Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.Comment: IEEE BHI 2019 accepte

3D Textured Shape Recovery with Learned Geometric Priors

3D textured shape recovery from partial scans is crucial for many real-world applications. Existing approaches have demonstrated the efficacy of implicit function representation, but they suffer from partial inputs with severe occlusions and varying object types, which greatly hinders their application value in the real world. This technical report presents our approach to address these limitations by incorporating learned geometric priors. To this end, we generate a SMPL model from learned pose prediction and fuse it into the partial input to add prior knowledge of human bodies. We also propose a novel completeness-aware bounding box adaptation for handling different levels of scales and partialness of partial scans.Comment: 5 pages, 3 figures, 2 table

Manipulation of electronic property of epitaxial graphene on SiC substrate by Pb intercalation

Manipulating the electronic properties of graphene has been a subject of great interest since it can aid material design to extend the applications of graphene to many different areas. In this paper, we systematically investigate the effect of lead (Pb) intercalation on the structural and electronic properties of epitaxial graphene on the SiC(0001) substrate. We show that the band structure of Pb-intercalated few-layer graphene can be effectively tuned through changing intercalation conditions, such as coverage, location of Pb, and the initial number of graphene layers. Lead intercalation at the interface between the buffer layer (BL) and the SiC substrate decouples the BL from the substrate and transforms the BL into a p-doped graphene layer. We also show that Pb atoms tend to donate electrons to neighboring layers, leading to an n-doping graphene layer and a small gap in the Dirac cone under a sufficiently high Pb coverage. This paper provides useful guidance for manipulating the electronic properties of graphene layers on the SiC substrate

Discovering rare-earth-free magnetic materials through the development of a database

We develop an open-access database that provides a large array of datasets specialized for magnetic compounds as well as magnetic clusters. Our focus is on rare-earth-free magnets. Available datasets include (i) crystallography, (ii) thermodynamic properties, such as the formation energy, and (iii) magnetic properties that are essential for magnetic-material design. Our database features a large number of stable and metastable structures discovered through our adaptive genetic algorithm (AGA) searches. Many of these AGA structures have better magnetic properties when compared to those of the existing rare-earth-free magnets and the theoretical structures in other databases. Our database places particular emphasis on site-specific magnetic data, which are obtained by high-throughput first-principles calculations. Such site-resolved data are indispensable for machine-learning modeling. We illustrate how our data-intensive methods promote efficiency of the experimental discovery of new magnetic materials. Our database provides massive datasets that will facilitate an efficient computational screening, machine-learning-assisted design, and the experimental fabrication of new promising magnets

Structural, electronic, and optical properties of α-Te tubular nanostructures: A first-principles study

We employed density functional theory to investigate the structural, electronic, and optical properties of α-Te tubular nanostructures. These α-Te tube-like structures, which are similar to carbon nanotubes in terms of their armchair and zigzag structures, are semiconductors with moderate bandgaps. The nanotubes in armchair configurations have an indirect-to-direct bandgap transition as tube diameter is decreased to a specific critical tube size, while those in zigzag configurations are always semiconductors with a direct gap independent of tube diameter. The calculated projected density of states reveals that such an indirect-to-direct bandgap transition found in armchair nanotubes can be attributed to the contributions of the different p-orbitals near the valence band maximum edges. The optical absorption spectra of α-Te nanotubes are found to be anisotropic and vary with the tube diameters. These findings are not only helpful for better understanding the physical characteristics of α-Te nanotubes but also opening up new possibilities for use in device applications

Manipulation of electronic property of epitaxial graphene on SiC substrate by Pb intercalation

Manipulating the electronic properties of graphene has been a subject of great interest since it can aid material design to extend the applications of graphene to many different areas. In this paper, we systematically investigate the effect of lead (Pb) intercalation on the structural and electronic properties of epitaxial graphene on the SiC(0001) substrate. We show that the band structure of Pb-intercalated few-layer graphene can be effectively tuned through changing intercalation conditions, such as coverage, location of Pb, and the initial number of graphene layers. Lead intercalation at the interface between the buffer layer (BL) and the SiC substrate decouples the BL from the substrate and transforms the BL into a p-doped graphene layer. We also show that Pb atoms tend to donate electrons to neighboring layers, leading to an n-doping graphene layer and a small gap in the Dirac cone under a sufficiently high Pb coverage. This paper provides useful guidance for manipulating the electronic properties of graphene layers on the SiC substrate.</p

Effect of different Lactobacillus species on volatile and nonvolatile flavor compounds in juices fermentation

Lactobacillus is the dominant genus during fruit and vegetable juices (FVFs) fermentation, which are the key factors for taste and flavor. This study was performed to investigate the effects of different Lactobacillus spp. on profile of volatile flavor compounds and nonvolatile taste compounds in FVFs fermentation. A total of 14 compounds were identified as discriminant flavor and taste markers for fermented FVFs via gas chromatography–mass spectrometry (GC‐MS)‐based multimarker profiling. The PCA score plot and PLS‐DA showed that different FVFs were divided into three distinct types, suggesting that the different species significantly affect the volatile and nonvolatile compounds profiles of FVFs. Lactobacillus casei and Lactobacillus rhamnosus (Type A FVFs) might make a greater contribution to the umami taste. Lactobacillus plantarum and Lactobacillus acidophilus (Type B FVFs) make a greater contribution to the sour taste. Lactobacillus fermentum may be an potential critical contributor to produce volatile compounds. We reveal that different Lactobacillus strains play different roles in modifying these compounds related to flavor and taste features

Origin of short- and medium-range order in supercooled liquid Ge3Sb2Te6 using ab initio molecular dynamics simulations

Phase-change materials such as Ge–Sb–Te compounds have attracted much attention due to their potential value in electrical data storage. In contrast to the amorphous and crystalline phases, supercooled liquids are far from being deeply understood despite their inevitable role in both amorphization and crystallization processes. To this end, we have studied the dynamics properties and structural characteristics of liquid and supercooled liquid Ge3Sb2Te6 during the fast cooling process. As the temperature decreases, chemical bonds become more homogeneous, but coordination numbers of Ge, Sb and Te atoms change very little. Meanwhile, the structural order of short-range configuration is obviously enhanced. Further studies suggest that Ge-centered, Sb-centered and Te-centered configurations change to the more ordered defective octahedrons mainly by adjusting the bond-angle relationship and bond length, rather than just by changing the coordination environment. It is the more ordered octahedrons that promote the formation of medium-range order. Our findings provide a deep insight into the origin of local structural order in supercooled liquid Ge3Sb2Te6, which is of great importance for the comprehensive understanding of amorphization and crystallization processes.</p

Effect of different Lactobacillus

Lactobacillus is the dominant genus during fruit and vegetable juices (FVFs) fermentation, which are the key factors for taste and flavor. This study was performed to investigate the effects of different Lactobacillus spp. on profile of volatile flavor compounds and nonvolatile taste compounds in FVFs fermentation. A total of 14 compounds were identified as discriminant flavor and taste markers for fermented FVFs via gas chromatography–mass spectrometry (GC‐MS)‐based multimarker profiling. The PCA score plot and PLS‐DA showed that different FVFs were divided into three distinct types, suggesting that the different species significantly affect the volatile and nonvolatile compounds profiles of FVFs. Lactobacillus casei and Lactobacillus rhamnosus (Type A FVFs) might make a greater contribution to the umami taste. Lactobacillus plantarum and Lactobacillus acidophilus (Type B FVFs) make a greater contribution to the sour taste. Lactobacillus fermentum may be an potential critical contributor to produce volatile compounds. We reveal that different Lactobacillus strains play different roles in modifying these compounds related to flavor and taste features