture design methodology is proposed to automatically build suitable model topologies for LSM in this study, without any prior knowledge. The effectiveness of the proposed method has been validated on commonly-used image classification tasks.

An approximate logic neural model (ALNM) is a novel single-neuron model with plastic dendritic morphology. During the training process, the model can eliminate unnecessary synapses and useless branches of dendrites. It will produce a specific dendritic structure for a particular task. The simplified structure of ALNM can be substituted by a logic circuit classifier (LCC) without losing any essential information. The LCC merely consists of the comparator and logic NOT, AND, and OR gates. Thus, it can be easily implemented in hardware. However, the architecture of ALNM affects the learning capacity, generalization capability, computing time and approximation of LCC. Thus, a Pareto-based multiobjective differential evolution (MODE) algorithm is proposed to simultaneously optimize ALNM's topology and weights. MODE can generate a concise and accurate LCC for every specific task from ALNM. To verify the effectiveness of MODE, extensive experiments are performed on eight benchmark classification problems. The statistical results demonstrate that MODE is superior to conventional learning methods, such as the backpropagation algorithm and single-objective evolutionary algorithms. In addition, compared against several commonly used classifiers, both ALNM and LCC are capable of obtaining promising and competitive classification performances on the benchmark problems. Besides, the experimental results also verify that the LCC obtains the faster classification speed than the other classifiers.

Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron's output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task - for example, the linearly nonseparable problem, a real-world benchmark problem - Glass classification and the directional selectivity problem.