Ultrathin MgB2 films fabricated on Al2O3 substrate by hybrid physical-chemical vapor deposition with high Tc and Jc

Ultrathin MgB2 superconducting films with a thickness down to 7.5 nm are epitaxially grown on (0001) Al2O3 substrate by hybrid physical-chemical vapor deposition method. The films are phase-pure, oxidation-free and continuous. The 7.5 nm thin film shows a Tc(0) of 34 K, which is so far the highest Tc(0) reported in MgB2 with the same thickness. The critical current density of ultrathin MgB2 films below 10 nm is demonstrated for the first time as Jc ~ 10^6 A cm^{-2} for the above 7.5 nm sample at 16 K. Our results reveal the excellent superconducting properties of ultrathin MgB2 films with thicknesses between 7.5 and 40 nm on Al2O3 substrate.Comment: 7 pages, 4 figures, 2 table

Unveiling the Power of Mixup for Stronger Classifiers

Mixup-based data augmentations have achieved great success as regularizers for deep neural networks. However, existing methods rely on deliberately handcrafted mixup policies, which ignore or oversell the semantic matching between mixed samples and labels. Driven by their prior assumptions, early methods attempt to smooth decision boundaries by random linear interpolation while others focus on maximizing class-related information via offline saliency optimization. As a result, the issue of label mismatch has not been well addressed. Additionally, the optimization stability of mixup training is constantly troubled by the label mismatch. To address these challenges, we first reformulate mixup for supervised classification as two sub-tasks, mixup sample generation and classification, then propose Automatic Mixup (AutoMix), a revolutionary mixup framework. Specifically, a learnable lightweight Mix Block (MB) with a cross-attention mechanism is proposed to generate a mixed sample by modeling a fair relationship between the pair of samples under direct supervision of the corresponding mixed label. Moreover, the proposed Momentum Pipeline (MP) enhances training stability and accelerates convergence on top of making the Mix Block fully trained end-to-end. Extensive experiments on five popular classification benchmarks show that the proposed approach consistently outperforms leading methods by a large margin.Comment: The second version of AutoMix. 12 pages, 7 figure

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration

In recent years, AI-assisted drug design methods have been proposed to generate molecules given the pockets' structures of target proteins. Most of them are atom-level-based methods, which consider atoms as basic components and generate atom positions and types. In this way, however, it is hard to generate realistic fragments with complicated structures. To solve this, we propose D3FG, a functional-group-based diffusion model for pocket-specific molecule generation and elaboration. D3FG decomposes molecules into two categories of components: functional groups defined as rigid bodies and linkers as mass points. And the two kinds of components can together form complicated fragments that enhance ligand-protein interactions. To be specific, in the diffusion process, D3FG diffuses the data distribution of the positions, orientations, and types of the components into a prior distribution; In the generative process, the noise is gradually removed from the three variables by denoisers parameterized with designed equivariant graph neural networks. In the experiments, our method can generate molecules with more realistic 3D structures, competitive affinities toward the protein targets, and better drug properties. Besides, D3FG as a solution to a new task of molecule elaboration, could generate molecules with high affinities based on existing ligands and the hotspots of target proteins.Comment: 9 page

Effects of NCMS Coverage on Access to Care and Financial Protection in China

The introduction of the New Cooperative Medical Scheme in rural China is one of the largest health care reforms in the developing world since the millennium. The literature to date has mainly used the uneven rollout of NCMS across counties as a way of identifying its effects on access to care and financial protection. This study exploits the cross-county variation in NCMS generosity in 2006 and 2008 in Ningxia and Shandong province and adopts an instrumenting approach to estimate the effect of a continuous measure of coverage level. Our results confirm earlier findings of NCMS being effective in increasing access to care, but not increasing financial protection. In addition, we find that NCMS enrollees are sensitive to the incentives set in the NCMS design when choosing their provider, but also that providers seem to respond by increasing prices and/or providing more expensive care

