Learning Latent Representations of Bank Customers With The Variational Autoencoder

Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we adopt the Variational Autoencoder (VAE), which has the ability to learn latent representations that contain useful information. We show that it is possible to steer the latent representations in the latent space of the VAE using the Weight of Evidence and forming a specific grouping of the data that reflects the customers' creditworthiness. Our proposed method learns a latent representation of the data, which shows a well-defied clustering structure capturing the customers' creditworthiness. These clusters are well suited for the aforementioned banks' activities. Further, our methodology generalizes to new customers, captures high-dimensional and complex financial data, and scales to large data sets.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0253

Deep Generative Models for Reject Inference in Credit Scoring

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring

Dye lasing in optically manipulated liquid aerosols

We report lasing in airborne, rhodamine B-doped glycerol-water droplets with diameters ranging between 7.7 and 11.0 mu m, which were localized using optical tweezers. While being trapped near the focal point of an infrared laser, the droplets were pumped with a Q-switched green laser. Our experiments revealed nonlinear dependence of the intensity of the droplet whispering gallery modes (WGMs) on the pump laser fluence, indicating dye lasing. The average wavelength of the lasing WGMs could be tuned between 600 and 630 nm by changing the droplet size. These results may lead to new ways of probing airborne particles, exploiting the high sensitivity of stimulated emission to small perturbations in the droplet laser cavity and the gain medium

Temporal variability of available P, microbial P and some phosphomonoesterase activities in a sewage sludge treated soil: The effect of soil water potential

Available P and enzyme activities strongly depend on the soil water potential. The objective of this study was to test the effects of water potential on soil available P, microbial biomass P(MBP) and somephosphomonoesterase activities. A semiarid soil classified as Calcic Haploxerept was treated with raw sewage sludge at a rate of 20 g kg-1. Four levels of irrigation (deionized water) were established for 90days of incubation. Constant water potentials used for soil incubation were: saturation (SA, 0 bar), field capacity (FC, -0.3 bar), and permanent wilting point (PWP, -15 bar) in three treatments. An irrigation treatment was also drying-rewetting cycle (DWC) between -0.3 to -15 bars. After 0, 20, 60 and 90 days of incubation, soils were sampled for analysis. The addition of sewage sludge decreased soil pH and increased soil EC, organic C, total N, organic P, available P, MBP contents and phytase, alkaline and acid phosphatases activities significantly. The effects of soil moisture, incubation time and their interaction on soil available P, MBP and phosphomonoesterase activities were significant at different levels. During 20 days of incubation, available P and phosphatase activities decreased, whereas microbial P and phytase activity increased significantly. Thereafter, only available P increased and phytase activities decreased continuously, but microbial P, alkaline and acid phosphatase activities fluctuated during incubation. Soils incubated in DWC and FC compared to soils incubated in SA and PWP had higher available P contents. Microbial P and phosphomonoesterase activities increased with increasing soil water potentials significantly. The highest (38.7 mg kg-1) and lowest (28.9 mg kg-1) microbial P was measured in soil incubated in SA and PWP respectively. Correlation coefficient between available and microbial P was negative and significant. The activities of alkaline phosphatase,acid phosphatase and phytase were higher and lower in soils incubated in SA and PWP, respectively

The effects of water potential on some active forms of phosphorus in a calcareous soil amended with sewage sludge

Immobilization and mobilization reactions of soil phosphorus depend on biological properties of  soil and these soil properties strongly depend on the soil water potential. The objective of this study was to test theeffects of water potential on some active forms of soil P. A semiarid soil classified as Calcic Haploxerept was treated with raw sewage sludge at a rate of 20 g kg-1. Water potentials established for soil incubation were: saturation (SA, 0 bar), field capacity (FC, -0.3 bar), and permanent wilting point (PWP, -15 bar). An irrigation treatment was dryingrewetting cycle (DWC) between -0.3 to -15 bars. After 0, 20, 60 and 90 days of incubation soils were sampled for analysis. The addition of sewage sludge increased soil total P, organic P, available P, microbial P, soluble and easily soluble P contents. The increase of soluble P was relatively higher. The effects of soil moisture, incubation time andtheir interaction on all active forms of soil P were significant. During 20 days of incubation, available P and soluble P decreased, whereas microbial P, easily soluble P and dicalcium phosphate increased significantly. After that, available P and easily soluble P increased continuously, but microbial P, soluble P and dicalcium phosphatefluctuated during incubation. Microbial P had negative and significant correlations with available P and easily soluble P. Soils incubated in DWC and FC compared to soils incubated in SA and PWP had significantly higher available P,soluble P and easily soluble P contents. However microbial P and dicalcium phosphate were significantly higher in soils incubated in higher water potential

Profil Perilaku Narsisme Remaja Serta Implikasinya Bagi Bimbingan dan Konseling

The behavior of narcissism on adolescent became one of some focuses by teachers of Guidance and Counseling to more comprehend the problem of students. The approach of Freudian traditional psychodynamic put narcissism as a failure of running progress passing through lower level in psychosexual development. The research is aimed to know general description of student narcissism in adolescence. The approach that was used in this research is quantitative approach where the research method is descriptive method. The result of research showed that generally narcissism level of students is categorized average, where it was obtained by data collection tool exactly using questionnaire narcissism behavior that were scattered randomly to students grade VIII SMP Negeri 29 Bandung year 2015/2016. Based on the research result, some student was found in high category, it means that should to hand over the case to the expert who can help more accurate. Meanwhile, some students who are in average and low category need some guidance service for preventing narcissism behavior that psychologically disrupt them

Influence of summer and winter climate variability on nitrogen wet deposition in Norway

Dominating wind patterns around Norway may change due to climate warming. This could affect transport of polluted air masses and precipitation. Here, we study relations between reactive nitrogen wet deposition and air mass transport during summer and winter expressed in the form of climate indices, at seven sites in Southern Norway for the period 1980–2005. Atmospheric nitrate concentrations decreased with 0 to 50% in the period, particularly at sites with little precipitation, and mostly during 1990–2005. For comparison, reported reductions in emissions of oxidised nitrogen in Europe in 1989–2003 were 23%. Climate indices explained up to 36% of the variation in winter nitrate deposition at the western and northern sites – and also explained 60% of the variation in winter precipitation (R=0.77). This suggests that the variation in nitrate wet deposition is closely related to variation in precipitation, and that the climate indices seem to also partly control the variation in atmospheric nitrate concentrations (R=−0.45 at coastal sites). At the coastal sites, local air temperature was highly correlated (R=0.84) with winter nitrate deposition, suggesting that warm, humid winter weather results in increased wet nitrate deposition. For ammonia the pattern was similar, but this compound is more influenced by local sources. Expected severe increase in precipitation in western and northern regions as a consequence of climate change suggest that nitrogen deposition in these areas will increase under global warming if emissions are held constant

An Integrated Circuit for Galvanostatic Electrodeposition of on-chip Electrochemical Sensors

This paper presents the design of an integrated circuit (IC) for (i) galvanostatic deposition of sensor layers on the on-chip pads, which serve as the sensor's base layer, and (ii) amperometric readout of electrochemical sensors. The system consists of three main circuit blocks: the electrochemical cell including a 4×4 electrode array, two Beta-multiplier based current generators and one pA-size current generator for galvanostatic electrodeposition, and a switch-capacitor based amperometric readout circuit for sensor current measurement. The circuits are designed and simulated in a 180-nm CMOS process. The three current reference circuits generate a stable current from 7.2 pA to 88 µA with low process, power supply voltage and temperature (PVT) sensitivity. The pA-size current generator has a temperature coefficient of 517.8 ppm/°C on average (across corners) in the range of 0 to 60°C. The line regulation is 4.4 %/V over a supply voltage range of 0.8-3 V. The feasibility of galvanostatic deposition on on-chip pads is validated by applying a fixed current of 300 nA to electrochemically deposit a gold layer on top of electrodes with nickel/zinc as the adhesive layer for gold. Successful deposition of gold was confirmed using optical microscope images of the on-chip electrodes

Two-divisibility of the coefficients of certain weakly holomorphic modular forms

We study a canonical basis for spaces of weakly holomorphic modular forms of weights 12, 16, 18, 20, 22, and 26 on the full modular group. We prove a relation between the Fourier coefficients of modular forms in this canonical basis and a generalized Ramanujan tau-function, and use this to prove that these Fourier coefficients are often highly divisible by 2.Comment: Corrected typos. To appear in the Ramanujan Journa

Discriminative Multimodal Learning via Conditional Priors in Generative Models

Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all modalities and class labels are available for model training, but where some modalities and labels required for downstream tasks are missing. We show, in this scenario, that the variational lower bound limits mutual information between joint representations and missing modalities. We, to counteract these problems, introduce a novel conditional multi-modal discriminative model that uses an informative prior distribution and optimizes a likelihood-free objective function that maximizes mutual information between joint representations and missing modalities. Extensive experimentation demonstrates the benefits of our proposed model, empirical results show that our model achieves state-of-the-art results in representative problems such as downstream classification, acoustic inversion, and image and annotation generation