Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering

Effective control of credit risk is a key link in the steady operation of commercial banks. This paper is mainly based on the customer information dataset of a foreign commercial bank in Kaggle, and we use LightGBM algorithm to build a classifier to classify customers, to help the bank judge the possibility of customer credit default. This paper mainly deals with characteristic engineering, such as missing value processing, coding, imbalanced samples, etc., which greatly improves the machine learning effect. The main innovation of this paper is to construct new feature attributes on the basis of the original dataset so that the accuracy of the classifier reaches 0.734, and the AUC reaches 0.772, which is more than many classifiers based on the same dataset. The model can provide some reference for commercial banks' credit granting, and also provide some feature processing ideas for other similar studies

Phase Compensation Enhancement of Photon Pair Entanglement Generated from Biexciton Decays in Quantum Dots

Exciton fine-structure splittings within quantum dots introduce phase differences between the two biexciton decay paths that greatly reduce the entanglement of photon pairs generated via biexciton recombination. We analyze this problem in the frequency domain and propose a practicable method to compensate the phase difference by inserting a spatial light modulator, which substantially improves the entanglement of the photon pairs without any loss.Comment: 4 pages, 3 figure

Stability and Mechanical Properties of w1-X Mox b4.2 (X=0.0-1.0) From First Principles

Heavy transition-metal tetraborides (e.g., tungsten tetraboride, molybdenum tetraboride, and molybdenum-doped tungsten tetraboride) exhibit superior mechanical properties, but solving their complex crystal structures has been a long-standing challenge. Recent experimental x-ray and neutron diffraction measurements combined with first-principles structural searches have identified a complex structure model for tungsten tetraboride that contains a boron trimer as an unusual structural unit with a stoichiometry of 1:4.2. In this paper, we expand the study to binary MoB4.2 and ternary W1-xMoxB4.2 (x=0.0-1.0) compounds to assess their thermodynamic stability and mechanical properties using a tailor-designed crystal structure search method in conjunction with first-principles energetic calculations. Our results reveal that an orthorhombic MoB4.2 structure in Cmcm symmetry matches well the experimental x-ray diffraction patterns. For the synthesized ternary Mo-doped tungsten tetraborides, a series of W1-xMoxB4.2 structures are theoretically designed using a random substitution approach by replacing the W to Mo atoms in the Cmcm binary crystal structure. This approach leads to the discovery of several W1-xMoxB4.2 structures that are energetically superior and stable against decomposition into binary WB4.2 and MoB4.2. The structural and mechanical properties of these low-energy W1-xMoxB4.2 structures largely follow the Vegard\u27s law. Under changing composition parameter x=0.0-1.0, the superior mechanical properties of W1-xMoxB4.2 stay in a narrow range. This unusual phenomenon stems from the strong covalent network with directional bonding configurations formed by boron atoms to resist elastic deformation. The findings offer insights into the fundamental structural and physical properties of ternary W1-xMoxB4.2 in relation to the binary WB4.2/MoB4.2 compounds, which open a promising avenue for further rational optimization of the functional performance of transition-metal borides that can be synthesized under favorable experimental conditions for wide applications

(Sr3La2O5)(Zn1-xMnx)2As2: A Bulk Form Diluted Magnetic Semiconductor isostructural to the "32522" Fe-based Superconductors

A new diluted magnetic semiconductor system, (Sr3La2O5)(Zn1-xMnx)2As2, has been synthesized and characterized. 10% Mn substitution for Zn in bulk form (Sr3La2O5)Zn2As2 results in a ferromagnetic ordering below Curie temperature, TC ~ 40 K. (Sr3La2O5)(Zn1-xMnx)2As2 has a layered crystal structure identical to that of 32522-type Fe based superconductors, and represents the fifth DMS family that has a direct counterpart among the FeAs high temperature superconductor families.Comment: Accepted for publication in EP

DPP-4 Inhibitors as Potential Candidates for Antihypertensive Therapy: Improving Vascular Inflammation and Assisting the Action of Traditional Antihypertensive Drugs

Dipeptidyl peptidase-4 (DPP-4) is an important protease that is widely expressed on the surface of human cells and plays a key role in immune-regulation, inflammation, oxidative stress, cell adhesion, and apoptosis by targeting different substrates. DPP-4 inhibitors (DPP-4i) are commonly used as hypoglycemic agents. However, in addition to their hypoglycemic effect, DPP-4i have also shown potent activities in the cardiovascular system, particularly in the regulation of blood pressure (BP). Previous studies have shown that the regulatory actions of DPP-4i in controlling BP are complex and that the mechanisms involved include the functional activities of the nerves, kidneys, hormones, blood vessels, and insulin. Recent work has also shown that inflammation is closely associated with the elevation of BP, and that the inhibition of DPP-4 can reduce BP by regulating the function of the immune system, by reducing inflammatory reactions and by improving oxidative stress. In this review, we describe the potential anti-hypertensive effects of DPP-4i and discuss potential new anti-hypertensive therapies. Our analysis indicated that DPP-4i treatment has a mild anti-hypertensive effect as a monotherapy and causes a significant reduction in BP when used in combined treatments. However, the combination of DPP-4i with high-dose angiotensin converting enzyme inhibitors (ACEI) can lead to increased BP. We suggest that DPP-4i improves vascular endothelial function in hypertensive patients by suppressing inflammatory responses and by alleviating oxidative stress. In addition, DPP-4i can also regulate BP by activating the sympathetic nervous system, interfering with the renin angiotensin aldosterone system (RAAS), regulating Na/H2O metabolism, and attenuating insulin resistance (IR)

Joint Generator-Ranker Learning for Natural Language Generation

Generate-then-rank is a widely used mechanism for text generation, where a generator produces multiple text candidates and a ranker chooses the best one among the text candidates. However, existing methods usually train the generator and the ranker individually, neglecting the mutual feedback that could further enhance the generation quality. To tackle this limitation, we propose JGR, a novel joint training algorithm that integrates the generator and the ranker in a single framework. JGR optimizes the generator with a hybrid objective that combines data likelihood and ranker reward, and trains the ranker with a contrastive loss that compares the generator outputs. By iteratively updating the generator and the ranker, JGR can effectively harmonize their learning and enhance their quality jointly. We evaluate JGR on various text generation tasks and demonstrate that it surpasses existing methods on four public datasets across three common generation scenarios. Our code and models are publicly available at https://github.com/microsoft/ProphetNet/tree/master/JGR