ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.

Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs.

A Novel Unsupervised Feature Selection for High-Dimensional Data Based on FCM and k -Nearest Neighbor Rough Sets.

Large amounts of high-dimensional unlabeled data typically contain only a small portion of truly effective information. Consequently, the issue of unsupervised feature selection methods has gained significant attention in research. However, current unsupervised feature selection approaches face limitations when dealing with datasets that exhibit uneven density, and they also require substantial computational time.

Fourier feature decorrelation based sample attention for dense crowd localization.

Crowd localization, which prevails to extract the independent individual features, plays an significant role in critical analysis for crowd scene. Dense trivial features of individual targets are frequently susceptible to interference from complex background features, which makes it difficult to obtain satisfactory predictions for individual targets. Aiming at this issue, a Fourier feature decorrelation based sample attention is proposed for dense crowd localization.

Incremental Learning for Simultaneous Augmentation of Feature and Class.

With the emergence of new data collection ways in many dynamic environment applications, the samples are gathered gradually in the accumulated feature spaces. With the incorporation of new type features, it may result in the augmentation of class numbers. For instance, in activity recognition, using the old features during warm-up, we can separate different warm-up exercises.

Learning With Incremental Instances and Features.

In many real-world applications, data may dynamically expand over time in both volume and feature dimensions. Besides, they are often collected in batches (also called blocks). We refer this kind of data whose volume and features increase in blocks as blocky trapezoidal data streams.

RSS-Bagging: Improving Generalization Through the Fisher Information of Training Data.

The bagging method has received much application and attention in recent years due to its good performance and simple framework. It has facilitated the advanced random forest method and accuracy-diversity ensemble theory. Bagging is an ensemble method based on simple random sampling (SRS) method with replacement.

Graph-Based Unsupervised Feature Selection for Interval-Valued Information System.

Feature selection has become one of the hot research topics in the era of big data. At the same time, as an extension of single-valued data, interval-valued data with its inherent uncertainty tend to be more applicable than single-valued data in some fields for characterizing inaccurate and ambiguous information, such as medical test results and qualified product indicators. However, there are relatively few studies on unsupervised attribute reduction for interval-valued information systems (IVISs), and it remains to be studied how to effectively control the dramatic increase of time cost in feature selection of large sample datasets.

Two-Way Concept-Cognitive Learning via Concept Movement Viewpoint.

Representation and learning of concepts are critical problems in data science and cognitive science. However, the existing research about concept learning has one prevalent disadvantage: incomplete and complex cognitive. Meanwhile, as a practical mathematical tool for concept representation and concept learning, two-way learning (2WL) also has some issues leading to the stagnation of its related research: the concept can only learn from specific information granules and lacks a concept evolution mechanism.

Generalization Performance of Pure Accuracy and its Application in Selective Ensemble Learning.

The pure accuracy measure is used to eliminate random consistency from the accuracy measure. Biases to both majority and minority classes in the pure accuracy are lower than that in the accuracy measure. In this paper, we demonstrate that compared with the accuracy measure and F-measure, the pure accuracy measure is class distribution insensitive and discriminative for good classifiers.

AF: An Association-Based Fusion Method for Multi-Modal Classification.

Multi-modal classification (MMC) aims to integrate the complementary information from different modalities to improve classification performance. Existing MMC methods can be grouped into two categories: traditional methods and deep learning-based methods. The traditional methods often implement fusion in a low-level original space.

Locality-Constrained Discriminative Matrix Regression for Robust Face Identification.

Regression-based methods have been widely applied in face identification, which attempts to approximately represent a query sample as a linear combination of all training samples. Recently, a matrix regression model based on nuclear norm has been proposed and shown strong robustness to structural noises. However, it may ignore two important issues: the label information and local relationship of data.

Enhanced Group Sparse Regularized Nonconvex Regression for Face Recognition.

Regression analysis based methods have shown strong robustness and achieved great success in face recognition. In these methods, convex l-norm and nuclear norm are usually utilized to approximate the l-norm and rank function. However, such convex relaxations may introduce a bias and lead to a suboptimal solution.

Target control based on edge dynamics in complex networks.

In the past decade, the study of the dynamics of complex networks has been a focus of research. In particular, the controllability of complex networks based on the nodal dynamics has received strong attention. As a result, significant theories have been formulated in network control.

An Algorithm for Clustering Categorical Data With Set-Valued Features.

In data mining, objects are often represented by a set of features, where each feature of an object has only one value. However, in reality, some features can take on multiple values, for instance, a person with several job titles, hobbies, and email addresses. These features can be referred to as set-valued features and are often treated with dummy features when using existing data mining algorithms to analyze data with set-valued features.

Investigation of the preconditioner-parameter in the preconditioned Chambolle-Pock algorithm applied to optimization-based image reconstruction.

The optimization-based image reconstruction methods have been thoroughly investigated in the field of medical imaging. The Chambolle-Pock (CP) algorithm may be employed to solve these convex optimization image reconstruction programs. The preconditioned CP (PCP) algorithm has been shown to have much higher convergence rate than the ordinary CP (OCP) algorithm.

A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.

Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information.

Three novel accurate pixel-driven projection methods for 2D CT and 3D EPR imaging.

Objectives: This work aims to explore more accurate pixel-driven projection methods for iterative image reconstructions in order to reduce high-frequency artifacts in the generated projection image.

Methods: Three new pixel-driven projection methods namely, small-pixel-large-detector (SPLD), linear interpolation based (LIB) and distance anterpolation based (DAB), were proposed and applied to reconstruct images. The performance of these methods was evaluated in both two-dimensional (2D) computed tomography (CT) images via the modified FORBILD phantom and three-dimensional (3D) electron paramagnetic resonance (EPR) images via the 6-spheres phantom.

Feature Selection Based on Neighborhood Discrimination Index.

Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation.

Quantifying edge significance on maintaining global connectivity.

Global connectivity is a quite important issue for networks. The failures of some key edges may lead to breakdown of the whole system. How to find them will provide a better understanding on system robustness.

Space Structure and Clustering of Categorical Data.

Learning from categorical data plays a fundamental role in such areas as pattern recognition, machine learning, data mining, and knowledge discovery. To effectively discover the group structure inherent in a set of categorical objects, many categorical clustering algorithms have been developed in the literature, among which k -modes-type algorithms are very representative because of their good performance. Nevertheless, there is still much room for improving their clustering performance in comparison with the clustering algorithms for the numeric data.

Implementation of GPU-accelerated back projection for EPR imaging.

Electron paramagnetic resonance (EPR) Imaging (EPRI) is a robust method for measuring in vivo oxygen concentration (pO2). For 3D pulse EPRI, a commonly used reconstruction algorithm is the filtered backprojection (FBP) algorithm, in which the backprojection process is computationally intensive and may be time consuming when implemented on a CPU. A multistage implementation of the backprojection can be used for acceleration, however it is not flexible (requires equal linear angle projection distribution) and may still be time consuming.

3D pulse EPR imaging from sparse-view projections via constrained, total variation minimization.

Tumors and tumor portions with low oxygen concentrations (pO2) have been shown to be resistant to radiation therapy. As such, radiation therapy efficacy may be enhanced if delivered radiation dose is tailored based on the spatial distribution of pO2 within the tumor. A technique for accurate imaging of tumor oxygenation is critically important to guide radiation treatment that accounts for the effects of local pO2.

Concept learning via granular computing: A cognitive viewpoint.

Concepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this paper we discuss concept learning via granular computing from the point of view of cognitive computing. More precisely, cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure.

The release rate of curcumin from calcium alginate beads regulated by food emulsifiers.

Curcumin-loaded alginate beads, which contain different food emulsifiers, have been prepared using CaCl₂ as the cross-linking agent. The controlled release of the curcumin from the beads was investigated at room temperature. For calcium alginate/Span-80/Tween-80 (A/S/T) formulations, almost all of the curcumin loaded in the beads was released into the medium within about 20 h, and the release rates could be regulated by changing the concentration of both Tween-80 and Span-80.