Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration. It is responsible for the unreliability of practical machine learning systems. For example, in online advertising, an ad can receive a click-through rate prediction of 0.1 over some population of users where its actual click rate is 0.15. In such cases, the probabilistic predictions have to be fixed before the system can be deployed. In this paper, we first introduce a new evaluation metric named field-level calibration error that measures the bias in predictions over the sensitive input field that the decision-maker concerns. We show that existing post-hoc calibration methods have limited improvements in the new field-level metric and other non-calibration metrics such as the AUC score. To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set. We present extensive experiments on five large-scale datasets. The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log-likelihood, Brier score and AUC, as well as the proposed field-level calibration error.Comment: WWW 202

Interfacial Properties of Bilayer and Trilayer Graphene on Metal Substrates

One popular approach to prepare graphene is to grow them on transition metal substrates via chemical vapor deposition. By using the density functional theory with dispersion correction, we systematically investigate for the first time the interfacial properties of bilayer (BLG) and trilayer graphene (TLG) on metal substrates. Three categories of interfacial structures are revealed. The adsorption of B(T)LG on Al, Ag, Cu, Au, and Pt substrates is a weak physisorption, but a band gap can be opened. The adsorption of B(T)LG on Ti, Ni, and Co substrates is a strong chemisorption, and a stacking-insensitive band gap is opened for the two uncontacted layers of TLG. The adsorption of B(T)LG on Pd substrate is a weaker chemisorption, with a band gap opened for the uncontacted layers. This fundamental study also helps for B(T)LG device study due to inevitable graphene/metal contact.Comment: 1 table, 8 figure

Does the Dirac Cone Exist in Silicene on Metal Substrates?

Absence of the Dirac cone due to a strong band hybridization is revealed to be a common feature for epitaxial silicene on metal substrates according to our first-principles calculations for silicene on Ir, Cu, Mg, Au, Pt, Al, and Ag substrates. The destroyed Dirac cone of silicene, however, can be effectively restored with linear or parabolic dispersion by intercalating alkali metal atoms between silicene and the metal substrates, offering an opportunity to study the intriguing properties of silicene without further transfer of silicene from the metal substrates

Egg white-mediated green synthesis of silver nanoparticles with excellent biocompatibility and enhanced radiation effects on cancer cells

A simple, cost-effective, and environmentally friendly approach to the aqueous-phase synthesis of silver (Ag) nanoparticles was demonstrated using silver nitrate (AgNO3) and freshly extracted egg white. The bio-conjugates were characterized by UV-visible spectroscopy, transmission electron microscopy, Fourier transform infrared spectrometry, and dynamic light scattering. These results indicated that biomolecule-coated Ag nanoparticles are predominantly spherical in shape with an average size of 20 nm. The proteins of egg white, which have different functional groups, played important roles in reducing Ag+ and maintaining product attributes such as stability and dispersity. In vitro cytotoxicity assays showed that these Ag-protein bio-conjugates showed good biocompatibility with mouse fibroblast cell lines 3T3. Furthermore, X-ray irradiation tests on 231 tumor cells suggested that the biocompatible Ag-protein bio-conjugates enhanced the efficacy of irradiation, and thus may be promising candidates for use during cancer radiation therapy

PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer

We present PBFormer, an efficient yet powerful scene text detector that unifies the transformer with a novel text shape representation Polynomial Band (PB). The representation has four polynomial curves to fit a text's top, bottom, left, and right sides, which can capture a text with a complex shape by varying polynomial coefficients. PB has appealing features compared with conventional representations: 1) It can model different curvatures with a fixed number of parameters, while polygon-points-based methods need to utilize a different number of points. 2) It can distinguish adjacent or overlapping texts as they have apparent different curve coefficients, while segmentation-based or points-based methods suffer from adhesive spatial positions. PBFormer combines the PB with the transformer, which can directly generate smooth text contours sampled from predicted curves without interpolation. A parameter-free cross-scale pixel attention (CPA) module is employed to highlight the feature map of a suitable scale while suppressing the other feature maps. The simple operation can help detect small-scale texts and is compatible with the one-stage DETR framework, where no postprocessing exists for NMS. Furthermore, PBFormer is trained with a shape-contained loss, which not only enforces the piecewise alignment between the ground truth and the predicted curves but also makes curves' positions and shapes consistent with each other. Without bells and whistles about text pre-training, our method is superior to the previous state-of-the-art text detectors on the arbitrary-shaped text datasets.Comment: 9 pages, 8 figures, accepted by ACM MM 202