Efficient Loss Landscape Reshaping for Convolutional Neural Networks.

Theoretical and empirical evidence highlights a positive correlation between the flatness of loss landscapes around minima and generalization. However, most current approaches that seek to find flat minima either incur high computational costs or struggle to balance generalization, training stability, and convergence. This work proposes reshaping the loss landscape to induce the optimizer toward flat regions, an approach that has negligible computational costs and does not compromise training stability, convergence, or efficiency.

Efficient adaptive learning rate for convolutional neural network based on quadratic interpolation egret swarm optimization algorithm.

Convolutional neural network (CNN) has recently become popular for addressing multi-domain image classification. However, most existing methods frequently suffer from poor performance, especially in performance and convergence for various datasets. Herein, we have proposed an algorithm for multi-domain image classification by introducing a novel adaptive learning rate rule to the conventional CNN.

Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake.

Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the time-series sequence.

KAGN:knowledge-powered attention and graph convolutional networks for social media rumor detection.

Rumor posts have received substantial attention with the rapid development of online and social media platforms. The automatic detection of rumor from posts has emerged as a major concern for the general public, the government, and social media platforms. Most existing methods focus on the linguistic and semantic aspects of posts content, while ignoring knowledge entities and concepts hidden within the article which facilitate rumor detection.

Modeling and Optimization of Sensitivity and Creep for Multi-Component Sensing Materials.

Pressure sensors urgently need high-performance sensing materials in order to be developed further. Sensitivity and creep are regarded as two key indices for assessing a sensor's performance. For the design and optimization of sensing materials, an accurate estimation of the impact of several parameters on sensitivity and creep is essential.

Revealing Physiochemical Factors and Zooplankton Influencing Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning.

Toxic cyanobacterial blooms have become a severe global hazard to human and environmental health. Most studies have focused on the relationships between cyanobacterial composition and cyanotoxins production. Yet, little is known about the environmental conditions influencing the hazard of cyanotoxins.

A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data.

With the rapid development of services computing in the past decade, Quality-of-Service (QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting warming-up tests on a large set of candidate services for QoS evaluation is time consuming and expensive, making it vital to implement accurate QoS-estimators. Existing QoS-estimators barely consider the temporal patterns hidden in QoS data.

A Pseudoinversion-Free Method for Weight Updating in Broad Learning System.

Neural networks have evolved into one of the most critical tools in the field of artificial intelligence. As a kind of shallow feedforward neural network, the broad learning system (BLS) uses a training process based on random and pseudoinverse methods, and it does not need to go through a complete training cycle to obtain new parameters when adding nodes. Instead, it performs rapid update iterations on the basis of existing parameters through a series of dynamic update algorithms, which enables BLS to combine high efficiency and accuracy flexibly.

Dynamical Conventional Neural Network Channel Pruning by Genetic Wavelet Channel Search for Image Classification.

Neural network pruning is critical to alleviating the high computational cost of deep neural networks on resource-limited devices. Conventional network pruning methods compress the network based on the hand-crafted rules with a pre-defined pruning ratio (PR), which fails to consider the variety of channels among different layers, thus, resulting in a sub-optimal pruned model. To alleviate this issue, this study proposes a genetic wavelet channel search (GWCS) based pruning framework, where the pruning process is modeled as a multi-stage genetic optimization procedure.

Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators.

In recent years, bicriteria optimization schemes for manipulator control have become preferred by researchers, given their satisfactory performance. In this article, a bicriteria weighted (BCW) scheme to remedy joint drift and minimize the infinity norm of joint velocity is proposed. The scheme adopts a novel repetitive motion index that can theoretically decouple the joint error and the position error, which many conventional cyclic motion generation schemes cannot achieve.

Activated Gradients for Deep Neural Networks.

Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point problem. In this article, a novel method by acting the gradient activation function (GAF) on the gradient is proposed to handle these challenges. Intuitively, the GAF enlarges the tiny gradients and restricts the large gradient.

The Stock Market Model with Delayed Information Impact from a Socioeconomic View.

Finding the critical factor and possible "Newton's laws" in financial markets has been an important issue. However, with the development of information and communication technologies, financial models are becoming more realistic but complex, contradicting the objective law "Greatest truths are the simplest." Therefore, this paper presents an evolutionary model independent of micro features and attempts to discover the most critical factor.