Efficient Multi-order Gated Aggregation Network

Since the recent success of Vision Transformers (ViTs), explorations toward transformer-style architectures have triggered the resurgence of modern ConvNets. In this work, we explore the representation ability of DNNs through the lens of interaction complexities. We empirically show that interaction complexity is an overlooked but essential indicator for visual recognition. Accordingly, a new family of efficient ConvNets, named MogaNet, is presented to pursue informative context mining in pure ConvNet-based models, with preferable complexity-performance trade-offs. In MogaNet, interactions across multiple complexities are facilitated and contextualized by leveraging two specially designed aggregation blocks in both spatial and channel interaction spaces. Extensive studies are conducted on ImageNet classification, COCO object detection, and ADE20K semantic segmentation tasks. The results demonstrate that our MogaNet establishes new state-of-the-art over other popular methods in mainstream scenarios and all model scales. Typically, the lightweight MogaNet-T achieves 80.0\% top-1 accuracy with only 1.44G FLOPs using a refined training setup on ImageNet-1K, surpassing ParC-Net-S by 1.4\% accuracy but saving 59\% (2.04G) FLOPs.Comment: Preprint with 14 pages of main body and 5 pages of appendi

Designing an object-based preproduction tool for multiscreen TV viewing

Multiscreen TV viewing refers to a spectrum of media productions that can be watched using TV and companion screens such as smartphones and tablets. In the last several years, companies are creating companion applications to enrich the TV viewing experience

Train unit scheduling guided by historic capacity provisions and passenger count surveys

Train unit scheduling concerns the assignment of train unit vehicles to cover all the journeys in a fixed timetable. Coupling and decoupling activities are allowed in order to achieve optimal utilization while satisfying passenger demands. While the scheduling methods usually assume unique and well-defined train capacity requirements, in practice most UK train operators consider different levels of capacity provisions. Those capacity provisions are normally influenced by information such as passenger count surveys, historic provisions and absolute minimums required by the authorities. In this paper, we study the problem of train unit scheduling with bi-level capacity requirements and propose a new integer multicommodity flow model based on previous research. Computational experiments on real-world data show the effectiveness of our proposed methodology

Toward an intensive understanding of sewer sediment prokaryotic community assembly and function

Prokaryotic communities play important roles in sewer sediment ecosystems, but the community composition, functional potential, and assembly mechanisms of sewer sediment prokaryotic communities are still poorly understood. Here, we studied the sediment prokaryotic communities in different urban functional areas (multifunctional, commercial, and residential areas) through 16S rRNA gene amplicon sequencing. Our results suggested that the compositions of prokaryotic communities varied significantly among functional areas. Desulfomicrobium, Desulfovibrio, and Desulfobacter involved in the sulfur cycle and some hydrolytic fermentation bacteria were enriched in multifunctional area, while Methanospirillum and Methanoregulaceae, which were related to methane metabolism were significantly discriminant taxa in the commercial area. Physicochemical properties were closely related to overall community changes (p < 0.001), especially the nutrient levels of sediments (i.e., total nitrogen and total phosphorus) and sediment pH. Network analysis revealed that the prokaryotic community network of the residential area sediment was more complex than the other functional areas, suggesting higher stability of the prokaryotic community in the residential area. Stochastic processes dominated the construction of the prokaryotic community. These results expand our understanding of the characteristics of prokaryotic communities in sewer sediment, providing a new perspective for studying sewer sediment prokaryotic community structure

DNA-based Self-Assembly of Chiral Plasmonic Nanostructures with Tailored Optical Response

Surface plasmon resonances generated in metallic nanostructures can be utilized to tailor electromagnetic fields. The precise spatial arrangement of such structures can result in surprising optical properties that are not found in any naturally occurring material. Here, the designed activity emerges from collective effects of singular components equipped with limited individual functionality. Top-down fabrication of plasmonic materials with a predesigned optical response in the visible range by conventional lithographic methods has remained challenging due to their limited resolution, the complexity of scaling, and the difficulty to extend these techniques to three-dimensional architectures. Molecular self-assembly provides an alternative route to create such materials which is not bound by the above limitations. We demonstrate how the DNA origami method can be used to produce plasmonic materials with a tailored optical response at visible wavelengths. Harnessing the assembly power of 3D DNA origami, we arranged metal nanoparticles with a spatial accuracy of 2 nm into nanoscale helices. The helical structures assemble in solution in a massively parallel fashion and with near quantitative yields. As a designed optical response, we generated giant circular dichroism and optical rotary dispersion in the visible range that originates from the collective plasmon-plasmon interactions within the nanohelices. We also show that the optical response can be tuned through the visible spectrum by changing the composition of the metal nanoparticles. The observed effects are independent of the direction of the incident light and can be switched by design between left- and right-handed orientation. Our work demonstrates the production of complex bulk materials from precisely designed nanoscopic assemblies and highlights the potential of DNA self-assembly for the fabrication of plasmonic nanostructures.Comment: 5 pages, 4 figure

The club of the future: Participatory clubbing experiences

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