GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set.

Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common rough set theoretical models. Although the PRS can use equivalence classes to represent knowledge, it is unable to process continuous data. On the other hand, NRSs, which can process continuous data, rather lose the ability of using equivalence classes to represent knowledge.

Toward Feature-Preserving Vector Field Compression.

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.

Random Space Division Sampling for Label-Noisy Classification or Imbalanced Classification.

This article presents a simple sampling method, which is very easy to be implemented, for classification by introducing the idea of random space division, called "random space division sampling" (RSDS). It can extract the boundary points as the sampled result by efficiently distinguishing the label noise points, inner points, and boundary points. This makes it the first general sampling method for classification that not only can reduce the data size but also enhance the classification accuracy of a classifier, especially in the label-noisy classification.

mCRF and mRD: Two Classification Methods Based on a Novel Multiclass Label Noise Filtering Learning Framework.

Mitigating label noise is a crucial problem in classification. Noise filtering is an effective method of dealing with label noise which does not need to estimate the noise rate or rely on any loss function. However, most filtering methods focus mainly on binary classification, leaving the more difficult counterpart problem of multiclass classification relatively unexplored.

Ball k-Means: Fast Adaptive Clustering With No Bounds.

This paper presents a novel accelerated exact k-means called as "Ball k-means" by using the ball to describe each cluster, which focus on reducing the point-centroid distance computation. The "Ball k-means" can exactly find its neighbor clusters for each cluster, resulting distance computations only between a point and its neighbor clusters' centroids instead of all centroids. What's more, each cluster can be divided into "stable area" and "active area", and the latter one is further divided into some exact "annular area".

Double conductivity-improved porous Sn/SnP@carbon nanocomposite as high performance anode in Lithium-ion batteries.

Carbon encapsulated porous Sn/SnP (Sn/SnP@C) composite is conveniently prepared by one-step electrochemical dealloying of SnP alloy in mild conditions followed by growing one carbon layer. Controllable dealloying of the SnP alloy results in the formation of bicontinuous spongy SnP nanostructure with a part of residued metallic Sn atoms embedded in the porous skeleton. A uniform carbon layer is deposited on the nanoporous Sn/SnP to prevent the nanostructure's pulverizing and agglomerating during lithium ion insertion/extraction.

One-step mild fabrication of porous core-shelled Si@TiO nanocomposite as high performance anode for Li-ion batteries.

Nanoporous Si@TiO composites with the unique core-shell architecture are conveniently fabricated through one-step selective dealloying of SiTiAl ternary alloy under mild conditions. The as-prepared composites consist of bimodal Si network skeleton as the core and interconnected TiO nanosponge layer as the shell uniformly distribute on the Si surface to form the porous core-shelled structure. The nanoporous TiO as the outer protective layer not only reduce the violent volume change of electrode materials for stable cycling performance but also shorten the diffusion distance of Li for high rate capacities.

Tin sulphide nanoflowers anchored on three-dimensional porous graphene networks as high-performance anode for sodium-ion batteries.

The SnS nanoflowers anchored on three dimensional porous graphene were easily constructed with nickel foam (NF) as supported backbone through the dip-coating method followed by one-step controllable hydrothermal growth and mild reduction. The interconnected SnS nanoflowers with cross-linking nanosheets and rich pores assembled to form one layer of continuous network structure, which tightly adhered on the surface of graphene. The porous graphene supported by NF built a conductively integral highway that is preferable for the charge transfer kinetics, while the hierarchical pores from the SnS nanoflowers and NF are particularly beneficial for mitigating the volume expansion and promoting electrolyte penetration.