Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools.

Hyperparameter optimization (HPO) has been well-developed and evolved into a well-established research topic over the decades. With the success and wide application of deep learning, HPO has garnered increased attention, particularly within the realm of machine learning model training and inference. The primary objective is to mitigate the challenges associated with manual hyperparameter tuning, which can be ad-hoc, reliant on human expertise, and consequently hinders reproducibility while inflating deployment costs.

Laser-upgraded coal tar for smart pavements in road and bridge monitoring applications.

The implementation of an intelligent road network system requires many sensors for acquiring data from roads, bridges, and vehicles, thereby enabling comprehensive monitoring and regulation of road networks. Given this large number of required sensors, the sensors must be cost-effective, dependable, and environmentally friendly. Here, we show a laser upgrading strategy for coal tar, a low-value byproduct of coal distillation, to manufacture flexible strain-gauge sensors with maximum gauge factors of 15.

A few-shot link prediction framework to drug repurposing using multi-level attention network.

Drug repurposing is a strategy aiming at uncovering novel medical indications of approved drugs. This process of discovery can be effectively represented as a link prediction task within a medical knowledge graph by predicting the missing relation between the disease entity and the drug entity. Typically, the links to be predicted pertain to rare types, thereby necessitating the task of few-shot link prediction.

Active Client Selection for Clustered Federated Learning.

Federated learning (FL) is an emerging distributed machine learning (ML) framework that operates under privacy and communication constraints. To mitigate the data heterogeneity underlying FL, clustered FL (CFL) was proposed to learn customized models for different client groups. However, due to the lack of effective client selection strategies, the CFL process is relatively slow, and the model performance is also limited in the presence of nonindependent and identically distributed (non-IID) client data.

How to prevent malicious use of intelligent unmanned swarms?

With advancements in swarm intelligence, artificial intelligence, and wireless mobile network technology, unmanned swarms such as unmanned aerial vehicles, ground vehicles, ships, and other unmanned systems are becoming increasingly autonomous and intelligent. Benefiting from these technologies, intelligent unmanned swarms are able to efficiently perform complex tasks through collaboration in various fields. However, malicious use of intelligent unmanned swarms raises concerns about the potential for significant damage to national infrastructures such as airports and power facilities.

Deep Reinforcement Learning: A Survey.

Deep reinforcement learning (DRL) integrates the feature representation ability of deep learning with the decision-making ability of reinforcement learning so that it can achieve powerful end-to-end learning control capabilities. In the past decade, DRL has made substantial advances in many tasks that require perceiving high-dimensional input and making optimal or near-optimal decisions. However, there are still many challenging problems in the theory and applications of DRL, especially in learning control tasks with limited samples, sparse rewards, and multiple agents.

Laser Direct-Write Sensors on Carbon-Fiber-Reinforced Poly-Ether-Ether-Ketone for Smart Orthopedic Implants.

Mechanically close-to-bone carbon-fiber-reinforced poly-ether-ether-ketone (CFR-PEEK)-based orthopedic implants are rising to compete with metal implants, due to their X-ray transparency, superior biocompatibility, and body-environment stability. While real-time strain assessment of implants is crucial for the postsurgery study of fracture union and failure of prostheses, integrating precise and durable sensors on orthopedic implants remains a great challenge. Herein, a laser direct-write technique is presented to pattern conductive features (minimum sheet resistance <1.

Graph-Attention-Based Casual Discovery With Trust Region-Navigated Clipping Policy Optimization.

In many domains of empirical sciences, discovering the causal structure within variables remains an indispensable task. Recently, to tackle unoriented edges or latent assumptions violation suffered by conventional methods, researchers formulated a reinforcement learning (RL) procedure for causal discovery and equipped a REINFORCE algorithm to search for the best rewarded directed acyclic graph. The two keys to the overall performance of the procedure are the robustness of RL methods and the efficient encoding of variables.

Mobile augmented reality based indoor map for improving geo-visualization.

Unlike traditional visualization methods, augmented reality (AR) inserts virtual objects and information directly into digital representations of the real world, which makes these objects and data more easily understood and interactive. The integration of AR and GIS is a promising way to display spatial information in context. However, most existing AR-GIS applications only provide local spatial information in a fixed location, which is exposed to a set of problems, limited legibility, information clutter and the incomplete spatial relationships.

Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation.

Beginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities' prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China's most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected.

Construction and confirmatory factor analysis of the core cognitive ability index system of ship C2 system operators.

Background: Cognitive ability refers to the ability to receive, process, store, and extract information. It is the most important psychological condition for people to successfully complete activities. Previous studies have shown that the design of the human-computer interface of the command and control system cannot exceed the cognitive ability of the operator of the command and control system, and it must match the cognitive ability of the operator in order to reduce the mental load intensity, and improve the accuracy, timeliness and work efficiency.

Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China.

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province.

On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem.

As a promising research direction in recent decades, active learning allows an oracle to assign labels to typical examples for performance improvement in learning systems. Existing works mainly focus on designing criteria for screening examples of high value to be labeled in a handcrafted manner. Instead of manually developing strategies of querying the user to access labels for the desired examples, we utilized the reinforcement learning algorithm parameterized with the neural network to automatically explore query strategies in active learning when addressing stream-based one-shot classification problems.

Identification of influential invaders in evolutionary populations.

The identification of the most influential nodes has been a vibrant subject of research across the whole of network science. Here we map this problem to structured evolutionary populations, where strategies and the interaction network are both subject to change over time based on social inheritance. We study cooperative communities, which cheaters can invade because they avoid the cost of contributions that are associated with cooperation.

A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine.

The identification of drug target proteins (IDTP) plays a critical role in biometrics. The aim of this study was to retrieve potential drug target proteins (DTPs) from a collected protein dataset, which represents an overwhelming task of great significance. Previously reported methodologies for this task generally employ protein-protein interactive networks but neglect informative biochemical attributes.

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM.

Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones.

Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model.

Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission.

Older partner selection promotes the prevalence of cooperation in evolutionary games.

Evolutionary games typically come with the interplays between evolution of individual strategy and adaptation to network structure. How these dynamics in the co-evolution promote (or obstruct) the cooperation is regarded as an important topic in social, economic, and biological fields. Combining spatial selection with partner choice, the focus of this paper is to identify which neighbour should be selected as a role to imitate during the process of co-evolution.

Heuristic search for planning with different forced goal-ordering constraints.

Planning with forced goal-ordering (FGO) constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. In certain planning domains, all the FGOs exist in the initial state. No matter which approach is adopted to achieve a subgoal, all the subgoals should be achieved in a given sequence from the initial state.