An L-and-L-Norm-Oriented Latent Factor Model for Recommender Systems.

A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L norm-oriented one, which ignores target data's characteristics described by other metrics like an L norm-oriented one.

An α-β-Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences.

To quantify user-item preferences, a recommender system (RS) commonly adopts a high-dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative latent factor analysis model relying on a single latent factor (LF)-dependent, non-negative, and multiplicative update algorithm. However, existing models' representative abilities are limited due to their specialized learning objective.

[Clinical path of mechanical ventilation and atomization inhalation therapy].

In recent years, atomization inhalation therapy has been more and more commonly used in patients with mechanical ventilation. However, the establishment of artificial airway has changed the environment and mode of aerosol delivery. Currently, Expert consensus on atomization inhalation during mechanical ventilation has been established to guide clinical practice.

[The value of growth differentiation factor-15 and extravascular lung water index in severity grading of acute respiratory distress syndrome patients and their prognosis prediction].

Objective: To investigate the value of growth differentiation factor-15 (GDF-15) and extravascular lung water index (EVLWI) in severity grading and prognosis prediction of patients with acute respiratory distress syndrome (ARDS).

Methods: Patients with ARDS aged 18-75 years admitted to the department of respiratory intensive care unit (RICU) of Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2019 to February 2020 were enrolled. All patients were treated with conventional therapies such as mechanical ventilation, anti-infection, stabilization of water, electrolytes and acid-base environment, blood purification and nutritional support according to their conditions.

Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management.

The frequency of toxin-producing cyanobacterial blooms has increased in recent decades due to nutrient enrichment and climate change. Because Microcystis blooms are related to different environmental conditions, identifying potential nutrient control targets can facilitate water quality managers to reduce the likelihood of microcystins (MCs) risk. However, complex biotic interactions and field data limitations have constrained our understanding of the nutrient-microcystin relationship.

Remote examination of the seasonal succession of phytoplankton assemblages from time-varying trends.

The seasonal succession of phytoplankton assemblages is important to ascertain the dynamics of an aquatic ecosystem structure, whereas its occurrence in response to hydrodynamic alterations is not clearly understood. In view of the characteristics of annual water level variation formed by the Three Gorges Dam Project (TGDP), our understanding about how these changes affect phytoplankton structure and dynamics is still very limited due to the shortage of long-term observation data. In this study, we used Huan Jing 1 charge-coupled device images over the past decade to examine the phytoplankton succession dates between cyanobacterial and green algal blooms in the backwater area of the Three Gorges Reservoir (TGR).

Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China.

Microcystis spp., which occur as colonies of different sizes under natural conditions, have expanded in temperate and tropical freshwater ecosystems and caused seriously environmental and ecological problems. In the current study, a Bayesian network (BN) framework was developed to access the probability of microcystins (MCs) risk in large shallow eutrophic lakes in China, namely, Taihu Lake, Chaohu Lake, and Dianchi Lake.

Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications.

High-dimensional and sparse (HiDS) matrices are commonly seen in big-data-related industrial applications like recommender systems. Latent factor (LF) models have proven to be accurate and efficient in extracting hidden knowledge from them. However, they mostly fail to fulfill the non-negativity constraints that describe the non-negative nature of many industrial data.

Popularity and Novelty Dynamics in Evolving Networks.

Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task.

Distinguishing two phenotypes of blooms using the normalised difference peak-valley index (NDPI) and Cyano-Chlorophyta index (CCI).

Harmful algal blooms are now widely recognised as a severe threat to freshwater ecosystems, particularly in semi-fluvial environments created by river damming. Given the high spatial and temporal variability of cyanobacterial blooms, remote sensing is more suitable than conventional field surveys in monitoring blooms. However, the majority of existing algorithms cannot distinguish cyanobacterial blooms from eukaryotic algal blooms by extracting spectral features in the remote-sensing reflectance (R).

Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China.

Harmful cyanobacterial blooms are exemplified as a major environmental concern due to producing toxin, and have generated a serious threat to public health. Knowledge on the spatial-temporal distribution of cyanobacterial blooms is therefore crucial for public health organizations and environmental agencies. In this study, field data and charge coupled device (CCD) image were collected in Lakes Gaoyang and Hanfeng of the Three Gorges Reservoir (TGR), China.

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering.

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs.