Joint Multi-view Unsupervised Feature Selection and Graph Learning

Despite the recent progress, the existing multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize either cluster structure or similarity structure to guide the feature selection, neglecting the possibility of a joint formulation with mutual benefits. Second, they often learn the similarity structure by either global structure learning or local structure learning, lacking the capability of graph learning with both global and local structural awareness. In light of this, this paper presents a joint multi-view unsupervised feature selection and graph learning (JMVFG) approach. Particularly, we formulate the multi-view feature selection with orthogonal decomposition, where each target matrix is decomposed into a view-specific basis matrix and a view-consistent cluster indicator. Cross-space locality preservation is incorporated to bridge the cluster structure learning in the projected space and the similarity learning (i.e., graph learning) in the original space. Further, a unified objective function is presented to enable the simultaneous learning of the cluster structure, the global and local similarity structures, and the multi-view consistency and inconsistency, upon which an alternating optimization algorithm is developed with theoretically proved convergence. Extensive experiments demonstrate the superiority of our approach for both multi-view feature selection and graph learning tasks

Effects of deformation on the electronic structure of a single-walled carbon nanotube bundle

We have studied the effects of uniaxial pressure on the geometric structure and the electronic structure of single-walled carbon nanotube bundles theoretically. The local-density approximation in the density-functional theory has been applied to three types of carbon nanotube bundles, made up of the (8, 0), (10, 0), and (11, 0) tubes under uniaxial pressure perpendicular to the tubule axis. In all these types of bundles, an abrupt change is observed in the deformation of the tubes and their configuration at a certain pressure. It is also found that, despite a similar change of the lattice constants of the bundle, the deformation and the configuration of the tubes depend strongly on their types: While the (8, 0) tube bundle has a dense structure at high pressures, larger tube bundles prefer a loose one. All types of bundles, which are calculated to be semiconducting, exhibit a semiconductor-metal transition before or at the beginning of the abrupt change of the lattice constants when they are deformed by the uniaxial pressure. The pressure effect on the energy gap, however, is not monotonous: a decrease and an upturn followed by its disappearance. By analyzing the atomic arrangement, the band structure, and the wave functions of the three types of carbon nanotube bundles, the relationship between them is also established

EPSD: Early Pruning with Self-Distillation for Efficient Model Compression

Neural network compression techniques, such as knowledge distillation (KD) and network pruning, have received increasing attention. Recent work `Prune, then Distill' reveals that a pruned student-friendly teacher network can benefit the performance of KD. However, the conventional teacher-student pipeline, which entails cumbersome pre-training of the teacher and complicated compression steps, makes pruning with KD less efficient. In addition to compressing models, recent compression techniques also emphasize the aspect of efficiency. Early pruning demands significantly less computational cost in comparison to the conventional pruning methods as it does not require a large pre-trained model. Likewise, a special case of KD, known as self-distillation (SD), is more efficient since it requires no pre-training or student-teacher pair selection. This inspires us to collaborate early pruning with SD for efficient model compression. In this work, we propose the framework named Early Pruning with Self-Distillation (EPSD), which identifies and preserves distillable weights in early pruning for a given SD task. EPSD efficiently combines early pruning and self-distillation in a two-step process, maintaining the pruned network's trainability for compression. Instead of a simple combination of pruning and SD, EPSD enables the pruned network to favor SD by keeping more distillable weights before training to ensure better distillation of the pruned network. We demonstrated that EPSD improves the training of pruned networks, supported by visual and quantitative analyses. Our evaluation covered diverse benchmarks (CIFAR-10/100, Tiny-ImageNet, full ImageNet, CUB-200-2011, and Pascal VOC), with EPSD outperforming advanced pruning and SD techniques.Comment: The first two authors are with equal contributions. Paper accepted by AAAI 202

Synergy of Pd atoms and oxygen vacancies on In₂O₃ for methane conversion under visible light

Methane (CH4) oxidation to high value chemicals under mild conditions through photocatalysis is a sustainable and appealing pathway, nevertheless confronting the critical issues regarding both conversion and selectivity. Herein, under visible irradiation (420 nm), the synergy of palladium (Pd) atom cocatalyst and oxygen vacancies (OVs) on In2O3 nanorods enables superior photocatalytic CH4 activation by O2. The optimized catalyst reaches ca. 100 μmol h-1 of C1 oxygenates, with a selectivity of primary products (CH3OH and CH3OOH) up to 82.5%. Mechanism investigation elucidates that such superior photocatalysis is induced by the dedicated function of Pd single atoms and oxygen vacancies on boosting hole and electron transfer, respectively. O2 is proven to be the only oxygen source for CH3OH production, while H2O acts as the promoter for efficient CH4 activation through ·OH production and facilitates product desorption as indicated by DFT modeling. This work thus provides new understandings on simultaneous regulation of both activity and selectivity by the synergy of single atom cocatalysts and oxygen vacancies

Study of slime water mixing process intensification using impingement flow regulation

slime water generally contains a large number of highly dispersed suspended particles, making solid-liquid separation difficult. Strengthening fluid mixing and particle collision by regulating turbulence is an effective way to achieve solid-liquid separation. Particle collision flocculation mostly occurs in turbulent environments where the motion of fine particles is strongly influenced by the turbulent minimum vortex scale. In this study, turbulent vortices are modulated by impinging flows to enhance the mixing of two different density suspensions and the collision of fine particles in the suspension. Two different solution models were used to simulate the mixing condition of the suspension and the distribution of the particles in the mixing drum in three dimensions. The water phase entering the mixing drum was considered as a continuous phase and the solid particles were considered as a continuous phase (suspension) or a secondary discrete phase (particles). The effects of different inlet fluid velocity ratios at different feed densities on the turbulent characteristic parameters and particle distribution in the mixing drum were analyzed. The results of the study show that the impact flow formed by the jets colliding vertically with each other can induce turbulent macro-vortices such as hairpin vortices, spanwise vortices and axial vortices. The velocity of particles moving in the turbulent macro-vortex is in the following order: Large particle size and density > Large particle size and small density > Small particle size and high density > Small particle size and density. The interaction between vortex and vortex and between vortex and the main fluid significantly increases the turbulent kinetic energy and decreases the vortex scale, resulting in a minimum scale vortex that is conducive to particle coalescence and collision; the minimum vortex scale generated in the flow field in the mixing drum is mainly smaller than the average minimum vortex scale. The minimum vortex scale tends to increase when the inlet flow rate and flow rate ratio increase from 1.258:1.87 to 1.882:1.258, independent of the inlet density. When the flow rate ratio is similar, the minimum vortex scale decreases only when the flow rate increases. An appropriate increase in the ratio of the upper and side feed flow rates helps fluid mixing and particle aggregation and collision, and the mixing density, apparent viscosity and particle coalescence are all optimal when the ratio of the upper and side feed flow rates is between 1.40 and 1.50. In addition, at the same flow rate ratio, the mixing uniformity and mixing strength are better than the case where the upper inlet feed density is greater than the side inlet feed density, which is more conducive to fluid mixing and particle collision. The study promotes slime water mixing and fine particle coalescence in mixing drums through the regulation of fluid hydraulic conditions, providing a new way of thinking about how to enhance the liquid-liquid mixing and solid-liquid separation process

Foreign Body Inclusion Cyst of the Nasal Radix after Augmentation Rhinoplasty

Development of a cystic mass on the nasal dorsum is a very rare complication of aesthetic rhinoplasty. Most reported cases are of mucous cyst and entrapment of the nasal mucosa in the subcutaneous space due to traumatic surgical technique has been suggested as a presumptive pathogenesis. Here, we report a case of dorsal nasal cyst that had a different pathogenesis for cyst formation. A 58-yr-old woman developed a large cystic mass on the nasal radix 30 yr after augmentation rhinoplasty with silicone material. The mass was removed via a direct open approach and the pathology findings revealed a foreign body inclusion cyst associated with silicone. Successful nasal reconstruction was performed with autologous cartilages. Discussion and a brief review of the literature will be focused on the pathophysiology of and treatment options for a postrhinoplasty dorsal cyst